www.gusucode.com > 几种多目标优化算法集合，包括MOEAD，MOPSO，NNIA，NSGA2等 > NSGA-II/genetic_operator.m
function f = genetic_operator(parent_chromosome, M, V, mu, mum, l_limit, u_limit)
%% function f = genetic_operator(parent_chromosome, M, V, mu, mum, l_limit, u_limit)
%
% This function is utilized to produce offsprings from parent chromosomes.
% The genetic operators corssover and mutation which are carried out with
% slight modifications from the original design. For more information read
% the document enclosed.
%
% parent_chromosome - the set of selected chromosomes.
% M - number of objective functions
% V - number of decision varaiables
% mu - distribution index for crossover (read the enlcosed pdf file)
% mum - distribution index for mutation (read the enclosed pdf file)
% l_limit - a vector of lower limit for the corresponding decsion variables
% u_limit - a vector of upper limit for the corresponding decsion variables
%
% The genetic operation is performed only on the decision variables, that
% is the first V elements in the chromosome vector.
% Copyright (c) 2009, Aravind Seshadri
% All rights reserved.
%
[N,m] = size(parent_chromosome);
% clear m; % modified by zzb
% p = 1; % modified by zzb
p = 0; % modified by zzb
% Flags used to set if crossover and mutation were actually performed.
% was_crossover = 0; % modified by zzb
% was_mutation = 0; % modified by zzb
child = zeros(2*N,M + V); % modified by zzb
for i = 1 : N
% With 90 % probability perform crossover
if rand(1) < 0.9
% Initialize the children to be null vector.
% child_1 = []; % modified by zzb
% child_2 = []; % modified by zzb
% Select the first parent
parent_1 = round(N*rand(1));
if parent_1 < 1
parent_1 = 1;
end
% Select the second parent
parent_2 = round(N*rand(1));
if parent_2 < 1
parent_2 = 1;
end
% Make sure both the parents are not the same.
while isequal(parent_chromosome(parent_1,:),parent_chromosome(parent_2,:))
parent_2 = round(N*rand(1));
if parent_2 < 1
parent_2 = 1;
end
end
% Get the chromosome information for each randomnly selected parents
parent_1 = parent_chromosome(parent_1,:);
parent_2 = parent_chromosome(parent_2,:);
% Perform corssover for each decision variable in the chromosome.
child_1 = zeros(1,m); % modified by zzb
child_2 = zeros(1,m); % modified by zzb
u = zeros(1,V); % modified by zzb
bq = zeros(1,V); % modified by zzb
for j = 1 : V
% SBX (Simulated Binary Crossover).
% For more information about SBX refer the enclosed pdf file.
% Generate a random number
u(j) = rand(1);
if u(j) <= 0.5
bq(j) = (2*u(j))^(1/(mu+1));
else
bq(j) = (1/(2*(1 - u(j))))^(1/(mu+1));
end
% Generate the jth element of first child
child_1(j) = ...
0.5*(((1 + bq(j))*parent_1(j)) + (1 - bq(j))*parent_2(j));
% Generate the jth element of second child
child_2(j) = ...
0.5*(((1 - bq(j))*parent_1(j)) + (1 + bq(j))*parent_2(j));
% Make sure that the generated element is within the specified
% decision space else set it to the appropriate extrema.
if child_1(j) > u_limit(j)
child_1(j) = u_limit(j);
elseif child_1(j) < l_limit(j)
child_1(j) = l_limit(j);
end
if child_2(j) > u_limit(j)
child_2(j) = u_limit(j);
elseif child_2(j) < l_limit(j)
child_2(j) = l_limit(j);
end
end
% Evaluate the objective function for the offsprings and as before
% concatenate the offspring chromosome with objective value.
% child_1(:,V + 1: M + V) = evaluate_objective(child_1, M, V);
% child_2(:,V + 1: M + V) = evaluate_objective(child_2, M, V);
child_1(:,V + 1: M + V) = evaluate_objective(child_1, M); % modified by zzb
child_2(:,V + 1: M + V) = evaluate_objective(child_2, M); % modified by zzb
% Set the crossover flag. When crossover is performed two children
% are generate, while when mutation is performed only only child is
% generated.
% was_crossover = 1; % modified by zzb
% was_mutation = 0; % modified by zzb
p = p + 2; % modified by zzb
child(p - 1,:) = child_1(1,1: M + V); % modified by zzb
child(p,:) = child_2(1,1: M + V); % modified by zzb
% With 10 % probability perform mutation. Mutation is based on
% polynomial mutation.
else
% Select at random the parent.
parent_3 = round(N*rand(1));
if parent_3 < 1
parent_3 = 1;
end
% Get the chromosome information for the randomnly selected parent.
% child_3 = parent_chromosome(parent_3,:);
parent_3 = parent_chromosome(parent_3,:); % modified by zzb
% Perform mutation on eact element of the selected parent.
child_3 = zeros(1,m); % modified by zzb
r = zeros(1,V); % modified by zzb
delta = zeros(1,V); % modified by zzb
for j = 1 : V
r(j) = rand(1);
if r(j) < 0.5
delta(j) = (2*r(j))^(1/(mum+1)) - 1;
else
delta(j) = 1 - (2*(1 - r(j)))^(1/(mum+1));
end
% Generate the corresponding child element.
% child_3(j) = child_3(j) + delta(j);
child_3(j) = parent_3(j) + delta(j); % modified by zzb
% Make sure that the generated element is within the decision
% space.
if child_3(j) > u_limit(j)
child_3(j) = u_limit(j);
elseif child_3(j) < l_limit(j)
child_3(j) = l_limit(j);
end
end
% Evaluate the objective function for the offspring and as before
% concatenate the offspring chromosome with objective value.
% child_3(:,V + 1: M + V) = evaluate_objective(child_3, M, V);
child_3(:,V + 1: M + V) = evaluate_objective(child_3, M); % modified by zzb
% Set the mutation flag
% was_mutation = 1; % modified by zzb
% was_crossover = 0; % modified by zzb
p = p + 1; % modified by zzb
child(p,:) = child_3(1,1 : M + V); % modified by zzb
end
% Keep proper count and appropriately fill the child variable with all
% the generated children for the particular generation.
% if was_crossover % modified by zzb
% child(p,:) = child_1; % modified by zzb
% child(p+1,:) = child_2; % modified by zzb
% was_cossover = 0; % modified by zzb
% p = p + 2; % modified by zzb
% elseif was_mutation % modified by zzb
% child(p,:) = child_3(1,1 : M + V); % modified by zzb
% was_mutation = 0; % modified by zzb
% p = p + 1; % modified by zzb
% end % modified by zzb
end
% f = child; % modified by zzb
f = child(1:p,:); % modified by zzb