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⛄ 内容介绍
在本文中，提出了一种称为基于 PID 的搜索算法（PSA）的元启发式算法，用于全局优化。该算法基于增量PID算法，通过不断调整系统偏差将整个群体收敛到最优状态。PSA 经过数学建模和实施，可在广泛的搜索空间中实现优化。PSA用于解决CEC2017基准测试函数和六个约束问题。通过与近年来提出的七种元启发法进行比较，验证了PSA的优化性能。Kruskal-Wallis、Holm 和 Friedman 检验验证了 PSA 在统计显着性方面的优越性。结果表明，PSA能够较好地平衡勘探与开采，具有较强的优化能力。
⛄ 部分代码
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% PID-based search algorithm (PSA) source codes version 1.0% % Developed in: MATLAB 9.13 (R2022b)% % Programmer: Yuansheng Gao% % Original paper: Yuansheng Gao,% PID-based search algorithm: A novel metaheuristic % algorithm based on PID algorithm % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% fun : @YourFunction% nvars : number of decision/design variables% lb : lower bound of decision variables% % ub : upper bound of decision variables%% PSA will return the following:% x : best solution found% fval : objective function value of the found solutionclcclearclose all%% InputsPopSize = 50; % Population sizeMaxIter = 500; % Maximum iterationsFunctionName = 'F3'; % Function number: F1~F23%% Run PSA[lb,ub,nvars,fun] = GetFunctionsDetails(FunctionName);[x,fval,ConvergenceCurve] = PSA (fun,nvars,lb,ub,PopSize,MaxIter);%% Plot result% function topologyfigure('Position',[500 400 700 290])subplot(1,2,1);func_plot(FunctionName);title('Function Topology')xlabel('x_1');ylabel('x_2');zlabel([FunctionName,'( x_1 , x_2 )'])% Convergence curvesubplot(1,2,2);semilogy(ConvergenceCurve,'Color','r')title('Objective space')xlabel('Iteration');ylabel('Best score obtained so far');
⛄ 运行结果
