【黏菌优化算法】基于自适应对抗粘菌优化算法求解单目标优化我问他 (AOSMA)含Matlab源码

1 简介

黏菌优化算法（Slime mould algorithm，SMA）由 Li等于 2020 年提出，其灵感来自于黏菌的扩散和觅食行为，属于元启发算法。具有收敛速度快，寻优能力强的特点。黏菌优化算法用数学模型模仿黏菌觅食行为和形态变化， SMA 包括三个阶段，分别为接近食物阶段、包围食物阶段和抓取食物阶段。​

2 部分代码

% Adaptive Opposition Slime Mould Algorithm (AOSMA) source Code Version 1.0%clearvarsclose allclcdisp('The AOSMA is tracking the problem');N=30; % Number of slime mouldFunction_name='F1'; % Name of the test function that can be from F1 to F23 MaxIT=500; % Maximum number of iterations​[lb,ub,dim,fobj]=Get_Functions_details(Function_name); % Function details​Times=21; %Number of independent times you want to run the AOSMAdisplay(['Number of independent runs: ', num2str(Times)]);​for i=1:Times[Destination_fitness(i),bestPositions(i,:),Convergence_curve(i,:)]=AOSMA(N,MaxIT,lb,ub,dim,fobj);display(['The optimal fitness of AOSMA is: ', num2str(Destination_fitness(i))]);end​[bestfitness,index]=min(Destination_fitness);disp('--------Best Fitness, Average Fitness, Standard Deviation and Best Solution--------');display(['The best fitness of AOSMA is: ', num2str(bestfitness)]);display(['The average fitness of AOSMA is: ', num2str(mean(Destination_fitness))]);display(['The standard deviation fitness of AOSMA is: ', num2str(std(Destination_fitness))]);display(['The best location of AOSMA is: ', num2str(bestPositions(index,:))]);figure('Position',[269   240   660   290])%Draw search spacesubplot(1,2,1);func_plot(Function_name);title('Parameter space')xlabel('x_1');ylabel('x_2');zlabel([Function_name,'( x_1 , x_2 )'])​%Draw objective spacesubplot(1,2,2);semilogy(Convergence_curve(index,:),'Color','r','linewidth',2.5);title('Objective space')xlabel('Iteration');ylabel('Best score obtained so far');legend('AOSMA');axis tightgrid onbox on​

3 仿真结果

4 参考文献

[1]郭雨鑫,刘升,张磊,黄倩.精英反向与二次插值改进的黏菌算法[J/OL].计算机应用研究:1-7

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