【智能优化算法】基于混沌策略和单纯形法改进的鲸鱼优化算法求解单目标优化问题(CSWOA)附matlab代码

1 简介

为解决鲸鱼优化算法收敛速度慢和寻优精度低等问题,提出了一种基于混沌策略和单纯形法优化的鲸鱼优化算法(whale optimization algorithm based on chaos optimization and simplex optimization,CSWOA).首先,采用混沌反向学习策略初始化鲸鱼种群个体,降低随机化的原始种群对算法收敛的影响;然后,引入一种自适应权重策略,平衡算法的全局寻优和局部探索能力;最后,再引入单纯形法对原算法进行改进,提高算法的局部搜索能力和寻优能力.对8个典型基准函数的仿真分析表明,CSWOA的收敛速度和寻优精度均有一定的提高.

2 部分代码

%_________________________________________________________________________%

% 鲸鱼优化算法             %

%_________________________________________________________________________%

% The Whale Optimization Algorithm

function [Leader_score,Leader_pos,Convergence_curve]=WOA(SearchAgents_no,Max_iter,lb,ub,dim,fobj)

% initialize position vector and score for the leader 

Leader_pos=zeros(1,dim);

Leader_score=inf; %change this to -inf for maximization problems

%Initialize the positions of search agents

Positions=initialization(SearchAgents_no,dim,ub,lb);

Convergence_curve=zeros(1,Max_iter);

t=0;% Loop counter

% Main loop

while t

    for i=1:size(Positions,1)

        % Return back the search agents that go beyond the boundaries of the search space

        Flag4ub=Positions(i,:)>ub;

        Flag4lb=Positions(i,:)

        Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;

        % Calculate objective function for each search agent

        fitness=fobj(Positions(i,:));

        % Update the leader

        if fitness for maximization problem

            Leader_score=fitness; % Update alpha

            Leader_pos=Positions(i,:);

        end

    end

    a=2-t*((2)/Max_iter); % a decreases linearly fron 2 to 0 in Eq. (2.3)

    % a2 linearly dicreases from -1 to -2 to calculate t in Eq. (3.12)

    a2=-1+t*((-1)/Max_iter);

    % Update the Position of search agents 

    for i=1:size(Positions,1)

        r1=rand(); % r1 is a random number in [0,1]

        r2=rand(); % r2 is a random number in [0,1]

        A=2*a*r1-a;  % Eq. (2.3) in the paper

        C=2*r2;      % Eq. (2.4) in the paper

        b=1;               %  parameters in Eq. (2.5)

        l=(a2-1)*rand+1;   %  parameters in Eq. (2.5)

        p = rand();        % p in Eq. (2.6)

        for j=1:size(Positions,2)

            if p<0.5   

                if abs(A)>=1

                    rand_leader_index = floor(SearchAgents_no*rand()+1);

                    X_rand = Positions(rand_leader_index, :);

                    D_X_rand=abs(C*X_rand(j)-Positions(i,j)); % Eq. (2.7)

                    Positions(i,j)=X_rand(j)-A*D_X_rand;      % Eq. (2.8)

                elseif abs(A)<1

                    D_Leader=abs(C*Leader_pos(j)-Positions(i,j)); % Eq. (2.1)

                    Positions(i,j)=Leader_pos(j)-A*D_Leader;      % Eq. (2.2)

                end

            elseif p>=0.5

                distance2Leader=abs(Leader_pos(j)-Positions(i,j));

                % Eq. (2.5)

                Positions(i,j)=distance2Leader*exp(b.*l).*cos(l.*2*pi)+Leader_pos(j);

            end

        end

    end

    t=t+1;

    Convergence_curve(t)=Leader_score;

end

3 仿真结果

4 参考文献

[1]张潮, 冯锋. 混沌策略和单纯形法改进的鲸鱼优化算法[J]. 中国科技论文, 2020, 15(3):7.

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