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xiaochoujiao新蟲(chóng) (小有名氣)
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[求助]
ACO測(cè)試2維的Rosenbrock函數(shù) 和Step函數(shù)
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本人剛學(xué)Matlab，ACO測(cè)試2維的Rosenbrock函數(shù) 和Step函數(shù)，但是都在同一個(gè)位置卡住了，請(qǐng)看看，然后提供正確的代碼可以嗎? % Rosenbrock函數(shù)定義 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 % 蟻群優(yōu)化算法主函數(shù) function [minVal, maxVal, meanVal, stdVal] = aco_rosenbrock(dim) % 蟻群算法參數(shù)設(shè)置 numAnts = 50; % 螞蟻數(shù)量 numIterations = 100; % 迭代次數(shù) evaporationRate = 0.5; % 信息素蒸發(fā)率 alpha = 1; % 信息素重要程度因子 beta = 2; % 啟發(fā)式信息重要程度因子 q0 = 0.9; % 選擇概率參數(shù) % 初始化信息素矩陣 pheromoneMatrix = ones(numAnts, dim); % 用于存儲(chǔ)每次迭代的最優(yōu)解 bestSolutions = zeros(numIterations, 1); % 多次迭代 for iteration = 1:100 % 螞蟻路徑構(gòu)建 antSolutions = zeros(numAnts, dim); for ant = 1:numAnts for d = 1:dim % 計(jì)算選擇概率 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); % 根據(jù)概率選擇下一個(gè)位置 if rand < q0 [~, selectedIndex] = max(probabilities); else selectedIndex = rouletteWheelSelection(probabilities); end antSolutions(ant, d) = randn; end end % 計(jì)算每只螞蟻的目標(biāo)函數(shù)值 antValues = zeros(numAnts, 1); for ant = 1:numAnts antValues(ant) = rosenbrock(antSolutions(ant,  );end % 更新全局最優(yōu)解 [globalBestValue, globalBestIndex] = min(antValues); bestSolutions(iteration) = globalBestValue; % 更新信息素矩陣 pheromoneMatrix = (1 - evaporationRate) * pheromoneMatrix; for ant = 1:numAnts for d = 1:dim pheromoneMatrix(ant, d) = pheromoneMatrix(ant, d) + 1 / antValues(ant); end end end % 計(jì)算最小值、最大值、平均值和標(biāo)準(zhǔn)差 minVal = min(bestSolutions); maxVal = max(bestSolutions); meanVal = mean(bestSolutions); stdVal = std(bestSolutions); end % 輪盤(pán)賭選擇函數(shù) function selectedIndex = rouletteWheelSelection(probabilities) cumulativeProbabilities = cumsum(probabilities); randomNumber = rand; for i = 1:length(probabilities) if randomNumber <= cumulativeProbabilities(i) selectedIndex = i; break; end end end % 調(diào)用函數(shù)并輸出綜合結(jié)果 dim = 2; % 這里設(shè)置維度，可以根據(jù)需要修改為其他值 [minVal, maxVal, meanVal, stdVal] = aco_rosenbrock(dim); fprintf('維度為 %d時(shí)：\n', dim); fprintf('最小值: %.4f\n', minVal); fprintf('最大值: %.4f\n', maxVal); fprintf('平均值: %.4f\n', meanVal); fprintf('標(biāo)準(zhǔn)差: %.4f\n', stdVal); % Step函數(shù)定義 function y = stepFunction(x) y = sum(floor(x + 0.5).^2); end % 蟻群優(yōu)化算法主函數(shù) function [minVal, maxVal, meanVal, stdVal] = aco_step(dim) % 蟻群算法參數(shù)設(shè)置 numAnts = 50; % 螞蟻數(shù)量 numIterations = 100; % 迭代次數(shù) evaporationRate = 0.5; % 信息素蒸發(fā)率 alpha = 1; % 信息素重要程度因子 beta = 2; % 啟發(fā)式信息重要程度因子 q0 = 0.9; % 選擇概率參數(shù) % 初始化信息素矩陣 pheromoneMatrix = ones(numAnts, dim); % 用于存儲(chǔ)每次迭代的最優(yōu)解 bestSolutions = zeros(numIterations, 1); % 多次迭代 for iteration = 1:numIterations % 螞蟻路徑構(gòu)建 antSolutions = zeros(numAnts, dim); for ant = 1:numAnts for d = 1:dim % 計(jì)算選擇概率 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); % 根據(jù)概率選擇下一個(gè)位置 if rand < q0 [~, selectedIndex] = max(probabilities); else selectedIndex = rouletteWheelSelection(probabilities); end antSolutions(ant, d) = randn; end end % 計(jì)算每只螞蟻的目標(biāo)函數(shù)值 antValues = zeros(numAnts, 1); for ant = 1:numAnts antValues(ant) = stepFunction(antSolutions(ant, );end % 更新全局最優(yōu)解 [globalBestValue, globalBestIndex] = min(antValues); bestSolutions(iteration) = globalBestValue; % 更新信息素矩陣 pheromoneMatrix = (1 - evaporationRate) * pheromoneMatrix; for ant = 1:numAnts for d = 1:dim pheromoneMatrix(ant, d) = pheromoneMatrix(ant, d) + 1 / antValues(ant); end end end % 計(jì)算最小值、最大值、平均值和標(biāo)準(zhǔn)差 minVal = min(bestSolutions); maxVal = max(bestSolutions); meanVal = mean(bestSolutions); stdVal = std(bestSolutions); end % 輪盤(pán)賭選擇函數(shù) function selectedIndex = rouletteWheelSelection(probabilities) cumulativeProbabilities = cumsum(probabilities); randomNumber = rand; for i = 1:length(probabilities) if randomNumber <= cumulativeProbabilities(i) selectedIndex = i; break; end end end % 調(diào)用函數(shù)并輸出綜合結(jié)果 dim = 2; % 這里設(shè)置維度，可以根據(jù)需要修改為其他值 [minVal, maxVal, meanVal, stdVal] = aco_step(dim); fprintf('維度為 %d時(shí)：\n', dim); fprintf('最小值: %.4f\n', minVal); fprintf('最大值: %.4f\n', maxVal); fprintf('平均值: %.4f\n', meanVal); fprintf('標(biāo)準(zhǔn)差: %.4f\n', stdVal); |
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