MOPSO多目标粒子群优化算法MATLAB实现.rar_粒子群目标_studyingq77_粒子群多目标_粒子群算法_mopso

% ackley.m % Ackley's function, from http://www.cs.vu.nl/~gusz/ecbook/slides/16 % and further shown at: % http://clerc.maurice.free.fr/pso/Semi-continuous_challenge/Semi-continuous_challenge.htm % % commonly used to test optimization/global minimization problems % % f(x)= [ 20 + e ... % -20*exp(-0.2*sqrt((1/n)*sum(x.^2,2))) ... % -exp((1/n)*sum(cos(2*pi*x),2))]; % % dimension n = # of columns of input, x1, x2, ..., xn % each row is processed independently, % you can feed in matrices of timeXdim no prob % % example: cost = ackley([1,2,3;4,5,6]) function [Y]=ackley(x,flag) % global P global T % x=mapminmax('apply',x(1,:)',P); %1X5 % x=x'; W2=[0.983513649425645,0.0156395494399536,0.612578690108221,0.348949772433000,0.266166079005764,0.124947353178640,0.923296305479215,-0.530232046763045,-0.327659275580791,-0.646199831676460;-0.309231346225098,0.596414396236188,0.397002323830802,0.206433928667196,0.720492746343545,0.199389937406352,-0.116765991949494,-0.370247577740941,-0.154903175718849,-0.507975216600424;0.128681017244323,-0.524317286229423,0.186056741679819,0.555730447171981,0.531275865167625,0.182558138013602,0.384961025796518,0.385276782693936,0.605236026395329,0.456005242346597]; W1=[-0.869936793889867,0.325779285347892,0.174037972470458,0.295236135069579,-0.273241174515210;-0.558186480531010,-0.186696321927505,0.562936588956597,0.974180561609398,0.674207406347660;0.155857579269969,-0.265768777238990,-0.874132188809125,-0.0827875095457866,0.370921943805725;0.0131249090186139,-0.201982618964050,0.519301705908160,-0.863972727171152,0.987929292098583;0.433834177489895,0.886924527100349,0.274107586179760,-0.303859698977215,0.422001002322857;-0.371103364530077,-0.560145560430240,-0.825019796560491,0.661826279134213,-0.310314984764697;-0.170958734188943,-0.423544695342573,0.691412124257754,-0.329984037233651,0.0755035626115093;0.136436814280577,-0.0655486161253700,0.0170589401427046,-0.423034172683370,-0.665773389641811;0.413456404045930,-0.682126171802981,0.517447538050501,-0.783398569576221,0.650999262711329;-0.733753096788900,0.0503955169192474,0.697086294190505,-0.636672620606997,0.121973937413338]; B1=[0.263524090679658;0.619752887788955;-0.323729827867708;0.820582965938063;0.392485096567817;-0.966033068034410;0.228122237322837;0.0332417952094948;-0.184288988372871;0.841419482048720]; B2=[0.222608902861025;-0.0339919509291149;-0.511468813194705]; F(:,1)=W2(1,:)*(2./(1+exp(-2*(W1*x(1,:)'+B1)))-1)+B2(1); F(:,2)=W2(2,:)*(2./(1+exp(-2*(W1*x(1,:)'+B1)))-1)+B2(2); F(:,3)=W2(3,:)*(2./(1+exp(-2*(W1*x(1,:)'+B1)))-1)+B2(3); f(1,:)=mapminmax('reverse',F(1,:)',T); %1X5 if flag==0 Y=norm(F); else Y=f; end