基于多目标樽海鞘算法的电力系统无功电压优化

%___________________________________________________________________% % Multi-Objective Grey Wolf Optimizer (MOGWO) % % Source codes demo version 1.0 % % % % Developed in MATLAB R2011b(7.13) % % % % Author and programmer: Seyedali Mirjalili % % % % e-Mail: ali.mirjalili@gmail.com % % seyedali.mirjalili@griffithuni.edu.au % % % % Homepage: http://www.alimirjalili.com % % % % Main paper: % % % % S. Mirjalili, S. Saremi, S. M. Mirjalili, L. Coelho, % % Multi-objective grey wolf optimizer: A novel algorithm for % % multi-criterion optimization, Expert Systems with Applications,% % in press, DOI: http://dx.doi.org/10.1016/j.eswa.2015.10.039 % % % % %___________________________________________________________________% % I acknowledge that this version of MOGWO has been written using % a large portion of the following code: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % MATLAB Code for % % % % Multi-Objective Particle Swarm Optimization (MOPSO) % % Version 1.0 - Feb. 2011 % % % % According to: % % Carlos A. Coello Coello et al., % % "Handling Multiple Objectives with Particle Swarm Optimization," % % IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, % % pp. 256-279, June 2004. % % % % Developed Using MATLAB R2009b (Version 7.9) % % % % Programmed By: S. Mostapha Kalami Heris % % % % e-Mail: sm.kalami@gmail.com % % kalami@ee.kntu.ac.ir % % % % Homepage: http://www.kalami.ir % % % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clear; close all; clc; drawing_flag = false; % 粒子群算法(PSO)、樽海鞘群算法(SSA)、灰色狼群算法(GWO)结果的综合比较 N = 12; % 补偿装置总数 nVar = 24*N; %维度（决策变量的个数） % M = 2; % 目标数 M = 3; % 目标数 % Active Powers in kWs Generated by Photovoltatic Arrays at Bus 8, 25, 32 % 第1、2、3、4、5、6、7、8、9、10、11、12组补偿位置分别为 % 母线24、25、7、8、30、32、4、14、29、31、2、23 % 第1组的补偿装置容量范围为0 ~ 0.2Mvar % 第2组的补偿装置容量范围为-0.2 ~ 0.2Mvar % 第3组的补偿装置容量范围为0 ~ 0.1Mvar % 第4组的补偿装置容量范围为-0.1 ~ 0.1Mvar % 第5组的补偿装置容量范围为0 ~ 0.1Mvar % 第6组的补偿装置容量范围为-0.1 ~ 0.1Mvar % 第7组的补偿装置容量范围为0 ~ 0.08Mvar % 第8组的补偿装置容量范围为0 ~ 0.08Mvar % 第9组的补偿装置容量范围为0 ~ 0.07Mvar % 第10组的补偿装置容量范围为0 ~ 0.07Mvar % 第11组的补偿装置容量范围为0 ~ 0.06Mvar % 第12组的补偿装置容量范围为0 ~ 0.05Mvar % 下边界 Lb = zeros(24, N); Lb(:, 2) = -0.2; Lb(:, [4, 6]) = -0.1; % 上边界 Ub = zeros(24, N); Ub(:, 1:2) = 0.2; Ub(:, 3:6) = 0.1; Ub(:, 7:8) = 0.08; Ub(:, 9:10) = 0.07; Ub(:, 11) = 0.06; Ub(:, 12) = 0.05; % 改变维度布局 min_range = reshape(Lb', 1, nVar); max_range = reshape(Ub', 1, nVar); % min_range = zeros(1, V); %下界 生成1*30的个体向量 全为0 % max_range = ones(1, V); %上界 生成1*30的个体向量 全为1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Objective Functions fobj = @fitness; % Lower bound and upper bound GreyWolves_num=200; MaxIt=200; % Maximum Number of Iterations Archive_size=200; % Repository Size alpha=0.1; % Grid Inflation Parameter nGrid=10; % Number of Grids per each Dimension beta=4; %=4; % Leader Selection Pressure Parameter gamma=2; % Extra (to be deleted) Repository Member Selection Pressure % Initialization GreyWolves=CreateEmptyParticle(GreyWolves_num); for i=1:GreyWolves_num GreyWolves(i).Velocity=0; GreyWolves(i).Position=zeros(1,nVar); for j=1:nVar GreyWolves(i).Position(1,j)=unifrnd(Lb(j),Ub(j),1); end GreyWolves(i).Cost=fobj(GreyWolves(i).Position); GreyWolves(i).Best.Position=GreyWolves(i).Position; GreyWolves(i).Best.Cost=GreyWolves(i).Cost; end GreyWolves=DetermineDomination(GreyWolves); Archive=GetNonDominatedParticles(GreyWolves); Archive_costs=GetCosts(Archive); G=CreateHypercubes(Archive_costs,nGrid,alpha); for i=1:numel(Archive) [Archive(i).GridIndex, Archive(i).GridSubIndex]=GetGridIndex(Archive(i),G); end % MOGWO main loop for it=1:MaxIt a=2-it*((2)/MaxIt); for i=1:GreyWolves_num clear rep2 clear rep3 % Choose the alpha, beta, and delta grey wolves Delta=SelectLeader(Archive,beta); Beta=SelectLeader(Archive,beta); Alpha=SelectLeader(Archive,beta); % If there are less than three solutions in the least crowded % hypercube, the second least crowded hypercube is also found % to choose other leaders from. if size(Archive,1)>1 counter=0; for newi=1:size(Archive,1) if sum(Delta.Position~=Archive(newi).Position)~=0 counter=counter+1; rep2(counter,1)=Archive(newi); end end Beta=SelectLeader(rep2,beta); end % This scenario is the same if the second least crowded hypercube % has one solution, so the delta leader should be chosen from the % third least crowded hypercube. if size(Archive,1)>2 counter=0; for newi=1:size(rep2,1) if sum(Beta.Position~=rep2(newi).Position)~=0 counter=counter+1; rep3(counter,1)=rep2(newi); end end Alpha=SelectLeader(rep3,beta); end % Eq.(3.4) in the paper c=2.*rand(1, nVar); % Eq.(3.1) in the paper D=abs(c.*Delta.Position-GreyWolves(i).Position); % Eq.(3.3) in the paper A=2.*a.*rand(1, nVar)-a; % Eq.(3.8) in the paper X1=Delta.Position-A.*abs(D); % Eq.(3.4) in the paper c=2.*rand(1, nVar); % Eq.(3.1) in the paper D=abs(c.*Beta.Position-GreyWolves(i).Position); % Eq.(3.3) in the paper A=2.*a.*rand()-a; % Eq.(3.9) in the paper X2=Beta.Position-A.*abs(D); % Eq.(3.4) in t