replace_chromosome.m

多目标优化进化算法目前公认效果收敛性最好的算法NSGA2源码

function f  = replace_chromosome(intermediate_chromosome,pro,pop)
%% replace_chromosome(intermediate_chromosome,pro,pop)
% This function replaces the chromosomes based on rank and crowding
% distance. Initially until the population size is reached each front is
% added one by one until addition of a complete front which results in
% exceeding the population size. At this point the chromosomes in that
% front is added subsequently to the population based on crowding distance.

[N,V] = size(intermediate_chromosome);
switch pro
        case 1
        M = 2;
        V = 6;
    case 2
        M = 3;
        V = 12;
end

% Get the index for the population sort based on the rank
[temp,index] = sort(intermediate_chromosome(:,M + V + 1));

% Now sort the individuals based on the index
for i = 1 : N
    sorted_chromosome(i,:) = intermediate_chromosome(index(i),:);
end

% Find the maximum rank in the current population
max_rank = max(intermediate_chromosome(:,M + V + 1));

% Start adding each front based on rank and crowing distance until the
% whole population is filled.
previous_index = 0;
for i = 1 : max_rank
    current_index = max(find(sorted_chromosome(:,M + V + 1) == i));
    if current_index > pop
        remaining = pop - previous_index;
        temp_pop = ...
            sorted_chromosome(previous_index + 1 : current_index, :);
        [temp_sort,temp_sort_index] = ...
            sort(temp_pop(:, M + V + 2),'descend');
        for j = 1 : remaining
            f(previous_index + j,:) = temp_pop(temp_sort_index(j),:);
        end
        return;
    elseif current_index < pop
        f(previous_index + 1 : current_index, :) = ...
            sorted_chromosome(previous_index + 1 : current_index, :);
    else
        f(previous_index + 1 : current_index, :) = ...
            sorted_chromosome(previous_index + 1 : current_index, :);
        return;
    end
    previous_index = current_index;
end