nmWTAI-Platform/Src/nmNum/nmCalculation/nmCalculationAutoFitGA.cpp

#include "nmCalculationAutoFitGA.h"
//#include "nmCalculationExeSolverTask.h" 
#include "nmCalculationDllPebiSolverTask.h"
#include "nmDataAnalyzeManager.h"
#include "nmDataWellBase.h"
#include "nmDataVerticalWell.h"
#include "nmDataVerticalFracturedWell.h"
#include "nmDataHorizontalFracturedWell.h"
#include "nmDataReservoir.h"
#include "nmDataAutomaticFitting.h"

#include <QApplication>
#include <QDebug>
#include <QTime>
#include <QDir>
#include <QtCore/qmath.h>
#include <cmath>
#include <algorithm>

#ifdef Q_OS_WIN
#include <windows.h>
#include <float.h>
#define DEBUG_OUT(msg) OutputDebugStringA(QString("[AutoFit] %1\n").arg(msg).toLocal8Bit().data())
#endif

// 常量定义
const double nmCalculationAutoFitGA::MIN_FITNESS_IMPROVEMENT = 1e-8;
const double nmCalculationAutoFitGA::MUTATION_STRENGTH = 0.1;
const int nmCalculationAutoFitGA::CONVERGENCE_CHECK_INTERVAL = 10;
const int nmCalculationAutoFitGA::MAX_STAGNATION_GENERATIONS = 20;

// 无穷大和NaN检查
static inline bool isFiniteNumber(double value)
{
#ifdef Q_OS_WIN
	return _finite(value) != 0 && !_isnan(value);
#else
	return std::isfinite(value);
#endif
}

// sleep函数
static inline void msleep(int ms)
{
#ifdef Q_OS_WIN
	Sleep(ms);
#endif
}

// 构造函数
nmCalculationAutoFitGA::nmCalculationAutoFitGA(QObject* parent)
	: QObject(parent)
	, m_isRunning(false)
	, m_shouldStop(false)
	, m_isPaused(false)
	, m_currentGeneration(0)
	, m_bestFitness(1e10)
	, m_worstFitness(-1e10)
	, m_averageFitness(1e10)
	, m_previousBestFitness(1e10)
	, m_populationSize(40)
	, m_maxGenerations(100)
	, m_targetError(0.001)
	, m_crossoverRate(0.8)
	, m_mutationRate(0.1)
	, m_elitismRate(0.1)
	, m_tournamentSize(3)
	, m_useUniformCrossover(true)
	, m_totalEvaluations(0)
	, m_successfulEvaluations(0)
	, m_evaluationInProgress(0)
	, m_consecutiveFailures(0)
	, m_progressTimer(0)
	, m_userInitialFitness(1e10)
	, m_consecutiveFailedGenerations(0)
	, m_maxConsecutiveFailures(3)
	, m_hasValidUserSolution(false)
	, m_improvementThreshold(0.05)
	, m_diversityThreshold(0.05)
	, m_convergenceVarianceThreshold(1e-8)
	, m_trueConvergenceWindow(15)
	, m_localOptimumWindow(8)
	, m_nearTargetFactor(2.0)
	, m_farTargetFactor(10.0)
	, m_targetWellName("")
{
	DEBUG_OUT(QString("GA Constructor: this=0x%1").arg((quintptr)this, 0, 16));

	// 初始化随机数种子
	qsrand(QTime::currentTime().msec());

	// 初始化临时目录用于DLL求解器
	initializeTemporaryDirectory();

	// 初始化最优个体
	m_bestIndividual.fitness = 1e10;
	m_bestIndividual.isEvaluated = false;

	// 创建进度更新定时器
	m_progressTimer = new QTimer(this);
	connect(m_progressTimer, SIGNAL(timeout()), this, SLOT(updateProgress()));

	DEBUG_OUT("AutoFit GA calculator initialized (data-driven mode)");
	DEBUG_OUT("GA Constructor completed");
}

// 析构函数
nmCalculationAutoFitGA::~nmCalculationAutoFitGA()
{
	DEBUG_OUT(QString("GA Destructor: this=0x%1").arg((quintptr)this, 0, 16));

	// 首先停止算法
	if(m_isRunning) {
		m_shouldStop = true;  // 立即设置停止标志

		// 等待当前操作完成，增加超时时间
		int waitCount = 0;
		while(m_isRunning && waitCount < 100) {
			QApplication::processEvents(QEventLoop::ExcludeUserInputEvents, 50);
			msleep(50);
			waitCount++;
		}

		// 如果仍在运行则强制停止
		if(m_isRunning) {
			DEBUG_OUT("Force stopping GA - timeout reached");
			m_isRunning = false;
		}
	}

	// 清理临时目录
	cleanupTemporaryDirectory();

	// 断开所有信号连接，防止回调已销毁的对象
	disconnect(this, nullptr, nullptr, nullptr);

	DEBUG_OUT("GA Destructor completed");
}

// ==================== 目录处理方法 ====================

void nmCalculationAutoFitGA::initializeTemporaryDirectory()
{
	QString timestamp = QDateTime::currentDateTime().toString("yyyyMMdd_hhmmss_zzz");
	QString processId = QString::number(QCoreApplication::applicationPid());

	m_tempDirectory = QApplication::applicationDirPath() + 
		"/autofit_temp_" + processId + "_" + timestamp;

	// 确保目录不存在
	int counter = 0;
	QString originalPath = m_tempDirectory;

	while(QDir(m_tempDirectory).exists() && counter < 100) {
		m_tempDirectory = originalPath + "_" + QString::number(counter);
		counter++;
	}

	if(QDir().mkpath(m_tempDirectory)) {
		DEBUG_OUT(QString("Initialized temp directory: %1").arg(m_tempDirectory));
	} else {
		DEBUG_OUT(QString("Warning: Failed to create temp directory: %1").arg(m_tempDirectory));
		m_tempDirectory = QApplication::applicationDirPath();
	}
}

void nmCalculationAutoFitGA::cleanupTemporaryDirectory()
{
	if(QDir(m_tempDirectory).exists()) {
		if(removeDirectoryRecursively(m_tempDirectory)) {
			DEBUG_OUT("Temp directory cleaned up successfully");
		} else {
			DEBUG_OUT("Warning: Failed to clean up temp directory completely");
		}
	}
}

bool nmCalculationAutoFitGA::removeDirectoryRecursively(const QString& path)
{
	QDir dir(path);

	if(!dir.exists()) {
		return true;
	}

	// 递归删除子目录和文件
	QFileInfoList entries = dir.entryInfoList(QDir::NoDotAndDotDot | QDir::AllEntries | QDir::Hidden);
	bool allRemoved = true;

	for(int i = 0; i < entries.size(); ++i) {
		const QFileInfo& entry = entries[i];

		if(entry.isDir()) {
			if(!removeDirectoryRecursively(entry.absoluteFilePath())) {
				allRemoved = false;
			}
		} else {
			QFile file(entry.absoluteFilePath());

			// 处理只读文件
			if(!file.permissions().testFlag(QFile::WriteUser)) {
				file.setPermissions(file.permissions() | QFile::WriteUser);
			}

			if(!file.remove()) {
				DEBUG_OUT(QString("Failed to remove file: %1").arg(entry.absoluteFilePath()));
				allRemoved = false;
			}
		}
	}

	// 删除目录本身
	if(allRemoved) {
		return dir.rmdir(path);
	}

	return false;
}

// ==================== 公共接口方法 ====================

void nmCalculationAutoFitGA::setTargetLogLogData(const QVector<QVector<double>>& targetData)
{
	m_targetLogLogData = targetData;
	DEBUG_OUT(QString("Target LogLog data set: %1 arrays").arg(targetData.size()));

	if(targetData.size() >= 3) {
		DEBUG_OUT(QString("LogLog data points: X=%1, Y1=%2, Y2=%3")
			.arg(targetData[0].size())
			.arg(targetData[1].size())
			.arg(targetData[2].size()));
	}
}

bool nmCalculationAutoFitGA::startAutoFitting()
{
	if(m_isRunning) {
		m_lastError = "GA fitting is already running";
		return false;
	}

	DEBUG_OUT("=== GA AUTO FITTING START ===");

	// 发送初始化日志
	emit logMessageGenerated(tr("=== GA Automatic Fitting Started ==="));
	emit logMessageGenerated(tr("Algorithm: Genetic Algorithm"));

	try {
		// 从数据管理器加载所有配置
		if(!loadAllConfigFromDataManager()) {
			emit logMessageGenerated(tr("ERROR: Failed to load configuration from data manager"));
			return false;
		}

		int enabledParams = getEnabledParameterCount();
		emit logMessageGenerated(tr("Enabled parameters count: %1").arg(enabledParams));

		if(enabledParams == 0) {
			m_lastError = "No parameters enabled for optimization";
			emit logMessageGenerated(tr("ERROR: No parameters enabled for optimization"));
			return false;
		}

		if(m_targetLogLogData.isEmpty() || m_targetLogLogData.size() < 3) {
			m_lastError = "Target LogLog data is empty or insufficient";
			emit logMessageGenerated(tr("ERROR: Target LogLog data is empty or insufficient"));
			return false;
		}

		// 检查数据一致性
		if(m_targetLogLogData[0].size() != m_targetLogLogData[1].size() ||
			m_targetLogLogData[0].size() != m_targetLogLogData[2].size()) {
				m_lastError = "Target LogLog data arrays have inconsistent sizes";
				emit logMessageGenerated(tr("ERROR: Target LogLog data arrays have inconsistent sizes"));
				return false;
		}

		emit logMessageGenerated(tr("Target data validation passed (%1 data points)").arg(m_targetLogLogData[0].size()));

		// 使用保存的初始值进行精英保护
		QVector<double> savedInitialValues = m_initialValues;

		// 重置状态
		resetOptimizer();
		m_isRunning = true;
		m_shouldStop = false;
		m_isPaused = false;
		m_currentGeneration = 0;
		m_consecutiveFailures = 0;
		m_consecutiveFailedGenerations = 0;

		// 精英保护：评估用户初始解
		if(!savedInitialValues.isEmpty()) {
			m_userInitialSolution = savedInitialValues;
			emit logMessageGenerated(tr("=== Evaluating Initial Solution (Elite Protection) ==="));

			// 输出初始参数值
			QString paramStr = tr("Initial parameters: ");
			for(int i = 0; i < m_userInitialSolution.size(); ++i) {
				paramStr += QString("[%1]=%2 ").arg(i).arg(m_userInitialSolution[i], 0, 'f', 6);
			}
			emit logMessageGenerated(paramStr);

			try {
				emit logMessageGenerated(tr("Starting initial solution evaluation..."));
				DEBUG_OUT(QString("Before evaluateFitness: m_userInitialSolution size = %1").arg(m_userInitialSolution.size()));

				if(m_userInitialSolution.isEmpty()) {
					emit logMessageGenerated(tr("ERROR: m_userInitialSolution is empty!"));
					return false;
				}

				for(int i = 0; i < m_userInitialSolution.size(); ++i) {
					emit logMessageGenerated(tr("Initial param[%1] = %2").arg(i).arg(m_userInitialSolution[i], 0, 'f', 6));
				}

				m_userInitialFitness = evaluateGenes(m_userInitialSolution);

				emit logMessageGenerated(tr("evaluateGenes returned: %1").arg(m_userInitialFitness, 0, 'e', 10));

				if(m_userInitialFitness < 1e9) {
					emit logMessageGenerated(tr("Taking SUCCESS branch (fitness < 1e9)"));
					m_hasValidUserSolution = true;
					m_bestFitness = m_userInitialFitness;
					m_bestIndividual.genes = m_userInitialSolution;
					m_bestIndividual.fitness = m_userInitialFitness;
					m_bestIndividual.isEvaluated = true;

					emit logMessageGenerated(tr("Initial solution evaluation successful"));
					emit logMessageGenerated(tr("Initial fitness (error): %1").arg(m_userInitialFitness, 0, 'e', 4));
					emit logMessageGenerated(tr("Elite protection activated - initial solution will be preserved if no significant improvement found"));
				} else {
					emit logMessageGenerated(tr("Taking FAILURE branch (fitness >= 1e9)"));
					m_hasValidUserSolution = false;
					emit logMessageGenerated(tr("Initial solution evaluation failed - starting with random initialization"));
				}
			} catch(...) {
				m_hasValidUserSolution = false;
				emit logMessageGenerated(tr("Exception during initial solution evaluation"));
			}

			// 恢复初始值供种群初始化使用
			m_initialValues = savedInitialValues;
		}

		// 初始化种群
		initializePopulation();
		emit logMessageGenerated(tr("Population initialized: %1 individuals, %2 dimensions").arg(m_populationSize).arg(getEnabledParameterCount()));

		// GA主循环
		emit logMessageGenerated(tr("=== Starting GA Main Loop ==="));

		for(m_currentGeneration = 0; m_currentGeneration < m_maxGenerations && !m_shouldStop; ++m_currentGeneration) {

			// 每次迭代都输出标题，或者只在重要迭代输出详细信息
			bool shouldOutputDetail = (m_currentGeneration % qMax(1, m_maxGenerations / 10) == 0) ||
				(m_currentGeneration < 5) ||
				(m_currentGeneration >= m_maxGenerations - 2);

			// 每次迭代都输出标题
			emit logMessageGenerated(tr("--- Generation %1/%2 ---").arg(m_currentGeneration + 1).arg(m_maxGenerations));

			// 只在特定迭代输出详细统计信息
			if(shouldOutputDetail) {
				emit logMessageGenerated(tr("Current best error: %1").arg(m_bestFitness, 0, 'e', 4));
				emit logMessageGenerated(tr("Total evaluations: %1 (successful: %2, failures: %3)")
					.arg(m_totalEvaluations).arg(m_successfulEvaluations).arg(m_totalEvaluations - m_successfulEvaluations));
			}

			// 检查暂停状态
			while(m_isPaused && !m_shouldStop) {
				QApplication::processEvents();
				//msleep(100);
			}

			if(m_shouldStop) {
				emit logMessageGenerated(tr("Optimization stopped by user request"));
				break;
			}

			// 1. 评估种群
			evaluatePopulation();

			if(m_shouldStop) break;

			// 2. 更新统计信息
			updatePopulationStatistics();

			// 3. 发射进度信号
			emit progressUpdated(m_currentGeneration, m_bestFitness);

			if(shouldOutputDetail) {
				emit logMessageGenerated(tr("Generation %1 completed: best = %2, avg = %3, worst = %4")
					.arg(m_currentGeneration + 1).arg(m_bestFitness, 0, 'e', 4)
					.arg(m_averageFitness, 0, 'e', 4).arg(m_worstFitness, 0, 'e', 4));
			}

			// 记录收敛历史
			m_convergenceHistory.append(m_bestFitness);

			// 更新收敛指标
			updateConvergenceMetrics();

			// 智能收敛判断
			StopReasonGA stopReason = analyzeOptimizationStatus();

			if(stopReason == GA_TARGET_ACHIEVED) {
				emit logMessageGenerated(tr("=== TARGET ACHIEVED ==="));
				emit logMessageGenerated(tr("Target error achieved! Current error: %1 < Target: %2")
					.arg(m_bestFitness, 0, 'e', 4).arg(m_targetError, 0, 'e', 4));
				emit logMessageGenerated(tr("Optimization completed successfully after %1 generations")
					.arg(m_currentGeneration + 1));
				break;
			}
			else if(stopReason == GA_TRUE_CONVERGENCE) {
				emit logMessageGenerated(tr("=== TRUE CONVERGENCE DETECTED ==="));
				emit logMessageGenerated(tr("Algorithm has converged to a stable solution"));
				emit logMessageGenerated(tr("Final error: %1 after %2 generations")
					.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1));
				emit logMessageGenerated(tr("Solution quality: %1 (1.0 = target achieved)")
					.arg(m_targetError / qMax(1e-10, m_bestFitness), 0, 'f', 3));
				break;
			}
			else if(stopReason == GA_LOCAL_OPTIMUM) {
				emit logMessageGenerated(tr("=== LOCAL OPTIMUM DETECTED ==="));
				emit logMessageGenerated(tr("Algorithm appears to be trapped in local optimum"));
				emit logMessageGenerated(tr("Current error: %1 after %2 generations")
					.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1));
				emit logMessageGenerated(tr("Suggestion: Try restarting with different parameters or larger search space"));
				break;
			}
			else if(stopReason == GA_CONSECUTIVE_FAILURES) {
				emit logMessageGenerated(tr("=== CONSECUTIVE FAILURES ==="));
				emit logMessageGenerated(tr("Too many consecutive failed generations (%1/%2)")
					.arg(m_consecutiveFailedGenerations).arg(m_maxConsecutiveFailures));
				break;
			}
			else if(stopReason == GA_CONTINUE_OPTIMIZATION) {
				// 继续优化，每10次迭代输出一次状态
				if(m_currentGeneration > 0 && m_currentGeneration % 10 == 0) {
					double diversity = calculatePopulationDiversity();

					emit logMessageGenerated(tr("  Optimization status: diversity=%1")
						.arg(diversity, 0, 'f', 4));
				}
			}

			// 4. 创建新一代种群
			if(m_currentGeneration < m_maxGenerations - 1)
			{
				QVector<GAIndividual> newPopulation;
				newPopulation.reserve(m_populationSize);

				// 精英保留
				applyElitism(newPopulation);

				// 生成新个体直到填满种群
				while(newPopulation.size() < m_populationSize && !m_shouldStop) {
					// 选择父代
					int parent1Index = tournamentSelection();
					int parent2Index = tournamentSelection();

					// 确保父代不同
					while(parent1Index == parent2Index && m_population.size() > 1) {
						parent2Index = tournamentSelection();
					}

					GAIndividual offspring1, offspring2;

					// 交叉
					if(random01() < m_crossoverRate) {
						crossover(m_population[parent1Index], m_population[parent2Index],
							offspring1, offspring2);
					} else {
						offspring1 = m_population[parent1Index];
						offspring2 = m_population[parent2Index];
					}

					// 变异
					if(random01() < m_mutationRate) {
						mutate(offspring1);
					}
					if(random01() < m_mutationRate) {
						mutate(offspring2);
					}

					// 边界约束
					clampToLimits(offspring1.genes);
					clampToLimits(offspring2.genes);

					// 添加到新种群
					if(newPopulation.size() < m_populationSize) {
						newPopulation.append(offspring1);
					}
					if(newPopulation.size() < m_populationSize) {
						newPopulation.append(offspring2);
					}
				}

				// 替换种群
				m_population = newPopulation;

				// 自适应参数调整
				adaptiveParameterUpdate(m_currentGeneration);
			}

			// 输出迭代结束标记
			if(shouldOutputDetail) {
				emit logMessageGenerated(tr("Generation %1 completed - Current best: %2")
					.arg(m_currentGeneration + 1).arg(m_bestFitness, 0, 'e', 4));
			}

			// 强制处理事件，保持界面响应
			QApplication::processEvents();

			// 在世代间稍作停顿，减少系统负载
			if(m_currentGeneration % 3 == 2) {
				//msleep(200);
			}
		}

		// 最终结果验证和保护
		validateAndProtectFinalResult();

	} catch(const std::exception& e) {
		m_lastError = QString("Critical exception in GA main loop: %1").arg(e.what());
		emit logMessageGenerated(tr("CRITICAL ERROR: %1").arg(e.what()));
		cleanupTemporaryDirectory();
		m_isRunning = false;
		if(m_progressTimer) m_progressTimer->stop();
		emit fittingFinished(false, m_lastError);
		return false;
	} catch(...) {
		m_lastError = "Unknown critical exception in GA main loop";
		emit logMessageGenerated(tr("CRITICAL ERROR: Unknown exception in GA main loop"));
		cleanupTemporaryDirectory();
		m_isRunning = false;
		if(m_progressTimer) m_progressTimer->stop();
		emit fittingFinished(false, m_lastError);
		return false;
	}

	m_isRunning = false;
	if(m_progressTimer) m_progressTimer->stop();

	// 应用最终参数
	if(!m_bestIndividual.genes.isEmpty()) {
		try {
			emit logMessageGenerated(tr("Applying optimized parameters to model..."));
			applyParametersToDataManager(m_bestIndividual.genes);
			saveOptimizationResult();

			// 输出最终优化结果
			emit logMessageGenerated(tr("=== Optimization Results ==="));
			emit logMessageGenerated(tr("Final error: %1").arg(m_bestFitness, 0, 'e', 4));
			emit logMessageGenerated(tr("Total generations: %1").arg(m_currentGeneration + 1));
			emit logMessageGenerated(tr("Total evaluations: %1 (successful: %2)")
				.arg(m_totalEvaluations).arg(m_successfulEvaluations));

			// 输出最优参数值
			QString finalParams = tr("Optimized parameters: ");
			for(int i = 0; i < m_bestIndividual.genes.size(); ++i) {
				finalParams += QString("[%1]=%2 ").arg(i).arg(m_bestIndividual.genes[i], 0, 'f', 6);
			}
			emit logMessageGenerated(finalParams);

			emit logMessageGenerated(tr("Parameters applied successfully to data manager"));
		} catch(const std::exception& e) {
			emit logMessageGenerated(tr("ERROR: Failed to apply final parameters: %1").arg(e.what()));
			m_lastError = QString("Failed to apply final parameters: %1").arg(e.what());
		} catch(...) {
			emit logMessageGenerated(tr("ERROR: Unknown error applying final parameters"));
			m_lastError = "Failed to apply final parameters due to unknown error";
		}
	}

	// 判断系统确定最终结果
	bool success;
	QString message;
	StopReasonGA finalReason = analyzeOptimizationStatus();

	if(finalReason == GA_TARGET_ACHIEVED) {
		success = true;
		//message = QString("GA optimization completed successfully. Target achieved. Best error: %1, Generations: %2")
		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
		emit logMessageGenerated(tr("=== GA OPTIMIZATION SUCCESSFUL ==="));
	}
	else if(finalReason == GA_TRUE_CONVERGENCE) {
		success = true;
		//message = QString("GA optimization converged to stable solution. Best error: %1, Generations: %2")
		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
		emit logMessageGenerated(tr("=== GA OPTIMIZATION CONVERGED ==="));
	}
	else if(finalReason == GA_LOCAL_OPTIMUM) {
		success = true;
		//message = QString("GA optimization trapped in local optimum. Best error: %1, Generations: %2")
		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
		emit logMessageGenerated(tr("=== GA OPTIMIZATION - LOCAL OPTIMUM ==="));
	}
	else if(finalReason == GA_MAX_ITERATIONS) {
		success = true;
		//message = QString("GA optimization completed. Max generations reached. Best error: %1, Generations: %2")
		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
		emit logMessageGenerated(tr("=== GA OPTIMIZATION - MAX GENERATIONS ==="));
	}
	else if(finalReason == GA_USER_STOPPED) {
		success = true;
		//message = QString("GA optimization stopped by user. Best error: %1, Generations: %2")
		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
		emit logMessageGenerated(tr("=== GA OPTIMIZATION STOPPED BY USER ==="));
	}
	else if(finalReason == GA_CONSECUTIVE_FAILURES) {
		success = false;
		//message = QString("GA optimization failed due to consecutive failures. Best error: %1, Generations: %2")
		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
		emit logMessageGenerated(tr("=== GA OPTIMIZATION FAILED ==="));
	}
	else {
		success = false;
		//message = QString("GA optimization ended unexpectedly. Best error: %1, Generations: %2")
		//	.arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1);
		emit logMessageGenerated(tr("=== GA OPTIMIZATION - UNKNOWN END ==="));
	}

	emit logMessageGenerated(tr("Result: %1").arg(success ? "SUCCESS" : "FAILED"));

	emit fittingFinished(success, message);
	cleanupTemporaryDirectory();
	return success;
}

void nmCalculationAutoFitGA::stopFitting()
{
	if(m_isRunning) {
		DEBUG_OUT("Stop request received, setting stop flag...");

		// 添加停止日志
		emit logMessageGenerated(tr("=== User Stop Request Received ==="));
		emit logMessageGenerated(tr("Gracefully stopping GA optimization..."));

		m_shouldStop = true;

		if(m_progressTimer) {
			m_progressTimer->stop();
		}

		// 等待当前评估完成，缩短超时时间
		int waitCount = 0;
		while(m_evaluationInProgress > 0 && waitCount < 30) {  // 减少等待时间
			QApplication::processEvents(QEventLoop::ExcludeUserInputEvents, 50);
			msleep(50);
			waitCount++;
		}

		// 超时时强制重置
		if(m_evaluationInProgress > 0) {
			DEBUG_OUT("Force resetting evaluation counter");
			emit logMessageGenerated(tr("Force stopping current evaluation..."));
			m_evaluationInProgress = 0;
		}

		// 确保运行标志被清除
		m_isRunning = false;

		emit logMessageGenerated(tr("GA optimization stop request processed"));
		DEBUG_OUT("Stop request processed");
	} else {
		DEBUG_OUT("Stop request received but GA is not running");
		emit logMessageGenerated(tr("Stop request received but optimization is not running"));
	}
	cleanupTemporaryDirectory();
}

bool nmCalculationAutoFitGA::isRunning() const
{
	return m_isRunning;
}

int nmCalculationAutoFitGA::getCurrentGeneration() const
{
	return m_currentGeneration;
}

QVector<double> nmCalculationAutoFitGA::getBestSolution() const
{
	return m_bestIndividual.genes;
}

double nmCalculationAutoFitGA::getBestFitness() const
{
	return m_bestFitness;
}

QString nmCalculationAutoFitGA::getLastError() const
{
	return m_lastError;
}

void nmCalculationAutoFitGA::resetOptimizer()
{
	m_population.clear();
	m_eliteIndividuals.clear();
	m_bestIndividual = GAIndividual();
	m_bestFitness = 1e10;
	m_worstFitness = -1e10;
	m_averageFitness = 1e10;
	m_previousBestFitness = 1e10;
	m_currentGeneration = 0;
	m_totalEvaluations = 0;
	m_successfulEvaluations = 0;
	m_convergenceHistory.clear();
	m_lastError.clear();
	m_initialValues.clear();
	m_userInitialSolution.clear();
	m_userInitialFitness = 1e10;
	m_hasValidUserSolution = false;
	m_diversityHistory.clear();

	DEBUG_OUT("GA optimizer reset");
}

void nmCalculationAutoFitGA::setGATargetWellName(const QString& wellName)
{
	m_targetWellName = wellName;
}

void nmCalculationAutoFitGA::updateProgress()
{
	// 这个槽函数在定时器触发时被调用，可以用来更新界面或执行周期性任务
	if(m_isRunning) {
		QApplication::processEvents();
	}
}

// ==================== 数据加载方法 ====================
bool nmCalculationAutoFitGA::loadAllConfigFromDataManager()
{
	try {
		loadOptimizationConfig();
		loadParameterBounds();
		extractUserInitialValues();
		return true;
	} catch(...) {
		m_lastError = "Failed to load configuration from data manager";
		return false;
	}
}

void nmCalculationAutoFitGA::loadOptimizationConfig()
{
	nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
	nmDataAutomaticFitting fittingData = dataManager->getAutomaticFittingDataCopy();

	// 加载基本配置
	m_maxGenerations = fittingData.getIterationCount().getValue().toInt();
	m_targetError = fittingData.getErrorTolerance().getValue().toDouble();

	// 设置GA默认参数
	m_populationSize = 40;
	m_crossoverRate = 0.8;
	m_mutationRate = 0.1;
	m_elitismRate = 0.15;
	m_tournamentSize = 3;
	m_useUniformCrossover = true;

	DEBUG_OUT(QString("Loaded GA optimization config: generations=%1, error=%2, population=%3")
		.arg(m_maxGenerations).arg(m_targetError).arg(m_populationSize));
}

void nmCalculationAutoFitGA::loadParameterBounds()
{
	nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
	nmDataAutomaticFitting fittingData = dataManager->getAutomaticFittingDataCopy();

	// 获取参数选择状态
	m_parameterSelected.resize(11);
	m_parameterSelected[0] = fittingData.getPermeabilitySelected();
	m_parameterSelected[1] = fittingData.getSkinSelected();
	m_parameterSelected[2] = fittingData.getWellboreStorageSelected();
	m_parameterSelected[3] = fittingData.getPorositySelected();
	m_parameterSelected[4] = fittingData.getInitialPressureSelected();
	m_parameterSelected[5] = fittingData.getThicknessSelected();
	m_parameterSelected[6] = fittingData.getCtSelected();
	m_parameterSelected[7] = fittingData.getCfSelected();
	m_parameterSelected[8] = fittingData.getSoiSelected();
	m_parameterSelected[9] = fittingData.getSwiSelected();
	m_parameterSelected[10] = fittingData.getSgiSelected();

	// 获取参数边界
	m_parameterLower.resize(11);
	m_parameterUpper.resize(11);

	m_parameterLower[0] = fittingData.getPermeabilityMin().getValue().toDouble();
	m_parameterUpper[0] = fittingData.getPermeabilityMax().getValue().toDouble();

	m_parameterLower[1] = fittingData.getSkinMin().getValue().toDouble();
	m_parameterUpper[1] = fittingData.getSkinMax().getValue().toDouble();

	m_parameterLower[2] = fittingData.getWellboreStorageMin().getValue().toDouble();
	m_parameterUpper[2] = fittingData.getWellboreStorageMax().getValue().toDouble();

	m_parameterLower[3] = fittingData.getPorosityMin().getValue().toDouble();
	m_parameterUpper[3] = fittingData.getPorosityMax().getValue().toDouble();

	m_parameterLower[4] = fittingData.getInitialPressureMin().getValue().toDouble();
	m_parameterUpper[4] = fittingData.getInitialPressureMax().getValue().toDouble();

	m_parameterLower[5] = fittingData.getThicknessMin().getValue().toDouble();
	m_parameterUpper[5] = fittingData.getThicknessMax().getValue().toDouble();

	m_parameterLower[6] = fittingData.getCtMin().getValue().toDouble();
	m_parameterUpper[6] = fittingData.getCtMax().getValue().toDouble();

	m_parameterLower[7] = fittingData.getCfMin().getValue().toDouble();
	m_parameterUpper[7] = fittingData.getCfMax().getValue().toDouble();

	m_parameterLower[8] = fittingData.getSoiMin().getValue().toDouble();
	m_parameterUpper[8] = fittingData.getSoiMax().getValue().toDouble();

	m_parameterLower[9] = fittingData.getSwiMin().getValue().toDouble();
	m_parameterUpper[9] = fittingData.getSwiMax().getValue().toDouble();

	m_parameterLower[10] = fittingData.getSgiMin().getValue().toDouble();
	m_parameterUpper[10] = fittingData.getSgiMax().getValue().toDouble();

	// 更新启用参数索引
	m_enabledParamIndices.clear();
	for(int i = 0; i < m_parameterSelected.size(); ++i) {
		if(m_parameterSelected[i]) {
			m_enabledParamIndices.append(i);
		}
	}
	DEBUG_OUT(QString("Loaded parameter bounds: %1 enabled parameters")
		.arg(m_enabledParamIndices.size()));
}

void nmCalculationAutoFitGA::extractUserInitialValues()
{
	nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
	nmDataReservoir reservoirData = dataManager->getReservoirDataCopy();
	//QVector<nmDataWellBase*> wells = dataManager->getWellDataList();
	nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName);

	m_initialValues.clear();

	// 按照启用参数的顺序提取初始值
	for(int i = 0; i < m_enabledParamIndices.size(); ++i) {
		int paramIndex = m_enabledParamIndices[i];
		double initialValue = 0.0;

		switch(paramIndex) {
		case 0: // 渗透率
			initialValue = reservoirData.getPermeability().getValue().toDouble();
			break;

		case 1: // 表皮系数
			if(pTargetWell) {
				initialValue = pTargetWell->getPerforation(0)->getSkin().getValue().toDouble();
			}

			break;

		case 2: // 井筒储集系数
			if(pTargetWell) {
				initialValue = pTargetWell->getWellboreStorage().getValue().toDouble();
			}

			break;

		case 3: // 孔隙度
			initialValue = reservoirData.getPorosity().getValue().toDouble();
			break;

		case 4: // 初始压力
			initialValue = reservoirData.getInitialPressure().getValue().toDouble();
			break;

		case 5: // 储层厚度
			initialValue = reservoirData.getThickness().getValue().toDouble();
			break;

		case 6: // 综合压缩系数
			initialValue = reservoirData.getCt().getValue().toDouble();
			break;

		case 7: // 岩石压缩系数
			initialValue = reservoirData.getCf().getValue().toDouble();
			break;

		case 8: // 初始含油饱和度
			initialValue = reservoirData.getSoi().getValue().toDouble();
			break;

		case 9: // 初始含水饱和度
			initialValue = reservoirData.getSwi().getValue().toDouble();
			break;

		case 10: // 初始含气饱和度  
			initialValue = reservoirData.getSgi().getValue().toDouble();
			break;
		}

		m_initialValues.append(initialValue);
	}

	DEBUG_OUT(QString("Extracted %1 user initial values").arg(m_initialValues.size()));

	for(int i = 0; i < m_initialValues.size(); ++i) {
		DEBUG_OUT(QString("  Initial[%1] = %2").arg(i).arg(m_initialValues[i], 0, 'e', 3));
	}

	// 验证初始值
	if(!validateInitialValues()) {
		DEBUG_OUT("Warning: Some initial values are outside parameter bounds");
	}
}

// ==================== 遗传算法核心方法 ====================

void nmCalculationAutoFitGA::initializePopulation()
{
	int dimensions = getEnabledParameterCount();
	if(dimensions == 0) return;

	m_population.clear();
	m_population.resize(m_populationSize);

	bool hasValidInitials = !m_initialValues.isEmpty() && m_initialValues.size() >= dimensions;
	int guidedCount = hasValidInitials ? qMax(2, m_populationSize / 2) : qMax(1, m_populationSize / 3);

	DEBUG_OUT(QString("Enhanced population initialization: %1 individuals, %2 guided, %3 random")
		.arg(m_populationSize).arg(guidedCount).arg(m_populationSize - guidedCount));

	for(int i = 0; i < m_populationSize; ++i) {
		GAIndividual& individual = m_population[i];
		individual.genes.resize(dimensions);
		individual.fitness = 1e10;
		individual.isEvaluated = false;

		// 基因初始化
        for(int j = 0; j < dimensions; ++j) {
			int paramIndex = m_enabledParamIndices[j];
			double range = m_parameterUpper[paramIndex] - m_parameterLower[paramIndex];

			if(i == 0 && hasValidInitials) {
				// 第一个个体：使用用户初始值
				individual.genes[j] = m_initialValues[j];
			} else if(i < guidedCount && hasValidInitials) {
				// 引导搜索策略
				double searchRadius;
				if(i <= guidedCount / 3) {
					searchRadius = range * 0.03;
				} else if(i <= guidedCount * 2 / 3) {
					searchRadius = range * 0.08;
				} else {
					searchRadius = range * 0.15;
				}
				double offset = (random01() - 0.5) * searchRadius;
				individual.genes[j] = m_initialValues[j] + offset;
			} else {
				// 随机初始化
				individual.genes[j] = m_parameterLower[paramIndex] + random01() * range;
			}
		}

		// 边界约束
		clampToLimits(individual.genes);
	}
}

double nmCalculationAutoFitGA::evaluateGenes(const QVector<double>& genes)
{
	const QString funcName = QString("evaluateGenes[Gen%1]").arg(m_currentGeneration);
	static int callCount = 0;
	callCount++;

	try {
		DEBUG_OUT(QString("%1: Call #%2 - Starting evaluation with %3 genes")
			.arg(funcName).arg(callCount).arg(genes.size()));

		// 打印参数值用于对比
		QString paramStr = "Parameters: ";

		for(int i = 0; i < genes.size(); ++i) {
			paramStr += QString("[%1]=%2 ").arg(i).arg(genes[i], 0, 'f', 6);
		}

		DEBUG_OUT(QString("%1: %2").arg(funcName).arg(paramStr));

		// 1. 参数有效性检查
		if(!validateParameters(genes)) {
			DEBUG_OUT(QString("%1: Call #%2 - Invalid parameters").arg(funcName).arg(callCount));
			return 1e10;
		}

		// 2. 检查数据管理器状态
		nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();

		if(!dataManager) {
			DEBUG_OUT(QString("%1: Call #%2 - DataManager is null").arg(funcName).arg(callCount));
			return 1e10;
		}

		// 3. 应用参数到数据管理器
		try {
			DEBUG_OUT(QString("%1: Call #%2 - Applying parameters to DataManager").arg(funcName).arg(callCount));
			applyParametersToDataManager(genes);
			DEBUG_OUT(QString("%1: Call #%2 - Parameters applied successfully").arg(funcName).arg(callCount));
		} catch(const std::exception& e) {
			DEBUG_OUT(QString("%1: Call #%2 - Failed to apply parameters: %3").arg(funcName).arg(callCount).arg(e.what()));
			return 1e10;
		} catch(...) {
			DEBUG_OUT(QString("%1: Call #%2 - Unknown error applying parameters").arg(funcName).arg(callCount));
			return 1e10;
		}

		// 4. 运行求解器
		QVector<QVector<double> > solverResult;
		const int maxRetries = 2;
		bool solverSuccess = false;

		for(int retry = 0; retry <= maxRetries; ++retry) {
			if(m_shouldStop) return 1e10;

			try {
				DEBUG_OUT(QString("%1: Call #%2 - Solver attempt %3/%4")
					.arg(funcName).arg(callCount).arg(retry + 1).arg(maxRetries + 1));

				// 在求解器调用前添加短暂延迟，确保状态稳定
				if(retry > 0) {
					DEBUG_OUT(QString("%1: Call #%2 - Retry delay before solver attempt")
						.arg(funcName).arg(callCount));
					msleep(1000); // 增加延迟时间
				}

				solverResult = runSolver();

				if(!solverResult.isEmpty() && validateSolverResult(solverResult)) {
					DEBUG_OUT(QString("%1: Call #%2 - Solver successful on attempt %3, result size: %4")
						.arg(funcName).arg(callCount).arg(retry + 1).arg(solverResult[0].size()));
					solverSuccess = true;
					break;
				} else {
					DEBUG_OUT(QString("%1: Call #%2 - Solver failed on attempt %3 - empty or invalid result")
						.arg(funcName).arg(callCount).arg(retry + 1));

					if(retry < maxRetries) {
						DEBUG_OUT(QString("%1: Call #%2 - Will retry solver").arg(funcName).arg(callCount));
					}
				}

			} catch(const std::exception& e) {
				DEBUG_OUT(QString("%1: Call #%2 - Solver exception on attempt %3: %4")
					.arg(funcName).arg(callCount).arg(retry + 1).arg(e.what()));
			} catch(...) {
				DEBUG_OUT(QString("%1: Call #%2 - Unknown solver exception on attempt %3")
					.arg(funcName).arg(callCount).arg(retry + 1));
			}
		}

		if(!solverSuccess) {
			DEBUG_OUT(QString("%1: Call #%2 - All solver attempts failed").arg(funcName).arg(callCount));
			return 1e10;
		}

		// 5. 获取双对数结果数据
		QVector<QVector<double> > resultLogLogData;

		try {

			nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName);

			if(pTargetWell) {
				resultLogLogData = pTargetWell->getResultLogLog();

				if(!validateLogLogData(resultLogLogData)) {
					DEBUG_OUT(QString("%1: Call #%2 - Invalid result LogLog data").arg(funcName).arg(callCount));
					return 1e10;
				}

				DEBUG_OUT(QString("%1: Call #%2 - LogLog result data obtained, size: %3")
					.arg(funcName).arg(callCount).arg(resultLogLogData[0].size()));
			} else {
				DEBUG_OUT(QString("%1: Call #%2 - No wells found after solver").arg(funcName).arg(callCount));
				return 1e10;
			}
		} catch(const std::exception& e) {
			DEBUG_OUT(QString("%1: Call #%2 - Error getting LogLog result: %3").arg(funcName).arg(callCount).arg(e.what()));
			return 1e10;
		} catch(...) {
			DEBUG_OUT(QString("%1: Call #%2 - Unknown error getting LogLog result").arg(funcName).arg(callCount));
			return 1e10;
		}

		// 6. 双对数曲线对齐和误差计算
		double error;

		try {
			error = calculateLogLogCurveError(m_targetLogLogData, resultLogLogData);

			if(!isFiniteNumber(error) || error < 0) {
				DEBUG_OUT(QString("%1: Call #%2 - Invalid error value: %3").arg(funcName).arg(callCount).arg(error));
				return 1e10;
			}

			DEBUG_OUT(QString("%1: Call #%2 - Evaluation successful, error = %3")
				.arg(funcName).arg(callCount).arg(error, 0, 'e', 6));

		} catch(const std::exception& e) {
			DEBUG_OUT(QString("%1: Call #%2 - LogLog error calculation failed: %3").arg(funcName).arg(callCount).arg(e.what()));
			return 1e10;
		} catch(...) {
			DEBUG_OUT(QString("%1: Call #%2 - Unknown error in LogLog error calculation").arg(funcName).arg(callCount));
			return 1e10;
		}

		return error;

	} catch(const std::exception& e) {
		DEBUG_OUT(QString("%1: Call #%2 - Top-level exception: %3").arg(funcName).arg(callCount).arg(e.what()));
		return 1e10;
	} catch(...) {
		DEBUG_OUT(QString("%1: Call #%2 - Unknown top-level exception").arg(funcName).arg(callCount));
		return 1e10;
	}
}

double nmCalculationAutoFitGA::evaluateIndividual(GAIndividual& individual)
{
	// 如果已经评估过，直接返回
	if(individual.isEvaluated) {
		return individual.fitness;
	}

	// 调用新的评估函数
	individual.fitness = evaluateGenes(individual.genes);
	individual.isEvaluated = true;

	return individual.fitness;
}

void nmCalculationAutoFitGA::evaluatePopulation()
{
	int successfulEvaluations = 0;
	int totalEvaluations = 0;
	int currentGenerationFailed = 0;

	for(int i = 0; i < m_population.size() && !m_shouldStop; ++i) {
		GAIndividual& individual = m_population[i];

		if(!individual.isEvaluated) {
			try {
				double previousFitness = individual.fitness;
				individual.fitness = evaluateGenes(individual.genes);
				individual.isEvaluated = true;
				totalEvaluations++;
				m_totalEvaluations++;

				if(individual.fitness < 1e9) {
					successfulEvaluations++;
					m_successfulEvaluations++;

					// 更新全局最优
					if(individual.fitness < m_bestFitness) {
						m_bestFitness = individual.fitness;
						m_bestIndividual = individual;

						emit logMessageGenerated(tr("  Individual %1 improved: %2 -> %3")
							.arg(i + 1).arg(previousFitness, 0, 'e', 3).arg(individual.fitness, 0, 'e', 3));
					}
				} else {
					currentGenerationFailed++;
					emit logMessageGenerated(tr("  Individual %1: evaluation failed").arg(i + 1));
				}

				// 每评估2个个体处理一次事件
				if(i % 2 == 0) {
					QApplication::processEvents();
				}

			} catch(const std::exception& e) {
				emit logMessageGenerated(tr("  Individual %1: Exception: %2").arg(i + 1).arg(e.what()));
				individual.fitness = 1e10;
				individual.isEvaluated = true;
				totalEvaluations++;
				currentGenerationFailed++;
				m_totalEvaluations++;
			} catch(...) {
				emit logMessageGenerated(tr("  Individual %1: Unknown exception").arg(i + 1));
				individual.fitness = 1e10;
				individual.isEvaluated = true;
				totalEvaluations++;
				currentGenerationFailed++;
				m_totalEvaluations++;
			}
		}
	}

	if(m_shouldStop) return;

	double currentSuccessRate = totalEvaluations > 0 ?
		(double)successfulEvaluations / totalEvaluations : 0.0;

	// 更新连续失败代数计数
	if(successfulEvaluations == 0) {
		m_consecutiveFailedGenerations++;
		emit logMessageGenerated(tr("WARNING: No successful evaluations in generation %1 (consecutive failures: %2)")
			.arg(m_currentGeneration + 1).arg(m_consecutiveFailedGenerations));
	} else {
		m_consecutiveFailedGenerations = 0; // 重置连续失败计数
	}

	// 输出当前代统计
	emit logMessageGenerated(tr("Current generation stats: %1 successful, %2 failed out of %3 individuals (success rate: %4%)")
		.arg(successfulEvaluations).arg(currentGenerationFailed)
		.arg(totalEvaluations).arg(currentSuccessRate * 100, 0, 'f', 1));

	// 检查是否需要停止优化
	if(m_consecutiveFailedGenerations >= m_maxConsecutiveFailures) {
		m_lastError = QString("Too many consecutive failed generations (%1)").arg(m_consecutiveFailedGenerations);
		emit logMessageGenerated(tr("ERROR: Too many consecutive failed generations (%1/%2) - stopping optimization")
			.arg(m_consecutiveFailedGenerations).arg(m_maxConsecutiveFailures));
		return;
	}

	// 警告低成功率但不立即停止
	if(currentSuccessRate < 0.5 && m_currentGeneration > 3) {
		emit logMessageGenerated(tr("WARNING: Low success rate (%1%) in generation %2, but continuing optimization")
			.arg(currentSuccessRate * 100, 0, 'f', 1).arg(m_currentGeneration + 1));
	}
}

int nmCalculationAutoFitGA::tournamentSelection()
{
	int bestIndex = qrand() % m_population.size();
	double bestFitness = m_population[bestIndex].fitness;

	for(int i = 1; i < m_tournamentSize; ++i) {
		int candidateIndex = qrand() % m_population.size();
		double candidateFitness = m_population[candidateIndex].fitness;

		if(candidateFitness < bestFitness) {
			bestIndex = candidateIndex;
			bestFitness = candidateFitness;
		}
	}

	return bestIndex;
}

int nmCalculationAutoFitGA::rouletteWheelSelection()
{
	// 计算适应度总和（使用倒数，因为我们要最小化）
	double totalFitness = 0.0;
	double maxFitness = -1e10;

	// 找到最大适应度值
	for(int i = 0; i < m_population.size(); ++i) {
		if(m_population[i].fitness > maxFitness) {
			maxFitness = m_population[i].fitness;
		}
	}

	// 计算转换后的适应度总和
	for(int i = 0; i < m_population.size(); ++i) {
		double transformedFitness = maxFitness - m_population[i].fitness + 1e-6;
		totalFitness += transformedFitness;
	}

	// 轮盘赌选择
	double randomValue = random01() * totalFitness;
	double cumulativeFitness = 0.0;

	for(int i = 0; i < m_population.size(); ++i) {
		double transformedFitness = maxFitness - m_population[i].fitness + 1e-6;
		cumulativeFitness += transformedFitness;

		if(cumulativeFitness >= randomValue) {
			return i;
		}
	}

	return m_population.size() - 1; // 备用选择
}

void nmCalculationAutoFitGA::crossover(const GAIndividual& parent1, const GAIndividual& parent2,
	GAIndividual& offspring1, GAIndividual& offspring2)
{
	if(m_useUniformCrossover) {
		uniformCrossover(parent1, parent2, offspring1, offspring2);
	} else {
		singlePointCrossover(parent1, parent2, offspring1, offspring2);
	}
}

void nmCalculationAutoFitGA::singlePointCrossover(const GAIndividual& parent1, const GAIndividual& parent2,
	GAIndividual& offspring1, GAIndividual& offspring2)
{
	int dimensions = parent1.genes.size();

	if(dimensions == 0) return;

	// 初始化子代
	offspring1.genes.resize(dimensions);
	offspring2.genes.resize(dimensions);
	offspring1.fitness = 1e10;
	offspring2.fitness = 1e10;
	offspring1.isEvaluated = false;
	offspring2.isEvaluated = false;

	// 选择交叉点
	int crossoverPoint = qrand() % dimensions;

	// 执行交叉
	for(int i = 0; i < dimensions; ++i) {
		if(i < crossoverPoint) {
			offspring1.genes[i] = parent1.genes[i];
			offspring2.genes[i] = parent2.genes[i];
		} else {
			offspring1.genes[i] = parent2.genes[i];
			offspring2.genes[i] = parent1.genes[i];
		}
	}
}

void nmCalculationAutoFitGA::uniformCrossover(const GAIndividual& parent1, const GAIndividual& parent2,
	GAIndividual& offspring1, GAIndividual& offspring2)
{
	int dimensions = parent1.genes.size();

	if(dimensions == 0) return;

	// 初始化子代
	offspring1.genes.resize(dimensions);
	offspring2.genes.resize(dimensions);
	offspring1.fitness = 1e10;
	offspring2.fitness = 1e10;
	offspring1.isEvaluated = false;
	offspring2.isEvaluated = false;

	// 均匀交叉
	for(int i = 0; i < dimensions; ++i) {
		if(random01() < 0.5) {
			offspring1.genes[i] = parent1.genes[i];
			offspring2.genes[i] = parent2.genes[i];
		} else {
			offspring1.genes[i] = parent2.genes[i];
			offspring2.genes[i] = parent1.genes[i];
		}
	}
}

void nmCalculationAutoFitGA::mutate(GAIndividual& individual)
{
	// 使用高斯变异作为主要方式
	gaussianMutation(individual);
}

void nmCalculationAutoFitGA::gaussianMutation(GAIndividual& individual)
{
	for(int i = 0; i < individual.genes.size(); ++i) {
		if(random01() < m_mutationRate) {
			int paramIndex = m_enabledParamIndices[i];
			double range = m_parameterUpper[paramIndex] - m_parameterLower[paramIndex];
			double sigma = range * MUTATION_STRENGTH;

			// 高斯变异
			double mutation = gaussianRandom(0.0, sigma);
			individual.genes[i] += mutation;

			// 边界处理
			individual.genes[i] = qMax(m_parameterLower[paramIndex],
				qMin(m_parameterUpper[paramIndex], individual.genes[i]));
		}
	}

	// 标记为未评估
	individual.isEvaluated = false;
}

void nmCalculationAutoFitGA::polynomialMutation(GAIndividual& individual)
{
	const double eta = 20.0; // 分布指数

	for(int i = 0; i < individual.genes.size(); ++i) {
		if(random01() < m_mutationRate) {
			int paramIndex = m_enabledParamIndices[i];
			double lower = m_parameterLower[paramIndex];
			double upper = m_parameterUpper[paramIndex];
			double y = individual.genes[i];

			double delta1 = (y - lower) / (upper - lower);
			double delta2 = (upper - y) / (upper - lower);

			double rnd = random01();
			double mut_pow = 1.0 / (eta + 1.0);

			double deltaq;

			if(rnd <= 0.5) {
				double xy = 1.0 - delta1;
				double val = 2.0 * rnd + (1.0 - 2.0 * rnd) * pow(xy, eta + 1.0);
				deltaq = pow(val, mut_pow) - 1.0;
			} else {
				double xy = 1.0 - delta2;
				double val = 2.0 * (1.0 - rnd) + 2.0 * (rnd - 0.5) * pow(xy, eta + 1.0);
				deltaq = 1.0 - pow(val, mut_pow);
			}

			y += deltaq * (upper - lower);
			individual.genes[i] = qMax(lower, qMin(upper, y));
		}
	}

	// 标记为未评估
	individual.isEvaluated = false;
}

void nmCalculationAutoFitGA::applyElitism(QVector<GAIndividual>& newPopulation)
{
	int eliteCount = static_cast<int>(m_populationSize * m_elitismRate);

	if(eliteCount == 0) return;

	// 对种群按适应度排序
	QVector<GAIndividual> sortedPopulation = m_population;

	// 冒泡排序（适应度从小到大）
	for(int i = 0; i < sortedPopulation.size() - 1; ++i) {
		for(int j = 0; j < sortedPopulation.size() - 1 - i; ++j) {
			if(sortedPopulation[j].fitness > sortedPopulation[j + 1].fitness) {
				GAIndividual temp = sortedPopulation[j];
				sortedPopulation[j] = sortedPopulation[j + 1];
				sortedPopulation[j + 1] = temp;
			}
		}
	}

	// 复制精英个体到新种群
	for(int i = 0; i < eliteCount && i < sortedPopulation.size(); ++i) {
		newPopulation.append(sortedPopulation[i]);
	}

	DEBUG_OUT(QString("Applied elitism: %1 elite individuals preserved").arg(eliteCount));
}

void nmCalculationAutoFitGA::updatePopulationStatistics()
{
	if(m_population.isEmpty()) return;

	m_previousBestFitness = m_bestFitness;

	double sum = 0.0;
	double minFitness = 1e10;
	double maxFitness = -1e10;
	int validCount = 0;

	for(int i = 0; i < m_population.size(); ++i) {
		const GAIndividual& individual = m_population[i];

		if(individual.isEvaluated && individual.fitness < 1e9) {
			sum += individual.fitness;
			validCount++;

			if(individual.fitness < minFitness) {
				minFitness = individual.fitness;
			}

			if(individual.fitness > maxFitness) {
				maxFitness = individual.fitness;
			}

			// 更新全局最优
			if(individual.fitness < m_bestFitness) {
				// 只有显著改进时才更新
				double improvement = (m_bestFitness - individual.fitness);
				double relativeImprovement = improvement / qMax(1e-10, qAbs(m_bestFitness));

				if(relativeImprovement > m_improvementThreshold) {
					m_bestFitness = individual.fitness;
					m_bestIndividual = individual;

					DEBUG_OUT(QString("Global best updated with %1% improvement: %2")
						.arg(relativeImprovement * 100, 0, 'f', 3)
						.arg(m_bestFitness, 0, 'e', 4));
				}
			}
		}
	}

	if(validCount > 0) {
		m_averageFitness = sum / validCount;
		m_worstFitness = maxFitness;
	} else {
		m_averageFitness = 1e10;
		m_worstFitness = 1e10;
	}

	// 在没有找到更好解时才检查精英保护
	if(m_hasValidUserSolution && m_userInitialFitness < m_bestFitness) {
		DEBUG_OUT("Elite protection: No significant improvement found, checking initial solution");

		// 检查初始解是否仍然是最优的
		double improvement = m_bestFitness - m_userInitialFitness;
		double relativeImprovement = improvement / qMax(1e-10, qAbs(m_bestFitness));

		if(relativeImprovement > m_improvementThreshold * 0.5) { // 使用更宽松的阈值
			DEBUG_OUT("Elite protection: Restoring user initial solution");
			m_bestFitness = m_userInitialFitness;
			m_bestIndividual.genes = m_userInitialSolution;
			m_bestIndividual.fitness = m_userInitialFitness;
			m_bestIndividual.isEvaluated = true;
		}
	}
}

bool nmCalculationAutoFitGA::checkConvergence()
{
	if(m_convergenceHistory.size() < CONVERGENCE_CHECK_INTERVAL) {
		return false;
	}

	// 检查最近几次世代的改进
	double recentBest = m_convergenceHistory.last();
	double oldBest = m_convergenceHistory[m_convergenceHistory.size() - CONVERGENCE_CHECK_INTERVAL];

	double improvement = oldBest - recentBest;

	// 如果连续多代没有显著改进，认为已收敛
	if(improvement < MIN_FITNESS_IMPROVEMENT) {
		// 检查是否连续停滞
		int stagnationCount = 0;

		for(int i = m_convergenceHistory.size() - 1; i >= qMax(0, m_convergenceHistory.size() - MAX_STAGNATION_GENERATIONS); --i) {
			if(i > 0) {
				double diff = m_convergenceHistory[i - 1] - m_convergenceHistory[i];

				if(diff < MIN_FITNESS_IMPROVEMENT) {
					stagnationCount++;
				} else {
					break;
				}
			}
		}

		return stagnationCount >= MAX_STAGNATION_GENERATIONS;
	}

	return false;
}

void nmCalculationAutoFitGA::adaptiveParameterUpdate(int generation)
{
	// 自适应调整变异率
	double progress = static_cast<double>(generation) / m_maxGenerations;

	// 早期探索，后期开发
	m_mutationRate = 0.2 * (1.0 - progress) + 0.05 * progress;

	// 自适应调整交叉率
	if(generation > 0) {
		double improvement = m_previousBestFitness - m_bestFitness;

		if(improvement < MIN_FITNESS_IMPROVEMENT) {
			// 如果改进很小，增加探索性
			m_mutationRate = qMin(0.3, m_mutationRate * 1.1);
			m_crossoverRate = qMax(0.6, m_crossoverRate * 0.95);
		} else {
			// 如果有明显改进，增加开发性
			m_mutationRate = qMax(0.05, m_mutationRate * 0.9);
			m_crossoverRate = qMin(0.9, m_crossoverRate * 1.05);
		}
	}
}

void nmCalculationAutoFitGA::validateAndProtectFinalResult()
{
	if(!m_hasValidUserSolution) {
		emit logMessageGenerated(tr("No initial solution for elite protection"));
		return;
	}

	emit logMessageGenerated(tr("=== Final Result Validation (Elite Protection) ==="));

	// 使用已有的评估结果
	double finalFitness = m_bestFitness;
	double initialFitness = m_userInitialFitness;

	emit logMessageGenerated(tr("Comparing results: Initial=%1, Final=%2")
		.arg(initialFitness, 0, 'e', 4).arg(finalFitness, 0, 'e', 4));

	// 计算改进程度
	double improvement = initialFitness - finalFitness;
	double relativeImprovement = improvement / qMax(1e-10, qAbs(initialFitness));

	emit logMessageGenerated(tr("Improvement: %1 (%2%)")
		.arg(improvement, 0, 'e', 4).arg(relativeImprovement * 100, 0, 'f', 2));

	if(relativeImprovement < m_improvementThreshold) {
		emit logMessageGenerated(tr("Elite protection triggered: insufficient improvement"));
		emit logMessageGenerated(tr("Threshold: %1%, Actual: %2%")
			.arg(m_improvementThreshold * 100, 0, 'f', 2)
			.arg(relativeImprovement * 100, 0, 'f', 4));
		emit logMessageGenerated(tr("Restoring initial solution as final result"));

		m_bestFitness = initialFitness;
		m_bestIndividual.genes = m_userInitialSolution;
		m_bestIndividual.fitness = initialFitness;
		m_bestIndividual.isEvaluated = true;

		emit logMessageGenerated(tr("Initial solution restored successfully"));
	} else {
		emit logMessageGenerated(tr("Final result validated - significant improvement achieved"));
	}
}

StopReasonGA nmCalculationAutoFitGA::analyzeOptimizationStatus()
{
	// 1. 检查用户停止
	if(m_shouldStop) {
		return GA_USER_STOPPED;
	}

	// 2. 检查连续失败
	if(m_consecutiveFailedGenerations >= m_maxConsecutiveFailures) {
		return GA_CONSECUTIVE_FAILURES;
	}

	// 3. 检查是否达到目标精度
	if(m_bestFitness < m_targetError) {
		return GA_TARGET_ACHIEVED;
	}

	// 4. 检查是否达到最大迭代数
	if(m_currentGeneration >= m_maxGenerations - 1) {
		return GA_MAX_ITERATIONS;
	}

	// 5. 需要足够的历史数据才能判断收敛
	if(m_convergenceHistory.size() < m_localOptimumWindow) {
		return GA_CONTINUE_OPTIMIZATION;
	}

	// 6. 检查真正的收敛
	if(m_convergenceHistory.size() >= m_trueConvergenceWindow && checkTrueConvergence()) {
		return GA_TRUE_CONVERGENCE;
	}

	// 7. 检查局部最优陷阱
	if(checkLocalOptimumTrap()) {
		return GA_LOCAL_OPTIMUM;
	}

	return GA_CONTINUE_OPTIMIZATION;
}

bool nmCalculationAutoFitGA::checkTrueConvergence() const
{
	if(m_convergenceHistory.size() < m_trueConvergenceWindow) {
		return false;
	}

	// 1. 检查解质量 - 如果已经接近目标，小改进可能是真收敛
	bool nearTarget = (m_bestFitness < m_targetError * m_nearTargetFactor);

	// 2. 检查适应度稳定性 - 长期小幅波动
	double recentVariance = calculateFitnessVariance(10);
	double recentMean = 0.0;
	int windowSize = qMin(10, m_convergenceHistory.size());

	// 计算最近窗口的均值
	for(int i = m_convergenceHistory.size() - windowSize; i < m_convergenceHistory.size(); ++i) {
		recentMean += m_convergenceHistory[i];
	}

	recentMean /= windowSize;

	double relativeVariance = recentVariance / qMax(1e-10, recentMean * recentMean);
	bool stableError = (relativeVariance < m_convergenceVarianceThreshold);

	// 3. 检查种群多样性 - 应该收敛到同一区域
	double currentDiversity = calculatePopulationDiversity();
	bool lowDiversity = (currentDiversity < m_diversityThreshold);

	// 4. 检查长期改进趋势
	double longTermImprovement = calculateLongTermImprovement(m_trueConvergenceWindow);
	bool minimalLongTermImprovement = (longTermImprovement < 1e-4); // 0.01%

	// 真收敛的判断条件
	bool isConverged = nearTarget ||
		(stableError && lowDiversity && minimalLongTermImprovement);

	if(isConverged) {
		DEBUG_OUT("=== TRUE CONVERGENCE ANALYSIS ===");
		DEBUG_OUT(QString("nearTarget=%1 (fitness=%2, target*factor=%3)")
			.arg(nearTarget).arg(m_bestFitness, 0, 'e', 4)
			.arg(m_targetError * m_nearTargetFactor, 0, 'e', 4));
		DEBUG_OUT(QString("stableError=%1 (relativeVariance=%2)")
			.arg(stableError).arg(relativeVariance, 0, 'e', 6));
		DEBUG_OUT(QString("lowDiversity=%1 (diversity=%2, threshold=%3)")
			.arg(lowDiversity).arg(currentDiversity, 0, 'f', 6).arg(m_diversityThreshold));
		DEBUG_OUT(QString("longTermImprovement=%1%")
			.arg(longTermImprovement * 100, 0, 'f', 4));
	}

	return isConverged;
}

bool nmCalculationAutoFitGA::checkLocalOptimumTrap() const
{
	if(m_convergenceHistory.size() < m_localOptimumWindow) {
		return false;
	}

	// 1. 检查解质量 - 如果距离目标还很远，停滞就可能是局部最优
	bool farFromTarget = (m_bestFitness > m_targetError * m_farTargetFactor);

	// 2. 检查短期改进 - 近期改进非常小
	double shortTermImprovement = calculateLongTermImprovement(m_localOptimumWindow);
	bool poorShortTermImprovement = (shortTermImprovement < 1e-5); // 0.001%

	// 3. 检查种群多样性 - 可能过早聚集或无效分散
	double currentDiversity = calculatePopulationDiversity();
	bool problematicDiversity = (currentDiversity < m_diversityThreshold * 0.1) ||
		(currentDiversity > m_diversityThreshold * 5.0);

	// 4. 检查适应度方差 - 可能卡在平坦区域
	double fitnessVariance = calculateFitnessVariance(m_localOptimumWindow);
	bool flatFitnessLandscape = (fitnessVariance < m_convergenceVarianceThreshold * 0.1);

	// 局部最优的判断条件
	bool isLocalOptimum = farFromTarget && poorShortTermImprovement &&
		(problematicDiversity || flatFitnessLandscape);

	if(isLocalOptimum) {
		DEBUG_OUT("=== LOCAL OPTIMUM ANALYSIS ===");
		DEBUG_OUT(QString("farFromTarget=%1 (fitness=%2, target*factor=%3)")
			.arg(farFromTarget).arg(m_bestFitness, 0, 'e', 4)
			.arg(m_targetError * m_farTargetFactor, 0, 'e', 4));
		DEBUG_OUT(QString("poorShortTermImprovement=%1 (improvement=%2%)")
			.arg(poorShortTermImprovement).arg(shortTermImprovement * 100, 0, 'f', 4));
		DEBUG_OUT(QString("problematicDiversity=%1 (diversity=%2)")
			.arg(problematicDiversity).arg(currentDiversity, 0, 'f', 6));
		DEBUG_OUT(QString("fitnessVariance=%1")
			.arg(fitnessVariance, 0, 'e', 6));
	}

	return isLocalOptimum;
}

double nmCalculationAutoFitGA::calculatePopulationDiversity() const
{
	if(m_population.size() < 2) return 0.0;

	int dimensions = getEnabledParameterCount();

	if(dimensions == 0) return 0.0;

	double totalDiversity = 0.0;

	for(int dim = 0; dim < dimensions; ++dim) {
		// 计算该维度上所有个体的均值
		double mean = 0.0;

		for(int i = 0; i < m_population.size(); ++i) {
			mean += m_population[i].genes[dim];
		}

		mean /= m_population.size();

		// 计算该维度上的方差
		double variance = 0.0;

		for(int i = 0; i < m_population.size(); ++i) {
			double diff = m_population[i].genes[dim] - mean;
			variance += diff * diff;
		}

		variance /= m_population.size();

		// 归一化到参数范围
		int paramIndex = m_enabledParamIndices[dim];
		double range = m_parameterUpper[paramIndex] - m_parameterLower[paramIndex];
		double normalizedStd = sqrt(variance) / qMax(1e-10, range);

		totalDiversity += normalizedStd;
	}

	return totalDiversity / dimensions;
}

double nmCalculationAutoFitGA::calculateFitnessVariance(int windowSize) const
{
	if(m_convergenceHistory.size() < windowSize) {
		return 1e10; // 数据不足，返回大值
	}

	// 计算最近windowSize次迭代的方差
	double mean = 0.0;
	int startIdx = m_convergenceHistory.size() - windowSize;

	for(int i = startIdx; i < m_convergenceHistory.size(); ++i) {
		mean += m_convergenceHistory[i];
	}

	mean /= windowSize;

	double variance = 0.0;

	for(int i = startIdx; i < m_convergenceHistory.size(); ++i) {
		double diff = m_convergenceHistory[i] - mean;
		variance += diff * diff;
	}

	variance /= windowSize;

	return variance;
}

double nmCalculationAutoFitGA::calculateLongTermImprovement(int windowSize) const
{
	if(m_convergenceHistory.size() < windowSize) {
		return 1.0; // 数据不足，假设有改进
	}

	double oldFitness = m_convergenceHistory[m_convergenceHistory.size() - windowSize];
	double improvement = (oldFitness - m_bestFitness) / qMax(1e-10, qAbs(oldFitness));

	return improvement;
}

void nmCalculationAutoFitGA::updateConvergenceMetrics()
{
	// 更新多样性历史
	m_diversityHistory.append(calculatePopulationDiversity());

	// 保持历史长度合理（最多保留50个数据点）
	const int maxHistorySize = 50;

	while(m_diversityHistory.size() > maxHistorySize) {
		m_diversityHistory.remove(0);
	}
}

// ==================== 参数应用方法 ====================

bool nmCalculationAutoFitGA::validateParameters(const QVector<double>& parameters) const
{
	if(parameters.size() != getEnabledParameterCount()) {
		return false;
	}

	for(int i = 0; i < parameters.size(); ++i) {
		if(!isFiniteNumber(parameters[i])) {
			return false;
		}

		// 检查参数范围
		if(i < m_enabledParamIndices.size()) {
			int paramIndex = m_enabledParamIndices[i];

			if(paramIndex >= 0 && paramIndex < m_parameterLower.size()) {
				if(parameters[i] < m_parameterLower[paramIndex] ||
					parameters[i] > m_parameterUpper[paramIndex]) {
						return false;
				}
			}
		}
	}

	// 直接拦截会导致求解器数值崩溃的参数值
	for(int i = 0; i < parameters.size() && i < m_enabledParamIndices.size(); ++i) {
		int paramIndex = m_enabledParamIndices[i];
		double value = parameters[i];

		switch(paramIndex) {
		case 0: // 渗透率：必须大于零
			if(value <= 1e-8) {
				DEBUG_OUT(QString("Rejecting near-zero permeability: %1").arg(value));
				return false;
			}

			break;

		case 2: // 井筒储集系数：必须大于零
			if(value <= 1e-10) {
				DEBUG_OUT(QString("Rejecting near-zero wellbore storage: %1").arg(value));
				return false;
			}

			break;

		case 3: // 孔隙度：必须在合理范围
			if(value <= 1e-6 || value >= 0.99) {
				DEBUG_OUT(QString("Rejecting unrealistic porosity: %1").arg(value));
				return false;
			}

			break;

		case 6: // 综合压缩系数：必须大于零
			if(value <= 1e-8) {
				DEBUG_OUT(QString("Rejecting near-zero total compressibility: %1").arg(value));
				return false;
			}

			break;
		}
	}

	return true;
}

bool nmCalculationAutoFitGA::validateLogLogData(const QVector<QVector<double>>& logLogData) const
{
	// 检查基本结构
	if(logLogData.size() < 3) {
		DEBUG_OUT("LogLog data has less than 3 arrays");
		return false;
	}

	// 检查数组大小一致性
	int size = logLogData[0].size();

	if(size == 0) {
		DEBUG_OUT("Empty LogLog data");
		return false;
	}

	if(logLogData[1].size() != size || logLogData[2].size() != size) {
		DEBUG_OUT(QString("LogLog data size mismatch: X=%1, Y1=%2, Y2=%3")
			.arg(logLogData[0].size())
			.arg(logLogData[1].size())
			.arg(logLogData[2].size()));
		return false;
	}

	// 检查最小数据点数
	if(size < 5) {
		DEBUG_OUT(QString("Too few LogLog data points: %1").arg(size));
		return false;
	}

	// 数据有效性检查
	for(int i = 0; i < size; ++i) {
		if(!isFiniteNumber(logLogData[0][i]) ||
			!isFiniteNumber(logLogData[1][i]) ||
			!isFiniteNumber(logLogData[2][i])) {
				DEBUG_OUT(QString("Invalid LogLog data at index %1").arg(i));
				return false;
		}
	}

	return true;
}

bool nmCalculationAutoFitGA::validateInitialValues() const
{
	if(m_initialValues.size() != m_enabledParamIndices.size()) {
		DEBUG_OUT("Initial values count mismatch with enabled parameters");
		return false;
	}

	bool allValid = true;

	for(int i = 0; i < m_initialValues.size(); ++i) {
		int paramIndex = m_enabledParamIndices[i];
		double value = m_initialValues[i];

		if(!isFiniteNumber(value)) {
			DEBUG_OUT(QString("Initial value[%1] is not finite: %2").arg(i).arg(value));
			allValid = false;
			continue;
		}

		if(paramIndex < m_parameterLower.size() && paramIndex < m_parameterUpper.size()) {
			double minVal = m_parameterLower[paramIndex];
			double maxVal = m_parameterUpper[paramIndex];

			if(value < minVal || value > maxVal) {
				DEBUG_OUT(QString("Initial value[%1] = %2 is outside bounds [%3, %4]")
					.arg(i).arg(value).arg(minVal).arg(maxVal));
				allValid = false;
			}
		}
	}

	return allValid;
}

void nmCalculationAutoFitGA::applyParametersToDataManager(const QVector<double>& parameters)
{
	if(parameters.size() != getEnabledParameterCount()) {
		return;
	}

	updateReservoirParameters(parameters);
	updateWellParameters(parameters);
}

void nmCalculationAutoFitGA::updateReservoirParameters(const QVector<double>& parameters)
{
	nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
	nmDataReservoir reservoirData = dataManager->getReservoirDataCopy();

	int paramIndex = 0;
	for(int i = 0; i < m_parameterSelected.size() && i < 11; ++i) {
		if(m_parameterSelected[i] && paramIndex < parameters.size()) {
			double value = parameters[paramIndex];

			switch(i) {
			case 0: // 渗透率
				reservoirData.getPermeability().setValue(value);
				break;
			case 3: // 孔隙度
				reservoirData.getPorosity().setValue(value);
				break;
			case 4: // 初始压力
				reservoirData.getInitialPressure().setValue(value);
				break;
			case 5: // 储层厚度
				reservoirData.getThickness().setValue(value);
				break;
			case 6: // 综合压缩系数
				reservoirData.getCt().setValue(value);
				break;
			case 7: // 岩石压缩系数
				reservoirData.getCf().setValue(value);
				break;
			case 8: // 初始含油饱和度
				reservoirData.getSoi().setValue(value);
				break;
			case 9: // 初始含水饱和度
				reservoirData.getSwi().setValue(value);
				break;
			case 10: // 初始含气饱和度
				reservoirData.getSgi().setValue(value);
				break;
			}
			paramIndex++;
		}
	}

	// 更新数据管理器
	dataManager->updateReservoirData(reservoirData);
}

void nmCalculationAutoFitGA::updateWellParameters(const QVector<double>& parameters)
{
	nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();

	// 获取井列表，更新第一口井的参数
	//QVector<nmDataWellBase*> wells = dataManager->getWellDataList();
	nmDataWellBase* pWell = dataManager->findWellByName(m_targetWellName);
	if(!pWell) return;

	//nmDataWellBase* pWell = wells[0]; // 使用第一口井

	int paramIndex = 0;
	for(int i = 0; i < m_parameterSelected.size() && i < 9; ++i) {
		if(m_parameterSelected[i] && paramIndex < parameters.size()) {
			double value = parameters[paramIndex];

			switch(i) {
			case 1: { // 表皮系数
                nmDataAttribute skinAttr = pWell->getPerforation(0)->getSkin();
				skinAttr.setValue(value);
                pWell->setRateDependentSkin(skinAttr);
					}
					break;
			case 2: { // 井筒储集系数
				nmDataAttribute wellboreAttr = pWell->getWellboreStorage();
				wellboreAttr.setValue(value);
				pWell->setWellboreStorage(wellboreAttr);
					}
					break;
			}
			paramIndex++;
		}
	}

	// 根据井类型更新到数据管理器
	updateWellToDataManager(pWell);
}

void nmCalculationAutoFitGA::updateWellToDataManager(nmDataWellBase* pWell)
{
	if(!pWell) return;

	nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
	NM_WELL_MODEL wellType = pWell->getWellType();

	switch(wellType) {
	case NM_WELL_MODEL::Vertical_Well: {
		nmDataVerticalWell* pVerticalWell = dynamic_cast<nmDataVerticalWell*>(pWell);
		if(pVerticalWell) {
			QVector<nmDataVerticalWell> wells;
			wells.append(*pVerticalWell);
			dataManager->updateVerticalWells(wells);
		}
		break;
									   }
	case NM_WELL_MODEL::Vertical_Fractured_Well: {
		nmDataVerticalFracturedWell* pVFracturedWell = dynamic_cast<nmDataVerticalFracturedWell*>(pWell);
		if(pVFracturedWell) {
			QVector<nmDataVerticalFracturedWell> wells;
			wells.append(*pVFracturedWell);
			dataManager->updateVerticalFracturedWells(wells);
		}
		break;
												 }
	case NM_WELL_MODEL::Horizontal_Fractured_Well: {
		nmDataHorizontalFracturedWell* pHFracturedWell = dynamic_cast<nmDataHorizontalFracturedWell*>(pWell);
		if(pHFracturedWell) {
			QVector<nmDataHorizontalFracturedWell> wells;
			wells.append(*pHFracturedWell);
			dataManager->updateHorizontalFracturedWells(wells);
		}
		break;
												   }
	default:
		break;
	}
}

void nmCalculationAutoFitGA::clampToLimits(QVector<double>& parameters) const
{
	for(int i = 0; i < parameters.size() && i < m_enabledParamIndices.size(); ++i) {
		int paramIndex = m_enabledParamIndices[i];
		if(paramIndex >= 0 && paramIndex < m_parameterLower.size()) {
			parameters[i] = qMax(m_parameterLower[paramIndex],
				qMin(m_parameterUpper[paramIndex], parameters[i]));
		}
	}
}

// ==================== 求解器相关方法 ====================

QVector<QVector<double>> nmCalculationAutoFitGA::runSolver()
{
	//return runSolverExe();
	return runSolverDll();
}

bool nmCalculationAutoFitGA::validateSolverResult(const QVector<QVector<double>>& result) const
{
	// 基本检查
	if(result.size() < 2) {
		DEBUG_OUT("Solver result has less than 2 arrays");
		return false;
	}

	if(result[0].size() != result[1].size()) {
		DEBUG_OUT(QString("Size mismatch: X=%1, Y=%2").arg(result[0].size()).arg(result[1].size()));
		return false;
	}

	if(result[0].size() == 0) {
		DEBUG_OUT("Empty solver result");
		return false;
	}

	// 检查最小数据点数
	if(result[0].size() < 10) {
		DEBUG_OUT(QString("Too few data points: %1").arg(result[0].size()));
		return false;
	}

	// 数据有效性检查
	for(int i = 0; i < result[0].size(); ++i) {
		if(!isFiniteNumber(result[0][i]) || !isFiniteNumber(result[1][i])) {
			DEBUG_OUT(QString("Invalid data at index %1: X=%2, Y=%3")
				.arg(i).arg(result[0][i]).arg(result[1][i]));
			return false;
		}
	}

	return true;
}

QVector<QVector<double>> nmCalculationAutoFitGA::runSolverDll()
{
	DEBUG_OUT("SOLVER DLL START");

	if(m_evaluationInProgress > 0) {
		DEBUG_OUT("DLL Solver already running, skipping");
		return QVector<QVector<double>>();
	}

	++m_evaluationInProgress;
	QVector<QVector<double>> result;
	nmCalculationDllPebiSolverTask* dllTask = nullptr;

	try {
		DEBUG_OUT("Creating DLL solver task");
		dllTask = new nmCalculationDllPebiSolverTask(m_tempDirectory);

		if(m_shouldStop) {
			DEBUG_OUT("Should stop - cleaning up and returning empty result");
			delete dllTask;
			--m_evaluationInProgress;
			return result;
		}

		DEBUG_OUT("Starting DLL solver execution...");

		// 异步执行
		dllTask->start();

		// 等待完成
		int waitTime = 0;
		const int maxWait = 30000;  // 30秒超时
		const int checkInterval = 500;

		while(waitTime < maxWait) {
			QApplication::processEvents(QEventLoop::ExcludeUserInputEvents, 100);

			if(!dllTask->isRunning()) {
				DEBUG_OUT("DLL solver task completed");
				break;
			}

			if(m_shouldStop) {
				DEBUG_OUT("DLL solver task terminated by user");
				dllTask->terminate();
				break;
			}

			msleep(checkInterval);
			waitTime += checkInterval;
		}

		// 超时处理
		if(dllTask->isRunning()) {
			DEBUG_OUT("DLL solver task timeout, terminating...");
			dllTask->terminate();
			dllTask->wait(2000);

			delete dllTask;
			dllTask = nullptr;
			--m_evaluationInProgress;
			m_consecutiveFailures++;
			return result;
		}

		// 验证结果数据是否已更新
		nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
		//QVector<nmDataWellBase*> wells = dataManager->getWellDataList();
		nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName);

		if(!pTargetWell) {
			DEBUG_OUT("No wells found in data manager after DLL execution");
			delete dllTask;
			--m_evaluationInProgress;
			return result;
		}

		// 验证结果数据
		QVector<QVector<double>> pressureResult = pTargetWell->getResultPressure();
		QVector<QVector<double>> logLogResult = pTargetWell->getResultLogLog();

		DEBUG_OUT(QString("DLL result verification - Pressure arrays: %1, LogLog arrays: %2")
			.arg(pressureResult.size()).arg(logLogResult.size()));

		if(pressureResult.size() >= 2) {
			DEBUG_OUT(QString("Pressure result - Time points: %1, Pressure points: %2")
				.arg(pressureResult[0].size()).arg(pressureResult[1].size()));

			if(pressureResult[0].size() > 0) {
				DEBUG_OUT(QString("Sample pressure data - Time[0]: %1, Time[last]: %2, P[0]: %3, P[last]: %4")
					.arg(pressureResult[0][0])
					.arg(pressureResult[0][pressureResult[0].size()-1])
					.arg(pressureResult[1][0])
					.arg(pressureResult[1][pressureResult[1].size()-1]));
			}
		}

		// 数据有效性检查
		if(pressureResult.size() >= 2 && pressureResult[0].size() > 0 && pressureResult[1].size() > 0) {
			result = pressureResult;
			DEBUG_OUT(QString("Got DLL solver result: %1 points").arg(result[0].size()));
			m_consecutiveFailures = 0;

			// 检查结果是否与之前不同
			static QVector<double> lastPressureResult;
			bool isDifferentFromLast = false;

			if(lastPressureResult.isEmpty() || lastPressureResult.size() != pressureResult[1].size()) {
				isDifferentFromLast = true;
			} else {
				for(int i = 0; i < qMin(5, pressureResult[1].size()); ++i) {
					if(qAbs(lastPressureResult[i] - pressureResult[1][i]) > 1e-12) {
						isDifferentFromLast = true;
						break;
					}
				}
			}

			if(isDifferentFromLast) {
				DEBUG_OUT("RESULT VERIFICATION: Got NEW result data from DLL");
				lastPressureResult = pressureResult[1];
			} else {
				DEBUG_OUT("!!! WARNING: Result data appears to be identical to previous run !!!");
			}

		} else {
			DEBUG_OUT("DLL solver result is empty or invalid");
			DEBUG_OUT(QString("Pressure result size: %1, Array sizes: %2, %3")
				.arg(pressureResult.size())
				.arg(pressureResult.size() > 0 ? pressureResult[0].size() : 0)
				.arg(pressureResult.size() > 1 ? pressureResult[1].size() : 0));
			m_consecutiveFailures++;
		}

	} catch(const std::bad_alloc& e) {
		DEBUG_OUT(QString("Memory allocation failed in DLL solver: %1").arg(e.what()));
		m_consecutiveFailures++;
	} catch(const std::exception& e) {
		DEBUG_OUT(QString("Exception in DLL solver: %1").arg(e.what()));
		m_consecutiveFailures++;
	} catch(...) {
		DEBUG_OUT("Unknown exception in DLL solver");
		m_consecutiveFailures++;
	}

	// 清理DLL任务
	if(dllTask) {
		if(dllTask->isRunning()) {
			dllTask->terminate();
			dllTask->wait(3000);
		}

		DEBUG_OUT("Cleaning up DLL solver task...");
		delete dllTask;
		dllTask = nullptr;
	}

	QApplication::processEvents(QEventLoop::AllEvents, 100);
	--m_evaluationInProgress;

	DEBUG_OUT(QString("SOLVER DLL END - ResultPoints: %1")
		.arg(result.isEmpty() ? 0 : result[0].size()));

	return result;
}

//QVector<QVector<double>> nmCalculationAutoFitGA::runSolverExe()
//{
//	DEBUG_OUT("SOLVER EXE START");
//
//	if(m_evaluationInProgress > 0) {
//		DEBUG_OUT("EXE Solver already running, skipping");
//		return QVector<QVector<double>>();
//	}
//
//	++m_evaluationInProgress;
//	QVector<QVector<double>> result;
//	nmCalculationExeSolverTask* exeTask = nullptr;
//
//	try {
//		DEBUG_OUT("Creating EXE solver task");
//		exeTask = new nmCalculationExeSolverTask(QString());
//
//		if(m_shouldStop) {
//			DEBUG_OUT("Should stop - cleaning up and returning empty result");
//			delete exeTask;
//			--m_evaluationInProgress;
//			return result;
//		}
//
//		DEBUG_OUT("Starting EXE solver execution...");
//
//		// 启动
//		bool executeSuccess = exeTask->execute();
//
//		// 检查执行状态
//		if(!executeSuccess) {
//			DEBUG_OUT(QString("EXE solver execution failed: %1").arg(exeTask->getLastError()));
//			DEBUG_OUT(QString("EXE solver exit code: %1").arg(exeTask->getExitCode()));
//
//			// 清理并返回空结果
//			delete exeTask;
//			exeTask = nullptr;
//			--m_evaluationInProgress;
//			m_consecutiveFailures++;
//			return result;
//		}
//
//		DEBUG_OUT("EXE solver execution completed successfully");
//
//		// 验证结果数据是否已更新
//		nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance();
//		QVector<nmDataWellBase*> wells = dataManager->getWellDataList();
//
//		if(wells.isEmpty()) {
//			DEBUG_OUT("No wells found in data manager after EXE execution");
//			delete exeTask;
//			--m_evaluationInProgress;
//			return result;
//		}
//
//		// 验证结果数据的时间戳或唯一性
//		QVector<QVector<double>> pressureResult = wells[0]->getResultPressure();
//		QVector<QVector<double>> logLogResult = wells[0]->getResultLogLog();
//
//		DEBUG_OUT(QString("Raw result verification - Pressure arrays: %1, LogLog arrays: %2")
//			.arg(pressureResult.size()).arg(logLogResult.size()));
//
//		if(pressureResult.size() >= 2) {
//			DEBUG_OUT(QString("Pressure result - Time points: %1, Pressure points: %2")
//				.arg(pressureResult[0].size()).arg(pressureResult[1].size()));
//
//			if(pressureResult[0].size() > 0) {
//				DEBUG_OUT(QString("Sample pressure data - Time[0]: %1, Time[last]: %2, P[0]: %3, P[last]: %4")
//					.arg(pressureResult[0][0])
//					.arg(pressureResult[0][pressureResult[0].size() - 1])
//					.arg(pressureResult[1][0])
//					.arg(pressureResult[1][pressureResult[1].size() - 1]));
//			}
//		}
//
//		if(logLogResult.size() >= 3) {
//			DEBUG_OUT(QString("LogLog result - X points: %1, Y1 points: %2, Y2 points: %3")
//				.arg(logLogResult[0].size()).arg(logLogResult[1].size()).arg(logLogResult[2].size()));
//
//			if(logLogResult[0].size() > 0) {
//				DEBUG_OUT(QString("Sample LogLog data - X[0]: %1, X[last]: %2, Y1[0]: %3, Y2[0]: %4")
//					.arg(logLogResult[0][0])
//					.arg(logLogResult[0][logLogResult[0].size() - 1])
//					.arg(logLogResult[1][0])
//					.arg(logLogResult[2][0]));
//			}
//		}
//
//		// 数据有效性检查
//		if(pressureResult.size() >= 2 && pressureResult[0].size() > 0 && pressureResult[1].size() > 0) {
//			result = pressureResult;
//			DEBUG_OUT(QString("Got EXE solver result: %1 points").arg(result[0].size()));
//			m_consecutiveFailures = 0;
//
//			// 检查结果是否与之前不同
//			static QVector<double> lastPressureResult;
//			bool isDifferentFromLast = false;
//
//			if(lastPressureResult.isEmpty() || lastPressureResult.size() != pressureResult[1].size()) {
//				isDifferentFromLast = true;
//			} else {
//				for(int i = 0; i < qMin(5, pressureResult[1].size()); ++i) {
//					if(qAbs(lastPressureResult[i] - pressureResult[1][i]) > 1e-12) {
//						isDifferentFromLast = true;
//						break;
//					}
//				}
//			}
//
//			if(isDifferentFromLast) {
//				DEBUG_OUT("RESULT VERIFICATION: Got NEW result data from EXE");
//				lastPressureResult = pressureResult[1];
//			} else {
//				DEBUG_OUT("!!! WARNING: Result data appears to be identical to previous run !!!");
//			}
//
//		} else {
//			DEBUG_OUT("EXE solver result is empty or invalid");
//			DEBUG_OUT(QString("Pressure result size: %1, Array sizes: %2, %3")
//				.arg(pressureResult.size())
//				.arg(pressureResult.size() > 0 ? pressureResult[0].size() : 0)
//				.arg(pressureResult.size() > 1 ? pressureResult[1].size() : 0));
//			m_consecutiveFailures++;
//		}
//
//	} catch(const std::exception& e) {
//		DEBUG_OUT(QString("Exception in EXE solver: %1").arg(e.what()));
//		m_consecutiveFailures++;
//	} catch(...) {
//		DEBUG_OUT("Unknown exception in EXE solver");
//		m_consecutiveFailures++;
//	}
//
//	// 清理EXE任务
//	if(exeTask) {
//		DEBUG_OUT("Cleaning up EXE solver task...");
//		delete exeTask;
//		exeTask = nullptr;
//	}
//
//	QApplication::processEvents(QEventLoop::AllEvents, 100);
//	--m_evaluationInProgress;
//
//	DEBUG_OUT(QString("SOLVER EXE END - ResultPoints: %1")
//		.arg(result.isEmpty() ? 0 : result[0].size()));
//
//	return result;
//}

// ==================== 数据处理方法 ====================
QVector<QPointF> nmCalculationAutoFitGA::interpolateData(
	const QVector<QPointF>& source, const QVector<double>& targetX) const
{
	QVector<QPointF> result;

	if(source.isEmpty() || targetX.isEmpty()) {
		DEBUG_OUT("Warning: Empty data in interpolation");
		return result;
	}

	// 数据清理和验证合并
	QVector<QPointF> validSource;
	const double MAX_REASONABLE_VALUE = 1e12;

	for(int i = 0; i < source.size(); ++i) {
		const QPointF& point = source[i];

		// 检查数值有效性
		if(!isFiniteNumber(point.x()) || !isFiniteNumber(point.y())) {
			DEBUG_OUT(QString("Skipping invalid data point at index %1: X=%2, Y=%3")
				.arg(i).arg(point.x()).arg(point.y()));
			continue;
		}

		// 检查极大值 - 跳过而不是失败
		if(qAbs(point.y()) > MAX_REASONABLE_VALUE) {
			DEBUG_OUT(QString("Skipping extremely large Y value at index %1: %2")
				.arg(i).arg(point.y()));
			continue;
		}

		// 只保留有效的数据点
		validSource.append(point);
	}

	// 检查清理后的数据是否足够
	if(validSource.size() < 3) {
		DEBUG_OUT(QString("Insufficient valid data points after cleaning: %1")
			.arg(validSource.size()));
		return result; // 返回空结果，但不算失败
	}

	DEBUG_OUT(QString("Data cleaning: %1 -> %2 valid points")
		.arg(source.size()).arg(validSource.size()));

	// 对清理后的数据进行排序
	QVector<QPointF> sortedSource = validSource;

	for(int i = 0; i < sortedSource.size() - 1; ++i) {
		for(int j = 0; j < sortedSource.size() - 1 - i; ++j) {
			if(sortedSource[j].x() > sortedSource[j + 1].x()) {
				QPointF temp = sortedSource[j];
				sortedSource[j] = sortedSource[j + 1];
				sortedSource[j + 1] = temp;
			}
		}
	}

	double sourceMinX = sortedSource.first().x();
	double sourceMaxX = sortedSource.last().x();

	double targetMinX = targetX[0];
	double targetMaxX = targetX[0];

	for(int i = 1; i < targetX.size(); ++i) {
		if(targetX[i] < targetMinX) targetMinX = targetX[i];

		if(targetX[i] > targetMaxX) targetMaxX = targetX[i];
	}

	DEBUG_OUT(QString("Source X range: [%1, %2], Target X range: [%3, %4]")
		.arg(sourceMinX).arg(sourceMaxX).arg(targetMinX).arg(targetMaxX));

	// 安全插值算法
	for(int i = 0; i < targetX.size(); ++i) {
		double x = targetX[i];

		if(!isFiniteNumber(x)) continue;

		double y = 0.0;

		// 插值逻辑（保持原有逻辑，但使用sortedSource）
		if(x <= sourceMinX) {
			if(sortedSource.size() >= 2) {
				double dx = sortedSource[1].x() - sortedSource[0].x();

				if(qAbs(dx) > 1e-10) {
					double slope = (sortedSource[1].y() - sortedSource[0].y()) / dx;
					slope = qMax(-1e6, qMin(1e6, slope));
					y = sortedSource[0].y() + slope * (x - sortedSource[0].x());
				} else {
					y = sortedSource[0].y();
				}
			} else {
				y = sortedSource[0].y();
			}
		} else if(x >= sourceMaxX) {
			if(sortedSource.size() >= 2) {
				int lastIdx = sortedSource.size() - 1;
				double dx = sortedSource[lastIdx].x() - sortedSource[lastIdx - 1].x();

				if(qAbs(dx) > 1e-10) {
					double slope = (sortedSource[lastIdx].y() - sortedSource[lastIdx - 1].y()) / dx;
					slope = qMax(-1e6, qMin(1e6, slope));
					y = sortedSource[lastIdx].y() + slope * (x - sortedSource[lastIdx].x());
				} else {
					y = sortedSource[lastIdx].y();
				}
			} else {
				y = sortedSource.last().y();
			}
		} else {
			// 内插
			bool found = false;

			for(int j = 0; j < sortedSource.size() - 1; ++j) {
				if(x >= sortedSource[j].x() && x <= sortedSource[j + 1].x()) {
					double dx = sortedSource[j + 1].x() - sortedSource[j].x();

					if(qAbs(dx) > 1e-10) {
						double ratio = (x - sortedSource[j].x()) / dx;
						y = sortedSource[j].y() + ratio * (sortedSource[j + 1].y() - sortedSource[j].y());
					} else {
						y = sortedSource[j].y();
					}

					found = true;
					break;
				}
			}

			if(!found) {
				// 使用最近点
				double minDist = 1e10;

				for(int k = 0; k < sortedSource.size(); ++k) {
					double dist = qAbs(sortedSource[k].x() - x);

					if(dist < minDist) {
						minDist = dist;
						y = sortedSource[k].y();
					}
				}
			}
		}

		// 最终数值检查
		if(!isFiniteNumber(y)) {
			y = sortedSource.size() > 0 ? sortedSource[0].y() : 1.0;
		}

		y = qMax(-1e12, qMin(1e12, y));

		result.append(QPointF(x, y));
	}

	if(result.isEmpty()) {
		DEBUG_OUT("LogLog interpolation failed");
		return result;
	}

	DEBUG_OUT(QString("Interpolation completed: %1 -> %2 points")
		.arg(validSource.size()).arg(result.size()));

	return result;
}

double nmCalculationAutoFitGA::calculateLogLogCurveError(
	const QVector<QVector<double>>& target,
	const QVector<QVector<double>>& result) const
{
	// 验证数据
	if(!validateLogLogData(target) || !validateLogLogData(result)) {
		return 1e10;
	}

	try {
		// 数据对齐：找到X值的重叠区域
		double targetMinX = target[0][0];
		double targetMaxX = target[0][0];

		for(int i = 1; i < target[0].size(); ++i) {
			if(target[0][i] < targetMinX) targetMinX = target[0][i];

			if(target[0][i] > targetMaxX) targetMaxX = target[0][i];
		}

		double resultMinX = result[0][0];
		double resultMaxX = result[0][0];

		for(int i = 1; i < result[0].size(); ++i) {
			if(result[0][i] < resultMinX) resultMinX = result[0][i];

			if(result[0][i] > resultMaxX) resultMaxX = result[0][i];
		}

		double overlapMinX = qMax(targetMinX, resultMinX);
		double overlapMaxX = qMin(targetMaxX, resultMaxX);

		if(overlapMinX >= overlapMaxX) {
			DEBUG_OUT("No overlap between target and result LogLog curves");
			return 1e10;
		}

		// 生成公共X网格进行插值
		QVector<double> commonX;
		int numPoints = 50;

		if(overlapMinX > 0 && overlapMaxX > 0) {
			// 对数空间均匀分布
			double logMin = qLn(overlapMinX);
			double logMax = qLn(overlapMaxX);

			for(int i = 0; i < numPoints; ++i) {
				double logX = logMin + i * (logMax - logMin) / (numPoints - 1);
				double x = qExp(logX);

				// 数值保护
				if(!isFiniteNumber(x) || x <= 0) {
					continue;
				}

				commonX.append(x);
			}

			DEBUG_OUT("Using log-uniform grid for better early-time coverage");
		}

		if(commonX.isEmpty()) {
			DEBUG_OUT("Failed to generate common X grid");
			return 1e10;
		}

		// 插值目标曲线
		QVector<QPointF> targetCurve1, targetCurve2;

		for(int i = 0; i < target[0].size(); ++i) {
			// 检查数据有效性
			if(isFiniteNumber(target[0][i]) && isFiniteNumber(target[1][i]) &&
				isFiniteNumber(target[2][i])) {
					targetCurve1.append(QPointF(target[0][i], target[1][i]));
					targetCurve2.append(QPointF(target[0][i], target[2][i]));
			}
		}

		if(targetCurve1.isEmpty() || targetCurve2.isEmpty()) {
			DEBUG_OUT("Target curves are empty after filtering");
			return 1e10;
		}

		QVector<QPointF> alignedTarget1 = interpolateData(targetCurve1, commonX);
		QVector<QPointF> alignedTarget2 = interpolateData(targetCurve2, commonX);

		// 插值结果曲线
		QVector<QPointF> resultCurve1, resultCurve2;

		for(int i = 0; i < result[0].size(); ++i) {
			// 检查数据有效性
			if(isFiniteNumber(result[0][i]) && isFiniteNumber(result[1][i]) &&
				isFiniteNumber(result[2][i])) {
					resultCurve1.append(QPointF(result[0][i], result[1][i]));
					resultCurve2.append(QPointF(result[0][i], result[2][i]));
			}
		}

		if(resultCurve1.isEmpty() || resultCurve2.isEmpty()) {
			DEBUG_OUT("Result curves are empty after filtering");
			return 1e10;
		}

		QVector<QPointF> alignedResult1 = interpolateData(resultCurve1, commonX);
		QVector<QPointF> alignedResult2 = interpolateData(resultCurve2, commonX);

		// 检查插值结果
		if(alignedTarget1.isEmpty() || alignedTarget2.isEmpty() ||
			alignedResult1.isEmpty() || alignedResult2.isEmpty()) {
				DEBUG_OUT("LogLog interpolation failed");
				return 1e10;
		}

		if(alignedTarget1.size() != alignedResult1.size() ||
			alignedTarget2.size() != alignedResult2.size()) {
				DEBUG_OUT("LogLog interpolation size mismatch");
				return 1e10;
		}

		// 计算两条曲线的误差
		double error1 = calculateCurveError(alignedTarget1, alignedResult1);
		double error2 = calculateCurveError(alignedTarget2, alignedResult2);

		// 检查个别误差是否有效
		if(!isFiniteNumber(error1) || error1 > 1e9) {
			DEBUG_OUT(QString("Curve1 error is invalid: %1").arg(error1));
			error1 = 1e10;
		}

		if(!isFiniteNumber(error2) || error2 > 1e9) {
			DEBUG_OUT(QString("Curve2 error is invalid: %1").arg(error2));
			error2 = 1e10;
		}

		// 组合误差 - 添加保护
		double combinedError;

		if(error1 > 1e9 && error2 > 1e9) {
			combinedError = 1e10;
		} else if(error1 > 1e9) {
			combinedError = error2;
		} else if(error2 > 1e9) {
			combinedError = error1;
		} else {
			combinedError = 0.5 * error1 + 0.5 * error2;
		}

		DEBUG_OUT(QString("LogLog errors: Curve1=%1, Curve2=%2, Combined=%3")
			.arg(error1, 0, 'e', 4).arg(error2, 0, 'e', 4).arg(combinedError, 0, 'e', 4));

		return qMin(1e9, combinedError);

	} catch(const std::exception& e) {
		DEBUG_OUT(QString("Exception in LogLog error calculation: %1").arg(e.what()));
		return 1e10;
	} catch(...) {
		DEBUG_OUT("Unknown exception in LogLog error calculation");
		return 1e10;
	}
}

double nmCalculationAutoFitGA::calculateCurveError(
	const QVector<QPointF>& curve1, const QVector<QPointF>& curve2) const
{
	if(curve1.size() != curve2.size() || curve1.isEmpty()) {
		return 1e10;
	}

	// 预检查：确保没有无穷大值
	for(int i = 0; i < curve1.size(); ++i) {
		if(!isFiniteNumber(curve1[i].y()) || !isFiniteNumber(curve2[i].y())) {
			DEBUG_OUT(QString("Infinite value detected at index %1: Y1=%2, Y2=%3")
				.arg(i).arg(curve1[i].y()).arg(curve2[i].y()));
			return 1e10;
		}

		if(qAbs(curve1[i].y()) > 1e12 || qAbs(curve2[i].y()) > 1e12) {
			DEBUG_OUT(QString("Extremely large value detected at index %1")
				.arg(i));
			return 1e10;
		}
	}

	double totalError = 0.0;
	double totalWeight = 0.0;
	int validPoints = 0;

	for(int i = 0; i < curve1.size(); ++i) {
		double y1 = curve1[i].y();
		double y2 = curve2[i].y();

		// 跳过异常值
		if(!isFiniteNumber(y1) || !isFiniteNumber(y2)) {
			continue;
		}

		// 自适应权重：根据Y值大小调整，添加上限
		double weightFactor = qMin(100.0, qAbs(y1) * 0.01);
		double weight = 1.0 / (1.0 + weightFactor);

		// 相对误差和绝对误差的组合
		double yMax = qMax(qAbs(y1), qAbs(y2));
		yMax = qMax(1e-12, yMax); // 防止除零

		double relativeError = qAbs(y1 - y2) / yMax;
		double absoluteError = qAbs(y1 - y2);

		// 限制误差值
		relativeError = qMin(1e6, relativeError);
		absoluteError = qMin(1e6, absoluteError);

		// 误差组合：相对误差为主，绝对误差为辅
		double pointError = 0.7 * relativeError + 0.3 * absoluteError;

		if(isFiniteNumber(pointError) && pointError < 1e10) {
			totalError += weight * pointError * pointError;
			totalWeight += weight;
			validPoints++;
		}
	}

	if(totalWeight > 0 && validPoints > 0) {
		double result = sqrt(totalError / totalWeight);

		// 最终检查
		if(!isFiniteNumber(result)) {
			DEBUG_OUT("Final error calculation produced infinite result");
			return 1e10;
		}

		return qMin(1e9, result); // 限制最大误差值
	} else {
		DEBUG_OUT(QString("No valid points for error calculation: validPoints=%1")
			.arg(validPoints));
		return 1e10;
	}
}
// ==================== 工具方法 ====================

double nmCalculationAutoFitGA::random01() const
{
	return static_cast<double>(qrand()) / RAND_MAX;
}

double nmCalculationAutoFitGA::gaussianRandom(double mean, double stddev) const
{
	static bool hasSpare = false;
	static double spare;

	if(hasSpare) {
		hasSpare = false;
		return spare * stddev + mean;
	}

	hasSpare = true;
	double u = qMax(random01(), 1e-12);
	double v = random01();
	double mag = stddev * sqrt(-2.0 * log(u));
	spare = mag * cos(2.0 * 3.14159265359 * v);

	return mag * sin(2.0 * 3.14159265359 * v) + mean;
}

int nmCalculationAutoFitGA::getEnabledParameterCount() const
{
	int count = 0;

	for(int i = 0; i < m_parameterSelected.size(); ++i) {
		if(m_parameterSelected[i]) count++;
	}

	return count;
}

void nmCalculationAutoFitGA::saveOptimizationResult()
{
	DEBUG_OUT(QString("GA optimization result: fitness=%1, evaluations=%2/%3")
		.arg(m_bestFitness, 0, 'e', 4)
		.arg(m_successfulEvaluations)
		.arg(m_totalEvaluations));
}