matlab-基于GA优化算法的三维曲面最大值搜索matlab仿真-源码

clc; clear; close all; warning off; addpath(genpath(pwd)); populationSize = 30; numberOfGenes = 40; crossoverProbability = 0.8; mutationProbability = 0.025; tournamentSelectionParameter = 0.75; variableRange = 3.0; numberOfGenerations = 100; fitness = zeros(populationSize, 1); fitnessFigureHandle = figure; hold on; set(fitnessFigureHandle, 'DoubleBuffer', 'on'); axis([1 numberOfGenerations 2.5 3]); bestPlotHandle = plot(1:numberOfGenerations, zeros(1, numberOfGenerations)); textHandle = text(30, 2.6, sprintf('best: %4.3f', 0.0)); hold off; drawnow; surfaceFigureHandle = figure; hold on; set(surfaceFigureHandle, 'DoubleBuffer', 'on'); delta = 0.1; limit = fix(2 * variableRange / delta) + 1; [xValues, yValues] = meshgrid(-variableRange:delta:variableRange, -variableRange:delta:variableRange); zValues = zeros(limit, limit); for j = 1:limit for k = 1:limit zValues(j, k) = EvaluateIndividual([xValues(j, k) yValues(j, k)]); end end surfl(xValues, yValues, zValues); colormap gray; shading interp; view([-7 -9 10]); decodedPopulation = zeros(populationSize, 2); populationPlotHandle = plot3(decodedPopulation(:, 1), decodedPopulation(:, 2), fitness(:), 'kp'); hold off; drawnow; population = InitializePopulation(populationSize, numberOfGenes); for iGeneration = 1:numberOfGenerations maximumFitness = 0.0; xBest = zeros(1, 2); bestInidivualIndex = 0; for i = 1:populationSize chromosome = population(i, :); x = DecodeChromosome(chromosome, variableRange); decodedPopulation(i, :) = x; fitness(i) = EvaluateIndividual(x); if fitness(i) > maximumFitness maximumFitness = fitness(i); bestInidivualIndex = i; xBest = x; end end disp('xBest'); disp(xBest); disp('maximumFitness'); disp(maximumFitness); tempPopulation = population; for i = 1:2:populationSize i1 = TournamentSelection(fitness, tournamentSelectionParameter); i2 = TournamentSelection(fitness, tournamentSelectionParameter); chromosome1 = population(i1, :); chromosome2 = population(i2, :); r = rand; if r < crossoverProbability newChromosomePair = Cross(chromosome1, chromosome2); tempPopulation(i, :) = newChromosomePair(1, :); tempPopulation(i + 1, :) = newChromosomePair(2, :); else tempPopulation(i, :) = chromosome1; tempPopulation(i + 1, :) = chromosome2; end end for i = 1:populationSize originalChromosome = tempPopulation(i, :); mutatedChromosome = Mutate(originalChromosome, mutationProbability); tempPopulation(i, :) = mutatedChromosome; end tempPopulation(1, :) = population(bestInidivualIndex, :); population = tempPopulation; plotvector = get(bestPlotHandle, 'YData'); plotvector(iGeneration) = maximumFitness; set(bestPlotHandle, 'YData', plotvector); set(textHandle, 'String', sprintf('best: %4.3f', maximumFitness)); set(populationPlotHandle, 'XData', decodedPopulation(:, 1), 'YData', decodedPopulation(:, 2), 'ZData', fitness(:)); drawnow; end disp('xBest'); disp(xBest); disp('maximumFitness'); disp(maximumFitness);