饥饿游戏搜索优化算法MATLAB实战

clear all close allclc
N=30; % Number of search agents
Function_name='F11'; % Name of the test function, range from F1-F13
FEs=100; % Maximum number of evaluation times
dimSize = 30;   %dimension size
% Load details of the selected benchmark function[lb,ub,dim,fobj]=Get_Functions_HGS(Function_name,dimSize);
[Destination_fitness,bestPositions,Convergence_curve]=HGS(N,FEs,lb,ub,dim,fobj);
% function topologyfigure('Position',[500 400 700 290])subplot(1,2,1);func_plot(Function_name);title('Function Topology')xlabel('x_1');ylabel('x_2');zlabel([Function_name,'( x_1 , x_2 )'])%Draw objective space% figure,% hold onsubplot(1,2,2);semilogy(Convergence_curve,'Color','b','LineWidth',4);title('Convergence curve')xlabel('Iteration');ylabel('Best fitness obtained so far');axis tightgrid offbox onlegend('HGS')

function [Destination_fitness,bestPositions,Convergence_curve]=HGS(N,Max_iter,lb,ub,dim,fobj)disp('HGS is now tackling your problem')
tic% initialize positionbestPositions=zeros(1,dim);tempPosition=zeros(N,dim);
Destination_fitness=inf;%change this to -inf for maximization problemsWorstest_fitness=-inf;AllFitness = inf*ones(N,1);%record the fitness of all positionsVC1 = ones(N,1);%record the variation control of all positions
weight3 = ones(N,dim);%hungry weight of each positionweight4 = ones(N,dim);%hungry weight of each position
%Initialize the set of random solutionsX=initialization(N,dim,ub,lb);Convergence_curve=zeros(1,Max_iter);it=1; %Number of iterations
hungry = zeros(1,size(X,1));%record the hungry of all positionscount=0;
% Main loopwhile  it <= Max_iter    VC2 = 0.03; %The variable of variation control 
    sumHungry = 0;%record the sum of each hungry         %sort the fitness    for i=1:size(X,1)        % Check if solutions go outside the search space and bring them back        Flag4ub=X(i,:)>ub;        Flag4lb=X(i,:)<lb;        X(i,:)=(X(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;        AllFitness(i) = fobj(X(i,:));    end    [AllFitnessSorted,IndexSorted] = sort(AllFitness);    bestFitness = AllFitnessSorted(1);    worstFitness = AllFitnessSorted(size(X,1));        %update the best fitness value and best position    if bestFitness < Destination_fitness        bestPositions=X(IndexSorted(1),:);        Destination_fitness = bestFitness;        count=0;    end        if worstFitness > Worstest_fitness        Worstest_fitness = worstFitness;    end        for i = 1:size(X,1)         %calculate the variation control of all positions         VC1(i) = sech(abs(AllFitness(i)-Destination_fitness));             %calculate the hungry of each position        if Destination_fitness == AllFitness(i)            hungry(1,i) = 0;            count = count+1;            tempPosition(count,:)=X(i,:);        else            temprand = rand();            c = (AllFitness(i)-Destination_fitness)/(Worstest_fitness-Destination_fitness)*temprand*2*(ub-lb);            if c<100                b=100*(1+temprand);            else                b=c;            end               hungry(1,i) = hungry(1,i)+ max(b);             sumHungry = sumHungry + hungry(1,i);        end    end         %calculate the hungry weight of each position    for i=1:size(X,1)        for j=2:size(X,2)                weight3(i,j) = (1-exp(-abs(hungry(1,i)-sumHungry)))*rand()*2;                if rand()<VC2                    weight4(i,j) = hungry(1,i)*size(X,1)/sumHungry*rand();                else                    weight4(i,j) = 1;                end        end            end            % Update the Position of search agents    shrink=2*(1-it/Max_iter); % a decreases linearly fron 2 to 0    for i=1:size(X,1)        if rand<VC2            X(i,:) = X(i,j)*(1+randn(1));        else            A = randi([1,count]);            for j=1:size(X,2)                r = rand();                vb = 2*shrink*r-shrink;%[-a,a]                % Moving based on the bestPosition                % The transformation range is controlled by weight3,bestPositions and X                if r>VC1(i)                    X(i,j) = weight4(i,j)*tempPosition(A,j)+vb*weight3(i,j)*abs(tempPosition(A,j)-X(i,j));                else                    X(i,j) = weight4(i,j)*tempPosition(A,j)-vb*weight3(i,j)*abs(tempPosition(A,j)-X(i,j));                end            end        end    end        Convergence_curve(it)=Destination_fitness;    it=it+1;endtocend