多目标粒子群在分布式能源中的选址定容（matlab程序）

function f = genetic_operator(parent_chromosome,M,V,mu,mum,Xmax,Xmin); %% 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. % % Redistribution and use in source and binary forms, with or without % modification, are permitted provided that the following conditions are % met: % % * Redistributions of source code must retain the above copyright % notice, this list of conditions and the following disclaimer. % * Redistributions in binary form must reproduce the above copyright % notice, this list of conditions and the following disclaimer in % the documentation and/or other materials provided with the distribution % % THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" % AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE % IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE % ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE % LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR % CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF % SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS % INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN % CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) % ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE % POSSIBILITY OF SUCH DAMAGE. [N,m] = size(parent_chromosome); clear m p = 1; % Flags used to set if crossover and mutation were actually performed. was_crossover = 0; was_mutation = 0; for i = 1 : N % With 90 % probability perform crossover if rand(1) < 0.9 % Initialize the children to be null vector. child_1 = []; child_2 = []; % 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. 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. %******************************************************** %% 控制变量约束 if child_1(j) >Xmax child_1(j) = Xmax; elseif child_1(j) < Xmin child_1(j) = Xmin; end %************************************************************** %% 控制变量约束 if child_2(j) >Xmax child_2(j) = Xmax; elseif child_2(j) < Xmin child_2(j) = Xmin; 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) = mokuaihanshu(child_1,V,M); child_2(:,V + 1: M + V) = mokuaihanshu(child_2,V,M); % 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; was_mutation = 0; % 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,:); % Perform mutation on eact element of the selected parent. 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); % Make sure that the generated element is within the decision % space. %**************************************** %% 控制变量约束 if child_3(j) >Xmax child_3(j) = Xmax; elseif child_3(j) < Xmin child_3(j) = Xmin; 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) = mokuaihanshu(child_3,V,M); % Set the mutation flag was_mutation = 1; was_crossover = 0; end % Keep proper count and appropriately fill the child variable with all % the generated children for the particular generation. if was_crossover child(p,:) = child_1; child(p+1,:) = child_2; was_cossover = 0; p = p + 2; elseif was_mutation child(p,:) = child_3(1,1 : M + V); was_mutation = 0; p = p + 1; end end f = child;