斑马优化算法(Zebra Optimization Algorithm,ZOA)是中科院三区期刊“IEEE Access”上论文“Zebra Optimization Algorithm: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm”上的智能优化算法

01.引言

斑马优化算法(Zebra Optimization Algorithm，ZOA)的基本灵感来自于斑马在自然界中的行为。ZOA模拟了斑马的觅食行为和它们抵御捕食者攻击的防御策略。描述了ZOA步骤，然后建立了数学模型。通过单峰、高维多峰、定维多峰、CEC2015、CEC2017等68个基准函数对ZOA优化后的性能进行了评价。将ZOA算法的结果与九种常用算法的性能进行了比较。仿真结果表明，ZOA算法通过在勘探和开采之间建立适当的平衡来解决优化问题，与9种竞争算法相比，具有优越的性能。ZOA解决实际问题的能力已经在四个工程设计问题上进行了测试，即拉伸/压缩弹簧、焊接梁、减速机和压力容器。

02.代码流程

03.部分代码

%%% Designed and Developed by Eva Trojovská1, Mohammad Dehghani1, and Pavel Trojovský1* %%%
function[Best_score,Best_pos,ZOA_curve]=ZOA(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness)
lowerbound=ones(1,dimension).*(lowerbound);                              % Lower limit for variables
upperbound=ones(1,dimension).*(upperbound);                              % Upper limit for variables
%% INITIALIZATION
for i=1:dimension
    X(:,i) = lowerbound(i)+rand(SearchAgents,1).*(upperbound(i) - lowerbound(i));                          % Initial population
end
for i =1:SearchAgents
    L=X(i,:);
    fit(i)=fitness(L);
end
%%
for t=1:Max_iterations
    %% update the global best (fbest)
    [best , location]=min(fit);
    if t==1
        PZ=X(location,:);                                           % Optimal location
        fbest=best;                                           % The optimization objective function
    elseif best:);
    end
    
    %% PHASE1: Foraging Behaviour
    for i=1:SearchAgents
        
        I=round(1+rand);
        X_newP1=X(i,:)+ rand(1,dimension).*(PZ-I.* X(i,:)); %Eq(3)
        X_newP1= max(X_newP1,lowerbound);X_newP1 = min(X_newP1,upperbound);
        
        
        % Updating X_i using (5)
        f_newP1 = fitness(X_newP1);
        if f_newP1 <= fit (i)
            X(i,:) = X_newP1;
            fit (i)=f_newP1;
        end
    end
    %% End Phase 1: Foraging Behaviour
    
    %% PHASE2: defense strategies against predators
    Ps=rand;
    k=randperm(SearchAgents,1);
    AZ=X(k,:);% attacked zebra
    
    for i=1:SearchAgents
        
        if Ps<0.5
            %% S1: the lion attacks the zebra and thus the zebra chooses an escape strategy
            R=0.1;
            X_newP2= X(i,:)+ R*(2*rand(1,dimension)-1)*(1-t/Max_iterations).*X(i,:);% Eq.(5) S1
            X_newP2= max(X_newP2,lowerbound);X_newP2 = min(X_newP2,upperbound);
      
        else
            %% S2: other predators attack the zebra and the zebra will choose the offensive strategy
            
            I=round(1+rand(1,1));
            X_newP2=X(i,:)+ rand(1,dimension).*(AZ-I.* X(i,:)); %Eq(5) S2
            X_newP2= max(X_newP2,lowerbound);X_newP2 = min(X_newP2,upperbound);
             
        end
        
        f_newP2 = fitness(X_newP2); %Eq (6)
        if f_newP2 <= fit (i)
            X(i,:) = X_newP2;
            fit (i)=f_newP2;
        end
    end %
    %%
    %%
    
    best_so_far(t)=fbest;
    average(t) = mean (fit);
    
end %t=1:Max_iterations
Best_score=fbest;
Best_pos=PZ;
ZOA_curve=best_so_far;
end

04.代码效果图