粒子群算法来求16个经典函数的最小最大值matlab源码程序 - matlab算法设计

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标题：粒子群算法来求16个经典函数的最小最大值matlab源码程序
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所属分类: 算法设计资源类型：程序源码文件大小: 736.99 KB 上传时间: 2019-06-16 21:50:19 下载次数: 49 资源积分：1分提供者: zhangsan456 code

内容：
此程序包是用粒子群算法来求16个经典函数的最小最大值，其中界面友好，运行时会出现动态二维图来展现粒子群是如何运动来求最值的

clear all

close all

clc

help demopsobehavior

warning off

functnames = {'ackley','alpine','DeJong_f2','DeJong_f3','DeJong_f4',...

'Foxhole','Griewank','NDparabola',...

'Rastrigin','Rosenbrock','f6','f6mod','tripod',...

'f6_bubbles_dyn','f6_linear_dyn','f6_spiral_dyn'};

disp('Static test functions, minima don''t change w.r.t. time/iteration:');

disp(' 1) Ackley');

disp(' 2) Alpine');

disp(' 3) DeJong_f2');

disp(' 4) DeJong_f3');

disp(' 5) DeJong_f4');

disp(' 6) Foxhole');

disp(' 7) Griewank');

disp(' 8) NDparabola (for this demo N = 2)');

disp(' 9) Rastrigin');

disp('10) Rosenbrock');

disp('11) Schaffer f6');

disp('12) Schaffer f6 modified (5 f6 functions translated from each other)');

disp('13) Tripod');

disp(' ');

disp('Dynamic test functions, minima/environment evolves over time/iteration:');

disp('14) f6_bubbles_dyn');

disp('15) f6_linear_dyn');

disp('16) f6_spiral_dyn');

functchc=input('Choose test function ? ');

functname = functnames{functchc};

disp(' ');

disp('1) Intense graphics, shows error topology and surfing particles');

disp('2) Default PSO graphing, shows error trend and particle dynamics');

disp('3) no plot, only final output shown, fastest');

plotfcn=input('Choose plotting function ? ');

if plotfcn == 1

plotfcn = 'goplotpso4demo';

shw = 1; % how often to update display

elseif plotfcn == 2

plotfcn = 'goplotpso';

shw = 1; % how often to update display

else

plotfcn = 'goplotpso';

shw = 0; % how often to update display

end

% set flag for 'dynamic function on', only used at very end for tracking plots

dyn_on = 0;

if functchc==15 | functchc == 16 | functchc == 17

dyn_on = 1;

end

%xrng=input('Input search range for X, e.g. [-10,10] ? ');

%yrng=input('Input search range for Y ? ');

xrng=[-30,30];

yrng=[-40,40];

disp(' ');

% if =0 then we look for minimum, =1 then max

disp('0) Minimize')

disp('1) Maximize')

minmax=input('Choose search goal ?');

% minmax=0;

disp(' ');

mvden = input('Max velocity divisor (2 is a good choice) ? ');

disp(' ');

ps = input('How many particles (24 - 30 is common)? ');

disp(' ');

disp('0) Common PSO - with inertia');

disp('1) Trelea model 1');

disp('2) Trelea model 2');

disp('3) Clerc Type 1" - with constriction');

modl = input('Choose PSO model ? ');

% note: if errgoal=NaN then unconstrained min or max is performed

if minmax==1

% errgoal=0.97643183; % max for f6 function (close enough for termination)

errgoal=NaN;

else

% errgoal=0; % min

errgoal=NaN;

end

minx = xrng(1);

maxx = xrng(2);

miny = yrng(1);

maxy = yrng(2);

%--------------------------------------------------------------------------

dims=2;

varrange=[];

mv=[];

for i=1:dims

varrange=[varrange;minx maxx];

mv=[mv;(varrange(i,2)-varrange(i,1))/mvden];

end

ac = [2.1,2.1];% acceleration constants, only used for modl=0

Iwt = [0.9,0.6]; % intertia weights, only used for modl=0

epoch = 400; % max iterations

wt_end = 100; % iterations it takes to go from Iwt(1) to Iwt(2), only for modl=0

errgrad = 1e-99; % lowest error gradient tolerance

errgraditer=100; % max # of epochs without error change >= errgrad

PSOseed = 0; % if=1 then can input particle starting positions, if= 0 then all random

% starting particle positions (first 20 at zero, just for an example)

PSOseedValue = repmat([0],ps-10,1);

psoparams=...

[shw epoch ps ac(1) ac(2) Iwt(1) Iwt(2) ...

wt_end errgrad errgraditer errgoal modl PSOseed];

% run pso

% vectorized version

[pso_out,tr,te]=pso_Trelea_vectorized(functname, dims,...

mv, varrange, minmax, psoparams,plotfcn,PSOseedValue);

%--------------------------------------------------------------------------

% display best params, this only makes sense for static functions, for dynamic

% you'd want to see a time history of expected versus optimized global best

% values.

disp(' ');

disp(['Best fit parameters: ']);

disp([' cost = ',functname,'( [ input1, input2 ] )']);

disp(['---------------------------------']);

disp([' input1 = ',num2str(pso_out(1))]);

disp([' input2 = ',num2str(pso_out(2))]);

disp([' cost = ',num2str(pso_out(3))]);

disp([' mean cost = ',num2str(mean(te))]);

disp([' # of epochs = ',num2str(tr(end))]);

%% optional, save picture

%set(gcf,'InvertHardcopy','off');

%print -d

%print('-djpeg',['demoPSOBehavior.jpg']);

文件列表（点击上边下载按钮，如果是垃圾文件请在下面评价差评或者投诉）:

关键词：粒子群算法

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关键词： 粒子群算法

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关键词：粒子群算法