1 简介

【优化求解】基于School Based Optimization (SBO)求解单目标_优化算法

【优化求解】基于School Based Optimization (SBO)求解单目标_优化算法_02

【优化求解】基于School Based Optimization (SBO)求解单目标_优化算法_03

【优化求解】基于School Based Optimization (SBO)求解单目标_优化算法_04

【优化求解】基于School Based Optimization (SBO)求解单目标_优化算法_05

2 部分代码

%% This function implements the basic School Based Optimization (SBO) algorithm for 10-bar truss optimization
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%%
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clc
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clear all
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close all
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global D
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% Specity SBO parameters
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Itmax=300;                                                                 % Maximum number of iterations
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NClass=5;                                                                  % Number of classes in the school
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PopSize=15;                                                                % Population size of each class
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% Optimization problem parameters
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D=Data10;                                                                  % For truss function evaluate the functio to get the initial parameters
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LB=D.LB;                                                                   % Lowerbound
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UB=D.UB;                                                                   % Upperbound
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FN='ST10';                                                                 % Name of analyzer function
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%% Randomely generate initial designs between LB and UB
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Cycle=1;
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for I=1:PopSize
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for NC=1:NClass
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Designs{NC}(I,:)=LB+rand(1,size(LB,2)).*(UB-LB);                   % Row vector
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end
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end
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% Analysis the designs
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for NC=1:NClass
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[PObj{NC},Obj{NC}]=Analyser(Designs{NC},FN);
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Best{NC}=[];
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end
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%% SBO loop
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for Cycle=2:Itmax
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for NC=1:NClass
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% Identify best designs and keep them
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[Best{NC},Designs{NC},PObj{NC},Obj{NC},WMeanPos{NC}]=Specifier(PObj{NC},Obj{NC},Designs{NC},Best{NC});
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TeachersPObj(NC,1)=Best{NC}.GBest.PObj;
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TeachersDes(NC,:)=Best{NC}.GBest.Design;
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end
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for NC=1:NClass
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% Select a teacher
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SelectedTeacher=TeacherSelector(Best,NC,TeachersPObj);
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% Apply Teaching
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[Designs{NC},PObj{NC},Obj{NC}]=Teaching(LB,UB,Designs{NC},PObj{NC},Obj{NC},TeachersDes(SelectedTeacher,:),WMeanPos{NC},FN);
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[Best{NC},Designs{NC},PObj{NC},Obj{NC},WMeanPos{NC}]=Specifier(PObj{NC},Obj{NC},Designs{NC},Best{NC});
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% Apply Learning
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[Designs{NC},PObj{NC},Obj{NC}]=Learning(LB,UB,Designs{NC},Obj{NC},PObj{NC},FN);
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[Best{NC},Designs{NC},PObj{NC},Obj{NC},WMeanPos{NC}]=Specifier(PObj{NC},Obj{NC},Designs{NC},Best{NC});
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end
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% Find best so far solution and Mean
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CumPObj=[];
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for NC=1:NClass
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ClassBestPObj(NC,1)=Best{NC}.GBest.PObj;
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ClassMean(NC,1)=mean(PObj{NC});
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CumPObj=[CumPObj;PObj{NC}];
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end
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[~,b]=min(ClassBestPObj);
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OveralBestPObj=Best{b}.GBest.PObj;
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OveralBestObj=Best{b}.GBest.Obj;
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OveralBestDes=Best{b}.GBest.Design;
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% Plot time history of the best solution vs. iteration and print the
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% results
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hold on;plot(Cycle,Best{b}.GBest.PObj,'b*');xlabel('Iteration');ylabel('Best solution value');pause(0.0001)
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fprintf('Cycle: %6d, Best (Penalized): %6.4f, Objective: %6.4f\n',Cycle,OveralBestPObj,OveralBestObj);
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end
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Solution.PObj=OveralBestPObj;% Objective value for best non-penalized solution
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Solution.Design=OveralBestDes;% Design for best non-penalized solution
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img =gcf;  %获取当前画图的句柄
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print(img, '-dpng', '-r600', './img.png')         %即可得到对应格式和期望dpi的图像
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%% Save the results
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save('SBO_Results.mat','Solution')
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3 仿真结果

【优化求解】基于School Based Optimization (SBO)求解单目标_优化算法_06

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.

【优化求解】基于School Based Optimization (SBO)求解单目标_优化算法_07