1 简介
2 部分代码
%% This function implements the basic School Based Optimization (SBO) algorithm for 10-bar truss optimization%%
clc
clear all
close all
global D
% Specity SBO parameters
Itmax=300; % Maximum number of iterations
NClass=5; % Number of classes in the school
PopSize=15; % Population size of each class
% Optimization problem parameters
D=Data10; % For truss function evaluate the functio to get the initial parameters
LB=D.LB; % Lowerbound
UB=D.UB; % Upperbound
FN='ST10'; % Name of analyzer function%% Randomely generate initial designs between LB and UB
Cycle=1;
for I=1:PopSizefor NC=1:NClassDesigns{NC}(I,:)=LB+rand(1,size(LB,2)).*(UB-LB); % Row vectorend
end% Analysis the designs
for NC=1:NClass[PObj{NC},Obj{NC}]=Analyser(Designs{NC},FN);Best{NC}=[];
end%% SBO loop
for Cycle=2:Itmaxfor NC=1:NClass% Identify best designs and keep them[Best{NC},Designs{NC},PObj{NC},Obj{NC},WMeanPos{NC}]=Specifier(PObj{NC},Obj{NC},Designs{NC},Best{NC});TeachersPObj(NC,1)=Best{NC}.GBest.PObj;TeachersDes(NC,:)=Best{NC}.GBest.Design;endfor NC=1:NClass% Select a teacherSelectedTeacher=TeacherSelector(Best,NC,TeachersPObj);% Apply Teaching[Designs{NC},PObj{NC},Obj{NC}]=Teaching(LB,UB,Designs{NC},PObj{NC},Obj{NC},TeachersDes(SelectedTeacher,:),WMeanPos{NC},FN);[Best{NC},Designs{NC},PObj{NC},Obj{NC},WMeanPos{NC}]=Specifier(PObj{NC},Obj{NC},Designs{NC},Best{NC});% Apply Learning[Designs{NC},PObj{NC},Obj{NC}]=Learning(LB,UB,Designs{NC},Obj{NC},PObj{NC},FN);[Best{NC},Designs{NC},PObj{NC},Obj{NC},WMeanPos{NC}]=Specifier(PObj{NC},Obj{NC},Designs{NC},Best{NC});end% Find best so far solution and MeanCumPObj=[];for NC=1:NClassClassBestPObj(NC,1)=Best{NC}.GBest.PObj;ClassMean(NC,1)=mean(PObj{NC});CumPObj=[CumPObj;PObj{NC}];end[~,b]=min(ClassBestPObj);OveralBestPObj=Best{b}.GBest.PObj;OveralBestObj=Best{b}.GBest.Obj;OveralBestDes=Best{b}.GBest.Design;% Plot time history of the best solution vs. iteration and print the% resultshold on;plot(Cycle,Best{b}.GBest.PObj,'b*');xlabel('Iteration');ylabel('Best solution value');pause(0.0001)fprintf('Cycle: %6d, Best (Penalized): %6.4f, Objective: %6.4f\n',Cycle,OveralBestPObj,OveralBestObj);
endSolution.PObj=OveralBestPObj;% Objective value for best non-penalized solution
Solution.Design=OveralBestDes;% Design for best non-penalized solution
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
%% Save the results
save('SBO_Results.mat','Solution')
3 仿真结果
4 参考文献
[1] Farshchin, M. , et al. "School based optimization algorithm for design of steel frames." Engineering Structures 171(2018):326-335.
5 MATLAB代码与数据下载地址
见博客主页