MATLAB神经网络和优化算法：19 遗传算法，模式搜索，模拟退火求局部最优问题.zip

% 用遗传算法求 20+x^2+y^2-10*(cos(2*pi*x)+cos(2*pi*y) 的最小值 function my_ga_test1() figure ezsurf(@(x,y)20+x^2+y^2-10*(cos(2*pi*x)+cos(2*pi*y)),[-5 5],[-5 5]) text(0,0,85,'\downarrowglobelmin(0,0)','EdgeColor','red','FontSize',18,'BackgroundColor',[.7 .9 .7]); figure ezcontour(@(x,y)20+x^2+y^2-10*(cos(2*pi*x)+cos(2*pi*y)),[-1 1],[-1 1]) text(0,0,'\downarrowglobelmin(0,0)','FontSize',18); disp('please press any key to continue.....') pause() FitnessFunction = @simple_fitness; numberOfVariables = 2; opt= gaoptimset('PlotFcns',{@gaplotbestf,@gaplotmaxconstr},'Display','iter',... 'PopulationSize',50, 'Generations',100,'StallGenLimit', 50,'StallTimeLimit',50,'CrossoverFraction',0.8,'MutationFcn',@mutationadaptfeasible); [x_ga,fval_ga] = ga(FitnessFunction,numberOfVariables,[],[],[],[],[],[],[],opt) x0=rand(1,2); [x1 f1]=fminsearch(FitnessFunction,x0) [x2 f2]=fminunc(FitnessFunction,x0) [x_ps,fval_ps] = patternsearch(FitnessFunction,x0) [x_sa,fval_sa]= simulannealbnd(FitnessFunction,x0) function y = simple_fitness(x) y = 20+x(1)^2+x(2)^2-10*(cos(2*pi*x(1))+cos(2*pi*x(2)));