ED_MOALO.rar_unhappyqke_多目标_多目标优化_电力优化_电力调度

%_________________________________________________________________________% % Multi-Objective Ant Lion Optimizer (MALO) source codes % %_________________________________________________________________________% clc; clear; close all; warning off; % Change these details with respect to your problem%%%%%%%%%%%%%% % This program solves the economic dispatch with Bmn coefficients by MOALO % Algorithm % The data matrix should have 5 columns of fuel cost coefficients and plant limits. % 1.a ($/MW^2) 2. b $/MW 3. c ($) 4.lower lomit(MW) 5.Upper limit(MW) %no of rows denote the no of plants(n) data=[0.007 7 240 100 500 0.0095 10 200 50 200 0.009 8.5 220 80 300 0.009 11 200 50 150 0.008 10.5 220 50 200 0.0075 12 120 50 120]; % Loss coefficients it should be squarematrix of size nXn where n is the no % of plants B=1e-4*[0.14 0.17 0.15 0.19 0.26 0.22 0.17 0.6 0.13 0.16 0.15 0.2 0.15 0.13 0.65 0.17 0.24 0.19 0.19 0.16 0.17 0.71 0.3 0.25 0.26 0.15 0.24 0.3 0.69 0.32 0.22 0.2 0.19 0.25 0.32 0.85 ]; % Demand (MW) Pd=700; ObjectiveFunction=@eldnba; dim=length(data(:,1));; lb=0; ub=1; obj_no=1; if size(ub,2)==1 ub=ones(1,dim)*ub; lb=ones(1,dim)*lb; end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Initial parameters of the MODA algorithm max_iter=100; N=100; ArchiveMaxSize=100; Archive_X=zeros(100,dim); Archive_F=ones(100,obj_no)*inf; Archive_member_no=0; r=(ub-lb)/2; V_max=(ub(1)-lb(1))/10; Elite_fitness=inf*ones(1,obj_no); Elite_position=zeros(dim,1); Ant_Position=initialization(N,dim,ub,lb); fitness=zeros(N,2); V=initialization(N,dim,ub,lb); iter=0; position_history=zeros(N,max_iter,dim); for iter=1:max_iter for i=1:N %Calculate all the objective values first Particles_F(i,:)=ObjectiveFunction(Ant_Position(:,i)'); if dominates(Particles_F(i,:),Elite_fitness) Elite_fitness=Particles_F(i,:); Elite_position=Ant_Position(:,i); end end [Archive_X, Archive_F, Archive_member_no]=UpdateArchive(Archive_X, Archive_F, Ant_Position, Particles_F, Archive_member_no); if Archive_member_no>ArchiveMaxSize Archive_mem_ranks=RankingProcess(Archive_F, ArchiveMaxSize, obj_no); [Archive_X, Archive_F, Archive_mem_ranks, Archive_member_no]=HandleFullArchive(Archive_X, Archive_F, Archive_member_no, Archive_mem_ranks, ArchiveMaxSize); else Archive_mem_ranks=RankingProcess(Archive_F, ArchiveMaxSize, obj_no); end Archive_mem_ranks=RankingProcess(Archive_F, ArchiveMaxSize, obj_no); % Chose the archive member in the least population area as arrtactor % to improve coverage index=RouletteWheelSelection(1./(Archive_mem_ranks+1e-20)); if index==-1 index=1; end Elite_fitness=Archive_F(index,:); Elite_position=Archive_X(index,:)'; Random_antlion_fitness=Archive_F(1,:); Random_antlion_position=Archive_X(1,:)'; for i=1:N index=0; neighbours_no=0; RA=Random_walk_around_antlion(dim,max_iter,lb,ub, Random_antlion_position',iter); [RE]=Random_walk_around_antlion(dim,max_iter,lb,ub, Elite_position',iter); Ant_Position(:,i)=(RE(iter,:)'+RA(iter,:)')/2; Flag4ub=Ant_Position(:,i)>ub'; Flag4lb=Ant_Position(:,i)<lb'; Ant_Position(:,i)=(Ant_Position(:,i).*(~(Flag4ub+Flag4lb)))+ub'.*Flag4ub+lb'.*Flag4lb; end display(['At the iteration ', num2str(iter), ' there are ', num2str(Archive_member_no), ' non-dominated solutions in the archive']); K(iter)=Elite_fitness; end [F P Pl]=eldnba(Elite_position) plot(K) grid title('Iteration vs Best Function Value'); xlabel('Iteration') ylabel('Function Value')