减法平均优化算法（SABO）求解单目标优化问题附matlab代码

原创已于 2023-08-16 19:49:38 修改 · 259 阅读

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于 2023-08-16 19:41:23 首次发布

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本文介绍了2023年提出的一种新的智能优化算法——Subtraction-Average-BasedOptimizer(SABO)，它利用个体间的减法平均来更新搜索位置，具有高效寻优和快速收敛的特点，涉及的应用领域包括信号处理、图像处理、路径规划、无人机技术和电力系统等。

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信号处理图像处理路径规划元胞自动机无人机

⛄ 内容介绍

基于减法平均的优化算法（Subtraction-Average-Based Optimizer (SABO)），是于2023年提出的一种基于数学行为的优化算法，该算法通过使用个体的减法平均值来更新群体成员在搜索空间中的位置，具有寻优能力强，收敛速度快等特点。

⛄ 部分代码

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Grey Wolf Optimizerfunction [Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)% initialize alpha, beta, and delta_posAlpha_pos=zeros(1,dim);Alpha_score=inf; %change this to -inf for maximization problemsBeta_pos=zeros(1,dim);Beta_score=inf; %change this to -inf for maximization problemsDelta_pos=zeros(1,dim);Delta_score=inf; %change this to -inf for maximization problems%Initialize the positions of search agentsPositions=initialization(SearchAgents_no,dim,ub,lb);Convergence_curve=zeros(1,Max_iter);l=0;% Loop counter% Main loopwhile l<Max_iter    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,:)<lb;        Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;                               % Calculate objective function for each search agent        fitness=fobj(Positions(i,:));                % Update Alpha, Beta, and Delta        if fitness<Alpha_score             Alpha_score=fitness; % Update alpha            Alpha_pos=Positions(i,:);        end                if fitness>Alpha_score && fitness<Beta_score             Beta_score=fitness; % Update beta            Beta_pos=Positions(i,:);        end                if fitness>Alpha_score && fitness>Beta_score && fitness<Delta_score             Delta_score=fitness; % Update delta            Delta_pos=Positions(i,:);        end    end            a=2-l*((2)/Max_iter); % a decreases linearly fron 2 to 0        % Update the Position of search agents including omegas    for i=1:size(Positions,1)        for j=1:size(Positions,2)                                        r1=rand(); % r1 is a random number in [0,1]            r2=rand(); % r2 is a random number in [0,1]                        A1=2*a*r1-a; % Equation (3.3)            C1=2*r2; % Equation (3.4)                        D_alpha=abs(C1*Alpha_pos(j)-Positions(i,j)); % Equation (3.5)-part 1            X1=Alpha_pos(j)-A1*D_alpha; % Equation (3.6)-part 1                                   r1=rand();            r2=rand();                        A2=2*a*r1-a; % Equation (3.3)            C2=2*r2; % Equation (3.4)                        D_beta=abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (3.5)-part 2            X2=Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2                               r1=rand();            r2=rand();                         A3=2*a*r1-a; % Equation (3.3)            C3=2*r2; % Equation (3.4)                        D_delta=abs(C3*Delta_pos(j)-Positions(i,j)); % Equation (3.5)-part 3            X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3                                     Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7)                    end    end    l=l+1;        Convergence_curve(l)=Alpha_score;end