平衡优化算法（EO）结合粘液霉菌算法（SMA）（Matlab代码实现）

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目录

1 概述

2 运行结果

3 参考文献

4 Matlab代码实现

1 概述

     平衡粘液霉菌算法 （ESMA） 算法，通过整合平衡优化算法（EO）的平衡概念，改进了粘液霉菌算法（SMA）。

       对粘液霉菌算法（SMA）的改进，通过整合平衡实践，从平衡优化器（EO）的平衡池概念更新粘液霉菌位置。ESMA性能与众所周知的优化算法进行比较，并在评估基准测试问题时根据弗里德曼的平均排名进行排名。

2 运行结果

 

部分代码：

function [Destination_fitness,bestPositions,Convergence_curve]=ESMA(N,Max_iter,lb,ub,dim,fobj)

bestPositions=zeros(1,dim);
Destination_fitness=inf;%change this to -inf for maximization problems
AllFitness = inf*ones(N,1);%record the fitness of all slime mold
weight = ones(N,dim);%fitness weight of each slime mold
%Initialize the set of random solutions
X=initialization(N,dim,ub,lb);
Convergence_curve=zeros(1,Max_iter);

it=1;  %Number of iterations
lb=ones(1,dim).*lb; % lower boundary 
ub=ones(1,dim).*ub; % upper boundary
z=0.03; % parameter

% Main loop
while  it <= Max_iter
    for i=1:N
        % Check if solutions go outside the search space and bring them back
        Flag4ub=X(i,:)>ub;
        Flag4lb=X(i,:)         X(i,:)=(X(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
        AllFitness(i) = fobj(X(i,:));
    end
    %sort the fitness       
    [SmellOrder,SmellIndex] = sort(AllFitness);  %Eq.(3)
    bestFitness = SmellOrder(1); %Eq.(5)/ Eq.(10)       
    worstFitness = SmellOrder(N); %Eq.(6)
    bestPositions2=X(SmellIndex(2),:); %Eq.(10)
    bestPositions3=X(SmellIndex(3),:); %Eq.(10)
    bestPositions4=X(SmellIndex(4),:); %Eq.(10)

    S=bestFitness-worstFitness+eps;  % plus eps to avoid denominator zero

    %calculate the fitness weight of each slime mold
    for i=1:N
        for j=1:dim
            if i<=(N/2)  %Eq.(4)
                weight(SmellIndex(i),j) = 1+rand()*log10((bestFitness-SmellOrder(i))/(S)+1);
            else
                weight(SmellIndex(i),j) = 1-rand()*log10((bestFitness-SmellOrder(i))/(S)+1);
            end
        end
    end
    
    %update the best fitness value and best position
    if bestFitness < Destination_fitness
        bestPositions=X(SmellIndex(1),:);
        Destination_fitness = bestFitness;
    end
    avgPositions=(bestPositions+bestPositions2+bestPositions3+bestPositions4)/4;    %Eq.(10)
    C_pool=[bestPositions; bestPositions2; bestPositions3; bestPositions4; avgPositions]; %Eq.(11)
    a = atanh(-(it/Max_iter)+1);   %Eq.(8)
    b = 1-it/Max_iter;             %Eq.(9)
    % Update the Position of search agents
    for i=1:N
        if rand             X(i,:) = (ub-lb)*rand+lb;
        else
            p =tanh(abs(AllFitness(i)-Destination_fitness));  %Eq.(7)
            vb = unifrnd(-a,a,1,dim);  
            vc = unifrnd(-b,b,1,dim);
            for j=1:dim
                r = rand();
                Ceq=C_pool(randi(size(C_pool,1)),:);
                A = randi([1,N]);  % one positions randomly selected from population
                if r

                    X(i,j)=bestPositions(j)+vb(j)*(weight(i,j)*Ceq(j)-X(A,j));
                else      %Eq.(12c)
                    X(i,j) = vc(j)*X(i,j);
                end
            end
        end
    end
    Convergence_curve(it)=Destination_fitness;
    it=it+1;
end

 

3 参考文献

[1]Naik, Manoj Kumar, et al. “An Entropy Minimization Based Multilevel Color Thresholding Technique for Analysis of Breast Thermograms Using Equilibrium Slime Mould Algorithm.” Applied Soft Computing, Elsevier BV, Oct. 2021, p. 107955, doi:10.1016/j.asoc.2021.107955.

4 Matlab代码实现