基于适应度距离平衡的全局优化问题导向机制的改进粘液-霉菌算法（Matlab代码实现）

% Max_iter: maximum iterations, N: populatoin size, Convergence_curve: Convergence curve
% To run SMA: [Destination_fitness,bestPositions,Convergence_curve]=SMA(N,Max_iter,lb,ub,dim,fobj)
%function [Destination_fitness,bestPositions,Convergence_curve]=sma(N,Max_iter,lb,ub,dim,fobj)
function[] = FDB_sma_case_1()
disp('SMA is now tackling your problem')
[N,dim,Max_iter ,lb,ub]=problem_terminate();
%fhd=cec20so;
% initialize position
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
    disp(it)
    fdbindex = fitnessDistanceBalance( X, Destination_fitness );
    %sort the fitness
    for i=1:N
        % 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,:));
        AllFitness(i)=problem( X(i,:)' );
            it=it+1;
    end
    [SmellOrder,SmellIndex] = sort(AllFitness);  %Eq.(2.6)
    worstFitness = SmellOrder(N);
    bestFitness = SmellOrder(1);
    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.(2.5)
                weight(SmellIndex(i),j) = 1+rand()*log10((bestFitness-SmellOrder(i))/(S)+1);%fdb
            else
                weight(SmellIndex(i),j) = 1-rand()*log10((bestFitness-SmellOrder(i))/(S)+1);%fdb
            end
        end
    end
    %update the best fitness value and best position
    if bestFitness < Destination_fitness
        bestPositions=X(SmellIndex(1),:);
        Destination_fitness = bestFitness;
    end
    a = atanh(-(it/Max_iter)+1);   %Eq.(2.4)
    b = 1-it/Max_iter;
    % Update the Position of search agents
    for i=1:N
        if rand<z     %Eq.(2.7)
            X(i,:) = (ub-lb)*rand+lb;
        else
           if(rand<0.5)
            p =tanh(abs(AllFitness(fdbindex)-Destination_fitness));  %Eq.(2.2)%fdb
           else
            p =tanh(abs(AllFitness(i)-Destination_fitness));  %Eq.(2.2)%fdb   
           end
            vb = unifrnd(-a,a,1,dim);  %Eq.(2.3)
            vc = unifrnd(-b,b,1,dim);
            for j=1:dim
                r = rand();
                A = randi([1,N]);  % two positions randomly selected from population
                B = randi([1,N]);
                if r<p    %Eq.(2.1)
                   if(rand<0.5)
                    X(i,j) = bestPositions(j)+ vb(j)*(weight( fdbindex,j)*X(fdbindex,j)-X(fdbindex,j));%fdb
                   else
                    X(i,j) = bestPositions(j)+ vb(j)*(weight( i,j)*X(A,j)-X(B,j));%fdb
                   end
                else
                    X(i,j) = vc(j)*X(fdbindex,j);%fdb
                end
            end
        end
    end
    Convergence_curve(it)=Destination_fitness;
    %it=it+1;
end
bestSolution=bestPositions;
bestFitness= Destination_fitness;
iteration=it;
disp(it)
end
function Positions=initialization(SearchAgents_no,dim,ub,lb)
Boundary_no= size(ub,2); % numnber of boundaries
% If the boundaries of all variables are equal and user enter a signle
% number for both ub and lb
if Boundary_no==1
    Positions=rand(SearchAgents_no,dim).*(ub-lb)+lb;
end
% If each variable has a different lb and ub
if Boundary_no>1
    for i=1:dim
        ub_i=ub(i);
        lb_i=lb(i);
        Positions(:,i)=rand(SearchAgents_no,1).*(ub_i-lb_i)+lb_i;
    end
end
end