水光互补优化调度】基于非支配排序遗传算法的多目标水光互补优化调度（Matlab代码实现）

%% NSGA-II Parameters
MaxIt=70;      % Maximum Number of Iterations
nPop=80;        % Population Size
pCrossover=0.7;                         % Crossover Percentage
nCrossover=2*round(pCrossover*nPop/2);  % Number of Parnets (Offsprings)
pMutation=0.4;                          % Mutation Percentage
nMutation=round(pMutation*nPop);        % Number of Mutants
mu=0.02;                    % Mutation Rate
sigma=0.1*(VarMax-VarMin);  % Mutation Step Size
%% Initialization
empty_individual.Position=[];
empty_individual.Cost=[];
empty_individual.Rank=[];
empty_individual.DominationSet=[];
empty_individual.DominatedCount=[];
empty_individual.CrowdingDistance=[];
pop=repmat(empty_individual,nPop,1);
disp('产生初始可行解...')
for i=1:nPop   
    flag=0;
    while flag==0
        tmp=[];
        for j=1:1:nVar
            tmp = [tmp unifrnd(VarMin(j),VarMax(j),1)];
        end
        flag = test(tmp);     % 检查约束 约束不满足就重新生成解
    end
    pop(i).Position=tmp;
    pop(i).Cost=CostFunction(pop(i).Position);
end
% pause
% Non-Dominated Sorting
[pop, F]=NonDominatedSorting(pop);
% Calculate Crowding Distance
pop=CalcCrowdingDistance(pop,F);
% Sort Population
[pop, F]=SortPopulation(pop);
%% NSGA-II Main Loop
for it=1:MaxIt
    % 交叉
    popc=repmat(empty_individual,nCrossover/2,2);
    for k=1:nCrossover/2
        i1=randi([1 nPop]);
        p1=pop(i1);
        i2=randi([1 nPop]);
        p2=pop(i2);
        [popc(k,1).Position, popc(k,2).Position]=Crossover(p1.Position,p2.Position,VarMin,VarMax);
        if test(popc(k,1).Position)+test(popc(k,2).Position)==2
            popc(k,1).Cost=CostFunction(popc(k,1).Position);
            popc(k,2).Cost=CostFunction(popc(k,2).Position);
        else
            popc(k,1)=p1;
            popc(k,2)=p2;
        end