crowding_distance.m

多目标遗传算法通用编程包

function f = crowding_distance(x,problem)
% This function calculates the crowding distance

%
%     Copyright (C) 2009 Aravind Seshadri
%
%     This program is free software: you can redistribute it and/or modify
%     it under the terms of the GNU General Public License as published by
%     the Free Software Foundation, either version 3 of the License, or
%     (at your option) any later version.
% 
%     This program is distributed in the hope that it will be useful,
%     but WITHOUT ANY WARRANTY; without even the implied warranty of
%     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
%     GNU General Public License for more details.
% 
%     You should have received a copy of the GNU General Public License
%     along with this program.  If not, see <http://www.gnu.org/licenses/>.

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

% Crowding distance for each front
for i = 1 : length(F(front).f)
    y(i,:) = x(F(front).f(i),:);
end
for i = 1 : M
    [sorted(i).individual,sorted(i).index] = sort(y(:,V + i));
    distance(sorted(i).index(1)).individual = Inf;
    distance(sorted(i).index(length(sorted(i).index))).individual = Inf;
end

[num,len] = size(y);
% Initialize all the distance of individuals as zero.
for i = 1 : M
    for j = 2 : num - 1
        distance(j).individual = 0;
    end
    objective(i).range = ...
                sorted(i).individual(length(sorted(i).individual)) - ...
                sorted(i).individual(1);
        % Maximum and minimum objectives value for the ith objective
end 
% Caluclate the crowding distance for front one.
for i = 1 : M
    for j = 2 : num - 1
        distance(j).individual = distance(j).individual + ...
            (sorted(i).individual(j + 1) - sorted(i).individual(j - 1))/...
            objective(i).range;
        y(sorted(i).index(j),M + V + 2) = distance(j).individual;
    end
end