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⛄ 内容介绍
该论文提出了一种称为成功历史智能优化器 (SHIO) 的新型随机群智能算法。它通过基于搜索空间中找到的三个最佳解决方案提出新的探索和开发运动策略来创建新的运动向量，从而为单目标优化问题提供解决方案，其中每个最佳解决方案都存储在内存中并从到目前为止在优化过程中找到的三个最佳解决方案的平均值。拟议的 SHIO 确保了搜索空间探索和使用的效率。为了确认 SHIO 性能，测试了几种性能测量（搜索历史、轨迹和收敛曲线），并使用 SHIO 来解决 (23) 单目标优化基准函数。这些功能已被分类为单峰，多式联运和固定式多式联运。使用了平均值、标准差、最小值和最大值等各种指标，并记录了定量结果。此外，定性结果的轨迹和搜索历史可视化。测试函数和性能指标的结果表明，所提出的算法可以探索各种搜索区域位置，在优化的同时利用潜在的搜索空间位置，避免局部乐观并有效地收敛到全局最优。与比较算法相比，SHIO 在评估的单峰和多峰基准测试中提供了极具竞争力和卓越的结果。请注意，SHIO 算法源代码可在 并记录了定量结果。此外，定性结果的轨迹和搜索历史可视化。测试函数和性能指标的结果表明，所提出的算法可以探索各种搜索区域位置，在优化的同时利用潜在的搜索空间位置，避免局部乐观并有效地收敛到全局最优。与比较算法相比，SHIO 在评估的单峰和多峰基准测试中提供了极具竞争力和卓越的结果。请注意，SHIO 算法源代码可在 并记录了定量结果。此外，定性结果的轨迹和搜索历史可视化。测试函数和性能指标的结果表明，所提出的算法可以探索各种搜索区域位置，在优化的同时利用潜在的搜索空间位置，避免局部乐观并有效地收敛到全局最优。与比较算法相比，SHIO 在评估的单峰和多峰基准测试中提供了极具竞争力和卓越的结果。请注意，SHIO 算法源代码可在 在优化的同时利用潜在的搜索空间位置，避免局部乐观，高效收敛到全局最优。与比较算法相比，SHIO 在评估的单峰和多峰基准测试中提供了极具竞争力和卓越的结果。请注意，SHIO 算法源代码可在 在优化的同时利用潜在的搜索空间位置，避免局部乐观，高效收敛到全局最优。与比较算法相比，SHIO 在评估的单峰和多峰基准测试中提供了极具竞争力和卓越的结果。
⛄ 部分代码
% Success history intelligent optimizer (SHIO) optmization Code
% code for paper: Fakhouri, H. N., Hamad, F., & Alawamrah, A. (2022). Success history intelligent optimizer. The Journal of Supercomputing, 78(5), 6461-6502.
%please give citation of the above paper
% benchmarkfunction = @YourCostFunction
% dimensionension = number of your variables
% part of this code is referenced to : https://www.mathworks.com/matlabcentral/fileexchange/44974-grey-wolf-optimizer-gwo
% we have modified the open source code of GWO equations and code to make SHIO code
% %
% reference of code and credit to : S. Mirjalili, S. M. Mirjalili, A. Lewis %
% Grey Wolf Optimizer, Advances in Engineering %
% Software , in press, %
clear all
clc
visFlag = 0;
SHIO_Particles_number=50; % Number of search agents
Maximum_numbef_of_iterations=100;
% Load details of the selected benchmark function
number_of_runs=3;
for i= [ 1 :1 : 5]
% for 23 function use i= [ 1 :1 : 23]
if i== 1
Function_name = 'F1';
display('The best optimal value of the objective funciton found F1 : ');
end
if i== 2
Function_name = 'F2';
display('The best optimal value of the objective funciton found F2 : ');
end
if i== 3
Function_name = 'F3';
display('The best optimal value of the objective funciton found F3 : ');
end
if i== 4
Function_name = 'F4';
display('The best optimal value of the objective funciton found F4 : ');
end
if i== 5
Function_name = 'F5';
display('The best optimal value of the objective funciton found F5 : ');
end
if i== 6
Function_name = 'F6';
display('The best optimal value of the objective funciton found F6 : ');
end
if i== 7
Function_name = 'F7';
display('The best optimal value of the objective funciton found F7 : ');
end
if i== 8
Function_name = 'F8';
display('The best optimal value of the objective funciton found F8 : ');
end
if i== 9
Function_name = 'F9';
display('The best optimal value of the objective funciton found F9 : ');
end
if i== 10
Function_name = 'F10';
display('The best optimal value of the objective funciton found F10 : ');
end
if i== 11
Function_name = 'F11';
display('The best optimal value of the objective funciton found F11 : ');
end
if i== 12
Function_name = 'F12';
display('The best optimal value of the objective funciton found F12 : ');
end
if i== 13
Function_name = 'F13';
display('The best optimal value of the objective funciton found F13 : ');
end
if i== 14
Function_name = 'F14';
display('The best optimal value of the objective funciton found F14 : ');
end
if i== 15
Function_name = 'F15';
display('The best optimal value of the objective funciton found F15 : ');
end
if i== 16
Function_name = 'F16';
display('The best optimal value of the objective funciton found F16 : ');
end
if i== 17
Function_name = 'F17';
display('The best optimal value of the objective funciton found F17 : ');
end
if i== 18
Function_name = 'F18';
display('The best optimal value of the objective funciton found F18 : ');
end
if i== 19
Function_name = 'F19';
display('The best optimal value of the objective funciton found F19 : ');
end
if i== 20
Function_name = 'F20';
display('The best optimal value of the objective funciton found F20 : ');
end
if i== 21
Function_name = 'F21';
display('The best optimal value of the objective funciton found F21 : ');
end
if i== 22
Function_name = 'F22';
display('The best optimal value of the objective funciton found F22 : ');
end
if i== 23
Function_name = 'F23';
display('The best optimal value of the objective funciton found F23 : ');
end
bestsolutionsofSHIO=zeros(1,number_of_runs);
%bestsolutionsofPSO=zeros(1,number_of_runs);
[lowerbound,upperbound,dimension,benchmarkfunction]=Get_Functions_details(Function_name);
for k= [ 1 :1 : number_of_runs]
[SHIO_best_solution_value,SHIO_best_particle_position,SHIO_convergence_curve, Trajectories,fitness_history, position_history]=SHIOoptmizer(SHIO_Particles_number,Maximum_numbef_of_iterations,lowerbound,upperbound,dimension,benchmarkfunction);
bestsolutionsofSHIO(k)=SHIO_best_solution_value;
disp(['run number', num2str(k)]);
disp(['is', num2str(SHIO_best_solution_value)]);
%[gBestScore, PSO_cg_curve]=PSO(SHIO_Particles_number,Maximum_numbef_of_iterations,lowerbound,upperbound,dimension,benchmarkfunction); % run PSO to compare to results
end
disp(['the avarage for SHIO', num2str(k)]);
mm=mean(bestsolutionsofSHIO);
disp(['the mean OF SHIO is ',num2str(mm)]);
%mm2=mean(bestsolutionsofPSO);
%disp(['the mean OF PSO is ',num2str(mm2)]);
MINSSHIO=min(bestsolutionsofSHIO);
disp(['the min OF SHIO is ',num2str(MINSSHIO)]);
MAXSSHIO=max(bestsolutionsofSHIO);
disp(['the max OF SHIO is ',num2str(MAXSSHIO)]);
disp(['the std for ', num2str(k)]);
stdSHIO=std(bestsolutionsofSHIO);
disp(['the std OF SHIO is ',num2str(stdSHIO)]);
end
% %***********************************************************
%draw last function values
% %***********************************************************
%draw curve compare with Pso
figure('Position',[500 500 660 290])
%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(SHIO_convergence_curve,'Color','r')
hold on
%semilogy(PSO_cg_curve,'Color','b')
%title('Objective space')
xlabel('Iteration');
ylabel('Best score obtained so far');
axis tight
box on
%******************************************************** draw all shapes togather
%please wait it take time
figure('Position',[454 445 894 297])
%Draw search space
subplot(1,5,1);
func_plot(Function_name);
title('Parameter space')
xlabel('x_1');
ylabel('x_2');
zlabel([Function_name,'( x_1 , x_2 )'])
box on
axis tight
subplot(1,5,2);
hold on
for k1 = 1: size(position_history,1)
for k2 = 1: size(position_history,2)
plot(position_history(k1,k2,1),position_history(k1,k2,2),'.','markersize',1,'MarkerEdgeColor','k','markerfacecolor','k');
end
end
plot(SHIO_best_particle_position(1),SHIO_best_particle_position(2),'.','markersize',10,'MarkerEdgeColor','r','markerfacecolor','r');
title('Search history (x1 and x2 only)')
xlabel('x1')
ylabel('x2')
box on
axis tight
subplot(1,5,3);
hold on
plot(Trajectories(1,:));
title('Trajectory of 1st Particle')
xlabel('Iteration#')
box on
axis tight
subplot(1,5,4);
hold on
semilogy(mean(fitness_history),'Color','g', 'LineWidth',2);
title('Average fitness of all Particles')
xlabel('Iteration#')
box on
axis tight
%Draw objective space
subplot(1,5,5);
semilogy(SHIO_convergence_curve,'Color','r')
title('Convergence curve')
xlabel('Iteration#');
ylabel('Best score obtained so far');
box on
axis tight
set(gcf, 'position' , [39 479 1727 267]);
%******************************************************** draw all shapes togather
⛄ 运行结果
⛄ 参考文献
Fakhouri, H. N., Hamad, F., & Alawamrah, A. (2022). Success history intelligent optimizer. The Journal of Supercomputing, 78(5), 6461-6502.
