帝国竞争算法源代码

clc; clear; close all; %% Problem Definition CostFunction=@(x) Sphere(x); % Cost Function nVar=5; % Number of Decision Variables VarSize=[1 nVar]; % Decision Variables Matrix Size VarMin=-10; % Lower Bound of Variables VarMax= 10; % Upper Bound of Variables %% ICA Parameters MaxIt=500; % Maximum Number of Iterations nPop=100; % Population Size nEmp=15; % Number of Empires/Imperialists alpha=1; % Selection Pressure beta=1.5; % Assimilation Coefficient pRevolution=0.05; % Revolution Probability mu=0.1; % Revolution Rate zeta=0.2; % Colonies Mean Cost Coefficient %% Globalization of Parameters and Settings参数和设置的全球化 global ProblemSettings; ProblemSettings.CostFunction=CostFunction; ProblemSettings.nVar=nVar; ProblemSettings.VarSize=VarSize; ProblemSettings.VarMin=VarMin; ProblemSettings.VarMax=VarMax; global ICASettings; ICASettings.MaxIt=MaxIt; ICASettings.nPop=nPop; ICASettings.nEmp=nEmp; ICASettings.alpha=alpha; ICASettings.beta=beta; ICASettings.pRevolution=pRevolution; ICASettings.mu=mu; ICASettings.zeta=zeta; %% Initialization % Initialize Empires emp=CreateInitialEmpires(); % Array to Hold Best Cost Values BestCost=zeros(MaxIt,1);%% MaxIt=500 %% ICA Main Loop for it=1:MaxIt % Assimilation emp=AssimilateColonies(emp); % Revolution emp=DoRevolution(emp); % Intra-Empire Competition emp=IntraEmpireCompetition(emp); % Update Total Cost of Empires emp=UpdateTotalCost(emp); % Inter-Empire Competition emp=InterEmpireCompetition(emp); % Update Best Solution Ever Found imp=[emp.Imp]; [~, BestImpIndex]=min([imp.Cost]); BestSol=imp(BestImpIndex); % Update Best Cost BestCost(it)=BestSol.Cost; % Show Iteration Information disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]); end %% Results figure; %plot(BestCost,'LineWidth',2); semilogy(BestCost,'LineWidth',2); xlabel('Iteration'); ylabel('Best Cost'); grid on;