genetic_operator.m

对多目标优化算法NSGA-II算法的改进

function f = genetic_operator(parent_pop,pc,pm,muc,mum)
    M = 2;
    V = 6;
    MIN = 0;
    MAX = 1;
    [N temp]= size(parent_pop);
    p = 1;
    was_crossover = 0;
    was_mutation = 0;
    for i = 1 : N
        if (rand < pc)
            parent_1 = [];
            parent_2 = [];
            child_1 = [];
            child_2 = [];
            parent_1 = round((N - 1)*rand) + 1;
            parent_2 = round((N - 1)*rand) + 1;
            while (isequal(parent_pop(parent_1,1 : V),parent_pop(parent_2,1 : V)))
                parent_2 = round((N - 1)*rand) + 1;
            end
            parent_1 = parent_pop(parent_1,1 : V);
            parent_2 = parent_pop(parent_2,1 : V);
            for k = 1 : V
                u(k) = rand;
                if u(k) <= 0.5
                    beita(k) = (2*u(k))^(1/(muc + 1));
                else
                    beita(k) = 1/((2*(1 - u(k)))^(1/(muc + 1)));
                end
                child_1(k) = 0.5*((1 - beita(k))*parent_1(k) + (1 + beita(k))*parent_2(k));
                child_2(k) = 0.5*((1 + beita(k))*parent_1(k) + (1 - beita(k))*parent_2(k));
                if (child_1(k) < MIN)
                    child_1(k) = MIN;
                elseif (child_1(k) > MAX)
                    child_1(k) = MAX;
                end
                if (child_2(k) < MIN)
                    child_2(k) = MIN;
                elseif (child_2(k) > MAX)
                    child_2(k) = MAX;
                end
            end
            child_1(:,V + 1 : M + V) = evaluate_objective(child_1);
            child_2(:,V + 1 : M + V) = evaluate_objective(child_2);
            was_crossover = 1;
        % pm = 0.1 执行变异操作
        else
            parent_3 = [];
            child_3 = [];
            parent_3 = round((N - 1)*rand) + 1;
            parent_3 = parent_pop(parent_3,1 : V);
            for k = 1 : V
                r(k) = rand;
                if (r(k) < 0.5)
                    delta(k) = (2*r(k))^(1/(mum + 1)) - 1;
                else
                    delta(k) = 1 - (2*(1 - r(k)))^(1/(mum + 1));
                end
                child_3(k) = parent_3(k) + (MAX - MIN)*delta(k);
                if (child_3(k) < MIN)
                    child_3(k) = MIN;
                elseif (child_3(k) > MAX)
                    child_3(k) = MAX;
                end
            end
            child_3(:,V + 1 : M + V) = evaluate_objective(child_3);
            was_mutation = 1;
        end
        if (was_crossover)
            child(p,:) = child_1;
            child(p + 1,:) = child_2;
            p = p + 2;
            was_crossover = 0; 
        elseif(was_mutation)
            child(p,:) = child_3; 
            p = p + 1;
            was_mutation = 0;
        end
    end
    f = child;