【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

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
%
clearvars
close all
clc
disp('The AOSMA is tracking the problem');
N=30; % Number of slime mould
Function_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 AOSMA
display(['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 space
subplot(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 space
subplot(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 tight
grid on
box 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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