猫群群算法解决优化问题 （cat optimization）

猫群群算法解决优化问题，比遗传算法和粒子群算法更优

clc;
close all;
num=2;     
MaxIt=200;  % Maximum Number of Iterations
nPop=50; 
%% Algorithm Parameters BINARY CAT 2013    
tb=10;
bitt=20;
nVar=bitt*tb; 
BestCost1_cat=zeros（numMaxIt）;  
CostFunction=@（xtbbitt） cost_function（xtbbitt）;   % Cost Function
c2_cat=1;
for ittt=1:num
    for ta=1:1 
            %  Number of Decision Variables
            alpha=0.3;
            VarSize=[1 nVar];   % Decision Variables Matrix Size
            %% PSO Parameters
            SMP=3;%0.25*nPop;
            SRD=0.2;
            CDC=0.2;
            nb=round（nVar*CDC）;        
            MR=0.3;
            num_seek=round（MR*nPop）;
            num_track=nPop-num_seek;
            cat=randperm（nPop）;
            w_cat=0.5;
            vmax_cat=4;
            %********************************
            %% Initialization
            % Define Empty Structure to Hold Particle Data
            empty_cat.Position=[];
            empty_cat.flag=[];
            empty_cat.Velocity=[];
            empty_cat.Cost=[];
            pop=repmat（empty_catnPop1）;
            vel_cat=rand（nPopnVar）-0.5;
            one_vel_cat=rand（nPopnVar）-0.5;
            zero_vel_cat=rand（nPopnVar）-0.5;
            % Initialize Global Best
            GlobalBest.Cost=inf;
            for i=1:nPop
                % Initialize Velocity
                pop（i）.Position = round（rand（1nVar））;
                pop（i）.Velocity = rand（1nVar）;
                % Evaluate Solution
                pop（i）.Cost=CostFunction（pop（i）.Positiontbbitt）; 
                y=find（cat==i）;
                if（y<=num_seek）
                    pop（i）.flag=1;
                else
                    pop（i）.flag=0;
                end
                % Update Global Best
                if pop（i）.Cost<=GlobalBest.Cost
                    GlobalBest=pop（i）;
                end
            end
            % Define Array to Hold Best Cost Values
            BestCost=zeros（MaxIt1）;
            c1=1;
            %% PSO Main Loop
            for it=1:MaxIt
                    oneadd_cat=zeros（nPopnVar）;
                    zeroadd_cat=zeros（nPopnVar）;
                    dd3_cat=c2_cat*rand;
                    %******************************************************
                    for t_i=1:nPop
                        for g_i=1:nVar
                            if（GlobalBest.Position（g_i）==0）
                               oneadd_cat（t_ig_i）=oneadd_cat（t_ig_i）-dd3_cat;
                               zeroadd_cat（t_ig_i）=zeroadd_cat（t_ig_i）+dd3_cat;
                            else
                               oneadd_cat（t_ig_i）=oneadd_cat（t_ig_i）+dd3_cat; 
                               zeroadd_cat（t_ig_i）=zeroadd_cat（t_ig_i）-dd3_cat;
                            end
                        end
                    end
                    one_vel_cat=w_cat*one_vel_cat+oneadd_cat;
                    zero_vel_cat=w_cat*z

 属性            大小     日期    时间   名称
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     文件       6616  2014-02-12 14:39  binary_cat_optimization\binary_cat_optimization.m

     文件        366  2014-02-12 14:39  binary_cat_optimization\cost_function.m

     文件        168  2014-10-19 21:46  binary_cat_optimization\f.m

     文件        141  2014-10-19 21:46  binary_cat_optimization\fitness.m

     文件       1018  2014-02-12 14:39  binary_cat_optimization\mutate.m

     文件        136  2014-02-12 14:39  binary_cat_optimization\mybin2dec.m

     文件        105  2014-02-12 14:39  binary_cat_optimization\RouletteWheelSelection.m

     目录          0  2014-10-20 11:00  binary_cat_optimization

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