【优化求解】一种非线性动态自适应惯性权重PSO算法(IPSO)Matlab代码

一种非线性动态自适应惯性权重PSO算法(IPSO)Matlab代码

[1]王生亮,刘根友.一种非线性动态自适应惯性权重PSO算法[J].计算机仿真,2021,38(04):249-253&＃43;451.

部分代码&＃xff1a;

function [gbest,gbestval,fitcount]&＃61; CLPSO_new_func(fhd,Max_Gen,Max_FES,Particle_Number,Dimension,VRmin,VRmax,varargin)
%[gbest,gbestval,fitcount]&＃61; CLPSO_new_func(&＃39;f8&＃39;,3500,200000,30,30,-5.12,5.12)
rand(&＃39;state&＃39;,sum(100*clock));
me&＃61;Max_Gen;
ps&＃61;Particle_Number;
D&＃61;Dimension;
cc&＃61;[1 1]; %acceleration constants
t&＃61;0:1/(ps-1):1;t&＃61;5.*t;
Pc&＃61;0.0&＃43;(0.5-0.0).*(exp(t)-exp(t(1)))./(exp(t(ps))-exp(t(1)));
% Pc&＃61;0.5.*ones(1,ps);
m&＃61;0.*ones(ps,1);
iwt&＃61;0.9-(1:me)*(0.7/me);
% iwt&＃61;0.729-(1:me)*(0.0/me);
cc&＃61;[1.49445 1.49445];
if length(VRmin)&＃61;&＃61;1VRmin&＃61;repmat(VRmin,1,D);VRmax&＃61;repmat(VRmax,1,D);
end
mv&＃61;0.2*(VRmax-VRmin);
VRmin&＃61;repmat(VRmin,ps,1);
VRmax&＃61;repmat(VRmax,ps,1);
Vmin&＃61;repmat(-mv,ps,1);
Vmax&＃61;-Vmin;
pos&＃61;VRmin&＃43;(VRmax-VRmin).*rand(ps,D);for i&＃61;1:ps;e(i,1)&＃61;feval(fhd,pos(i,:),varargin{:});
endfitcount&＃61;ps;
vel&＃61;Vmin&＃43;2.*Vmax.*rand(ps,D);%initialize the velocity of the particles
pbest&＃61;pos;
pbestval&＃61;e; %initialize the pbest and the pbest&＃39;s fitness value
[gbestval,gbestid]&＃61;min(pbestval);
gbest&＃61;pbest(gbestid,:);%initialize the gbest and the gbest&＃39;s fitness value
gbestrep&＃61;repmat(gbest,ps,1);stay_num&＃61;zeros(ps,1);ai&＃61;zeros(ps,D);
f_pbest&＃61;1:ps;f_pbest&＃61;repmat(f_pbest&＃39;,1,D);
for k&＃61;1:psar&＃61;randperm(D);ai(k,ar(1:m(k)))&＃61;1;fi1&＃61;ceil(ps*rand(1,D));fi2&＃61;ceil(ps*rand(1,D));fi&＃61;(pbestval(fi1)<pbestval(fi2))&＃39;.*fi1&＃43;(pbestval(fi1)>&＃61;pbestval(fi2))&＃39;.*fi2;bi&＃61;ceil(rand(1,D)-1&＃43;Pc(k));if bi&＃61;&＃61;zeros(1,D),rc&＃61;randperm(D);bi(rc(1))&＃61;1;endf_pbest(k,:)&＃61;bi.*fi&＃43;(1-bi).*f_pbest(k,:);
endstop_num&＃61;0;
i&＃61;1;while i<&＃61;me&fitcount<&＃61;Max_FESi&＃61;i&＃43;1;for k&＃61;1:psif stay_num(k)>&＃61;5% if round(i/10)&＃61;&＃61;i/10%|stay_num(k)>&＃61;5stay_num(k)&＃61;0;ai(k,:)&＃61;zeros(1,D);f_pbest(k,:)&＃61;k.*ones(1,D);ar&＃61;randperm(D);ai(k,ar(1:m(k)))&＃61;1;fi1&＃61;ceil(ps*rand(1,D));fi2&＃61;ceil(ps*rand(1,D));fi&＃61;(pbestval(fi1)<pbestval(fi2))&＃39;.*fi1&＃43;(pbestval(fi1)>&＃61;pbestval(fi2))&＃39;.*fi2;bi&＃61;ceil(rand(1,D)-1&＃43;Pc(k));if bi&＃61;&＃61;zeros(1,D),rc&＃61;randperm(D);bi(rc(1))&＃61;1;endf_pbest(k,:)&＃61;bi.*fi&＃43;(1-bi).*f_pbest(k,:);endfor dimcnt&＃61;1:Dpbest_f(k,dimcnt)&＃61;pbest(f_pbest(k,dimcnt),dimcnt);endaa(k,:)&＃61;cc(1).*(1-ai(k,:)).*rand(1,D).*(pbest_f(k,:)-pos(k,:))&＃43;cc(2).*ai(k,:).*rand(1,D).*(gbestrep(k,:)-pos(k,:));%~~~~~~~~~~~~~~~~~~~~~~vel(k,:)&＃61;iwt(i).*vel(k,:)&＃43;aa(k,:);vel(k,:)&＃61;(vel(k,:)>mv).*mv&＃43;(vel(k,:)<&＃61;mv).*vel(k,:);vel(k,:)&＃61;(vel(k,:)<(-mv)).*(-mv)&＃43;(vel(k,:)>&＃61;(-mv)).*vel(k,:);pos(k,:)&＃61;pos(k,:)&＃43;vel(k,:);if (sum(pos(k,:)>VRmax(k,:))&＃43;sum(pos(k,:)<VRmin(k,:)))&＃61;&＃61;0;e(k,1)&＃61;feval(fhd,pos(k,:),varargin{:});fitcount&＃61;fitcount&＃43;1;tmp&＃61;(pbestval(k)<&＃61;e(k));if tmp&＃61;&＃61;1stay_num(k)&＃61;stay_num(k)&＃43;1;endtemp&＃61;repmat(tmp,1,D);pbest(k,:)&＃61;temp.*pbest(k,:)&＃43;(1-temp).*pos(k,:);pbestval(k)&＃61;tmp.*pbestval(k)&＃43;(1-tmp).*e(k);%update the pbestif pbestval(k)<gbestvalgbest&＃61;pbest(k,:);gbestval&＃61;pbestval(k);gbestrep&＃61;repmat(gbest,ps,1);%update the gbestendendend% if round(i/100)&＃61;&＃61;i/100% plot(pos(:,D-1),pos(:,D),&＃39;b*&＃39;);hold on;% for k&＃61;1:floor(D/2)% plot(gbest(:,2*k-1),gbest(:,2*k),&＃39;r*&＃39;);% end% hold off% title([&＃39;PSO: &＃39;,num2str(i),&＃39; generations, Gbestval&＃61;&＃39;,num2str(gbestval)]);% axis([VRmin(1,D-1),VRmax(1,D-1),VRmin(1,D),VRmax(1,D)])% drawnow% endif fitcount>&＃61;Max_FESbreak;endif (i&＃61;&＃61;me)&(fitcount<Max_FES)i&＃61;i-1;end
end
gbestval