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xiaochoujiao新虫 (小有名气)
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[求助]
ACO测试2维的Rosenbrock函数 和Step函数
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本人刚学Matlab，ACO测试2维的Rosenbrock函数 和Step函数，但是都在同一个位置卡住了，请看看，然后提供正确的代码可以吗? % Rosenbrock函数定义 function y = rosenbrock(x) y = 0; for i = 1:length(x) - 1 y = y + 100 * (x(i + 1) - x(i)^2)^2 + (x(i) - 1)^2; end end % 蚁群优化算法主函数 function [minVal, maxVal, meanVal, stdVal] = aco_rosenbrock(dim) % 蚁群算法参数设置 numAnts = 50; % 蚂蚁数量 numIterations = 100; % 迭代次数 evaporationRate = 0.5; % 信息素蒸发率 alpha = 1; % 信息素重要程度因子 beta = 2; % 启发式信息重要程度因子 q0 = 0.9; % 选择概率参数 % 初始化信息素矩阵 pheromoneMatrix = ones(numAnts, dim); % 用于存储每次迭代的最优解 bestSolutions = zeros(numIterations, 1); % 多次迭代 for iteration = 1:100 % 蚂蚁路径构建 antSolutions = zeros(numAnts, dim); for ant = 1:numAnts for d = 1:dim % 计算选择概率 probabilities = zeros(numAnts, 1); for i = 1:numAnts probabilities(i) = (pheromoneMatrix(i, d)^alpha) * (1 / rosenbrock([antSolutions(i, 1:d-1), randn]))^beta; end probabilities = probabilities / sum(probabilities); % 根据概率选择下一个位置 if rand < q0 [~, selectedIndex] = max(probabilities); else selectedIndex = rouletteWheelSelection(probabilities); end antSolutions(ant, d) = randn; end end % 计算每只蚂蚁的目标函数值 antValues = zeros(numAnts, 1); for ant = 1:numAnts antValues(ant) = rosenbrock(antSolutions(ant,  );end % 更新全局最优解 [globalBestValue, globalBestIndex] = min(antValues); bestSolutions(iteration) = globalBestValue; % 更新信息素矩阵 pheromoneMatrix = (1 - evaporationRate) * pheromoneMatrix; for ant = 1:numAnts for d = 1:dim pheromoneMatrix(ant, d) = pheromoneMatrix(ant, d) + 1 / antValues(ant); end end end % 计算最小值、最大值、平均值和标准差 minVal = min(bestSolutions); maxVal = max(bestSolutions); meanVal = mean(bestSolutions); stdVal = std(bestSolutions); end % 轮盘赌选择函数 function selectedIndex = rouletteWheelSelection(probabilities) cumulativeProbabilities = cumsum(probabilities); randomNumber = rand; for i = 1:length(probabilities) if randomNumber <= cumulativeProbabilities(i) selectedIndex = i; break; end end end % 调用函数并输出综合结果 dim = 2; % 这里设置维度，可以根据需要修改为其他值 [minVal, maxVal, meanVal, stdVal] = aco_rosenbrock(dim); fprintf('维度为 %d时：\n', dim); fprintf('最小值: %.4f\n', minVal); fprintf('最大值: %.4f\n', maxVal); fprintf('平均值: %.4f\n', meanVal); fprintf('标准差: %.4f\n', stdVal); % Step函数定义 function y = stepFunction(x) y = sum(floor(x + 0.5).^2); end % 蚁群优化算法主函数 function [minVal, maxVal, meanVal, stdVal] = aco_step(dim) % 蚁群算法参数设置 numAnts = 50; % 蚂蚁数量 numIterations = 100; % 迭代次数 evaporationRate = 0.5; % 信息素蒸发率 alpha = 1; % 信息素重要程度因子 beta = 2; % 启发式信息重要程度因子 q0 = 0.9; % 选择概率参数 % 初始化信息素矩阵 pheromoneMatrix = ones(numAnts, dim); % 用于存储每次迭代的最优解 bestSolutions = zeros(numIterations, 1); % 多次迭代 for iteration = 1:numIterations % 蚂蚁路径构建 antSolutions = zeros(numAnts, dim); for ant = 1:numAnts for d = 1:dim % 计算选择概率 probabilities = zeros(numAnts, 1); for i = 1:numAnts probabilities(i) = (pheromoneMatrix(i, d)^alpha) * (1 / stepFunction([antSolutions(i, 1:d-1), randn]))^beta; end probabilities = probabilities / sum(probabilities); % 根据概率选择下一个位置 if rand < q0 [~, selectedIndex] = max(probabilities); else selectedIndex = rouletteWheelSelection(probabilities); end antSolutions(ant, d) = randn; end end % 计算每只蚂蚁的目标函数值 antValues = zeros(numAnts, 1); for ant = 1:numAnts antValues(ant) = stepFunction(antSolutions(ant, );end % 更新全局最优解 [globalBestValue, globalBestIndex] = min(antValues); bestSolutions(iteration) = globalBestValue; % 更新信息素矩阵 pheromoneMatrix = (1 - evaporationRate) * pheromoneMatrix; for ant = 1:numAnts for d = 1:dim pheromoneMatrix(ant, d) = pheromoneMatrix(ant, d) + 1 / antValues(ant); end end end % 计算最小值、最大值、平均值和标准差 minVal = min(bestSolutions); maxVal = max(bestSolutions); meanVal = mean(bestSolutions); stdVal = std(bestSolutions); end % 轮盘赌选择函数 function selectedIndex = rouletteWheelSelection(probabilities) cumulativeProbabilities = cumsum(probabilities); randomNumber = rand; for i = 1:length(probabilities) if randomNumber <= cumulativeProbabilities(i) selectedIndex = i; break; end end end % 调用函数并输出综合结果 dim = 2; % 这里设置维度，可以根据需要修改为其他值 [minVal, maxVal, meanVal, stdVal] = aco_step(dim); fprintf('维度为 %d时：\n', dim); fprintf('最小值: %.4f\n', minVal); fprintf('最大值: %.4f\n', maxVal); fprintf('平均值: %.4f\n', meanVal); fprintf('标准差: %.4f\n', stdVal); |
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