1 简介
2 部分代码
clear all clc close all d=5; % dimension options.lk=-32*ones(1,d); % lower bound options.uk=32*ones(1,d); % upper bound options.m=50; % Size of the population options.MAXITER=500; % Maximum number of iterations options.n=length(options.uk); % dimension of the problem. options.ObjFunction=@Ackley; % the name of the objective function options.Display_Flag=1; % Flag for displaying results over iterations options.run_parallel_index=0; options.run=10; if options.run_parallel_index % run_parallel stream = RandStream('mrg32k3a'); parfor index=1:options.run % tic % index set(stream,'Substream',index); RandStream.setGlobalStream(stream) [bestX, bestFitness, bestFitnessEvolution,nEval]=BH_v1(options); bestX_M(index,:)=bestX; Fbest_M(index)=bestFitness; fbest_evolution_M(index,:)=bestFitnessEvolution; end else rng('default') for index=1:options.run [bestX, bestFitness, bestFitnessEvolution,nEval]=BH_v1(options); bestX_M(index,:)=bestX; Fbest_M(index)=bestFitness; fbest_evolution_M(index,:)=bestFitnessEvolution; end end [a,b]=min(Fbest_M); figure plot(1:options.MAXITER,fbest_evolution_M(b,:)) xlabel('Iterations') ylabel('Fitness') img =gcf; %获取当前画图的句柄 print(img, '-dpng', '-r600', './img.png') %即可得到对应格式和期望dpi的图像 fprintf(' MIN=%g MEAN=%g MEDIAN=%g MAX=%g SD=%g \n',... min(Fbest_M),mean(Fbest_M),median(Fbest_M),max(Fbest_M),std(Fbest_M))
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3 仿真结果
4 参考文献
[1]王通, 高宪文, and 蒋子健. "基于黑洞算法的LSSVM的参数优化." 东北大学学报:自然科学版 35.2(2014):5.