【优化算法】世界杯优化算法（WCOA）【含Matlab源码 1427期】

一、获取代码方式

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二、部分源代码

function y=my_cost_function(x)
% y=1/x;
% y=x(1)+x(2)+x(3)
% y=sum(x.^2-10*cos(2*pi*x)+10);
% y=sum(-x(1).*sin(sqrt(abs(x(2)))));
% y=abs(sum(abs(x.^2+x).*sin(x)));
% y=sum(x.^2);
% y=x^4+5*x^3+6*x^2+7;
% y=sum(x.^2-10*cos(2*pi*x)+10);
% y=-((sin(x(1)).*(sin(x(1)^2/pi))^(20)+sin(x(2)).*(sin(2*x(2)^2/pi))^(20)));
% y = [1+(x(1)+x(2)+1).^2*(19-14*x(1)+3*x(1).^2-14*x(2)+6*x(1).*x(2)+3*x(2).^2)].*...
%             [30+(2*x(1)-3*x(2)).^2*(18-32*x(1)+12*x(1).^2+48*x(2)-36*x(1).*(2)+27*x(2).^2)];
%%
% y = (x(1).^2 + x(2) - 11).^2 + (x(1) + x(2).^2 - 7).^2; %himmelblau
% y=(1+(x(1)+x(2)+1).^2.*(19-14*x(1)+3*x(1).^2-14*x(2)+6*x(1).*x(2)+3*x(2).^2)).*...
%             (30+(2*x(1)-3*x(2)).^2.*(18-32*x(1)+12*x(1).^2 +48*x(2)-36*x(1).*x(2)+27*x(2).^2)); %goldstein & price
y = -cos(x(1)).*cos(x(2)).*exp(-((x(1)-pi).^2 + (x(2)-pi).^2));  % easom
% y=x(1).^2 + x(2).^2 - 10*cos(2*pi*x(1)) - 10*cos(2*pi*x(2)) + 20;% rastrigin
% y=100*(x(2) - x(1).^2).^2 + (1 - x(1)).^2;  %rosenbruck
% y=-((sin(x(1)).*(sin(x(1)^2/pi))^(20)+sin(x(2)).*(sin(2*x(2)^2/pi))^(20)));  % beale
% y=(x(1) + 2*x(2) - 7).^2 + (2*x(1) + x(2) - 5).^2; %booth
% y=100*(x(2) - 0.01*x(1).^2 + 1) + 0.01*(x(1) + 10).^2;  %bukin2
% y=-((cos(x(1)).*cos(x(2)).*exp(abs(1 - sqrt(x(1).^2 + x(2).^2)/pi))).^2)/30;
% y=x(1).^2 - 12*x(1) + 11 + 10*cos(pi*x(1)/2) + 8*sin(5*pi*x(1)/2) - ...
%             1/sqrt(5)*exp(-((x(2) - 0.5).^2)/2);  %chichinzade
% y = 0.6 + sum(sin(16*x/15 - 1) + sin(16*x/15 - 1).^2 + sin(4*16*x/15 - 1)/50, 2); %giunta
% y=(abs(sin(x(1)).*sin(x(2)).*exp(abs(100 - sqrt(x(1).^2 + x(2).^2)/pi))) + 1).^(-0.1);  %crossfun
% y=-0.0001*(abs(sin(x(1)).*sin(x(2)).*exp(abs(100 - sqrt(x(1).^2 + x(2).^2)/pi))) + 1).^(0.1);  %crossintary
% y=-(abs(sin((x(1))).*sin((x(2))).*exp(abs(100 - sqrt((x(1)).^2 + (x(2)).^2)/pi))) + 1).^(-0.1);  %crosslegtable
% y = (x(1).^2 + x(2).^2)/200 - cos(x(1)).*cos(x(2)/sqrt(2)) + 1;%griewank
% y=-abs(sin(x(1)).*cos(x(2)).*exp(abs(1 - sqrt(x(1).^2 + x(2).^2)/pi)));   %holdertable
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%  Portion of MATLAB Code for                                   %
%                                                               %
%    World Cup Optimization Algorithm (WCO)                     %
%            %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% ! Caution
%  This program is written for minimizing the ERROR; for different Uses abs
%  command should be elliminater from column 128 to column 156
%% ---------------------------Function-------------------------------------
%1.wca                       Main Function
%2.my_cost_function          Fitness (Cost) Function%% ------------------INITIALIZATION OF PARAMETERS--------------------------
commandwindow
clear all;
close all;
clc;
disp('**************************************        ')
disp('   World Cup Optimization Algorithm    ')
disp('**************************************        ')
tic
warning('off')
itr=0;
iter=100;                      %no_competition_periods
no_par=2;
% countries=4;
% continents=5;
% popsize=countries*continents;
popsize=50;                   % popsize=no_Rank
ra=3;                         % Percentage of random countries rise as play-off teams [0 6]
co=rand;
ur=10;                       % Up countries rating
dr=round(co*10);               % Down countries rating
D=10;                          %constant
alpha=.9;                      %exploration_coefficient in the range [0 1]_0 gives Max exploitation & 1 give max exploration
UB=10*ones(no_par,popsize);     %Upward bound condition
LB=-10*zeros(no_par,popsize);   %Downward bound condition%% -----------MAIN PROGRAM STARTS------------------------------------------% ------------Initializing--6 countries ----------------------------------- local_best_fitness1=inf;
local_best_fitness2=inf;
local_best_fitness3=inf;
local_best_fitness4=inf;
local_best_fitness5=inf;
local_best_fitness6=inf;for i=1:no_parfor j=1:popsize        current_position1(i,j)=(UB(i)-LB(i))*rand+LB(i);endendfor i=1:no_parfor j=1:popsizecurrent_position2(i,j)=(UB(i)-LB(i))*rand+LB(i);endendfor i=1:no_parfor j=1:popsizecurrent_position3(i,j)=(UB(i)-LB(i))*rand+LB(i);endendfor i=1:no_parfor j=1:popsizecurrent_position4(i,j)=(UB(i)-LB(i))*rand+LB(i);endendfor i=1:no_parfor j=1:popsizecurrent_position5(i,j)=(UB(i)-LB(i))*rand+LB(i);endendfor i=1:no_parfor j=1:popsizecurrent_position6(i,j)=(UB(i)-LB(i))*rand+LB(i);endend% --------------------Evaluate initial population--------------------------while itr<iter
itr=itr+1;for jj=1:popsizecurrent_fitness1(jj) = my_cost_function(current_position1(:,jj));    endfor jj=1:popsizecurrent_fitness2(jj) = my_cost_function(current_position2(:,jj));    endfor jj=1:popsizecurrent_fitness3(jj) = my_cost_function(current_position3(:,jj));    endfor jj=1:popsizecurrent_fitness4(jj) = my_cost_function(current_position4(:,jj));    endfor jj=1:popsizecurrent_fitness5(jj) = my_cost_function(current_position5(:,jj));    endfor jj=1:popsizecurrent_fitness6(jj) = my_cost_function(current_position6(:,jj));    endif abs(min(current_fitness1))<abs(min(local_best_fitness1))local_best_fitness1=current_fitness1;local_best_position1=current_position1;
endif abs(min(current_fitness2))<abs(min(local_best_fitness2))local_best_fitness2=current_fitness2;local_best_position2=current_position2;
endif abs(min(current_fitness3))<abs(min(local_best_fitness3))local_best_fitness3=current_fitness3;local_best_position3=current_position3;
endif abs(min(current_fitness4))<abs(min(local_best_fitness4))local_best_fitness4=current_fitness4;local_best_position4=current_position4;
endif abs(min(current_fitness5))<abs(min(local_best_fitness5))local_best_fitness5=current_fitness5;local_best_position5=current_position5;
endif abs(min(current_fitness6))<abs(min(local_best_fitness6))local_best_fitness6=current_fitness6;local_best_position6=current_position6;
endl_b_p=[local_best_position1 local_best_position2 local_best_position3 local_best_position4 local_best_position5 local_best_position6];
l_b_f=[local_best_fitness1;local_best_fitness2;local_best_fitness3;local_best_fitness4;local_best_fitness5;local_best_fitness6];        % ----Evaluate best fittness , position-&-Sorting-for-the next-play-off----[global_best_fitness1,g1] = min(local_best_fitness1);
[global_best_fitness2,g2] = min(local_best_fitness2);
[global_best_fitness3,g3] = min(local_best_fitness3);
[global_best_fitness4,g4] = min(local_best_fitness4);
[global_best_fitness5,g5] = min(local_best_fitness5);
[global_best_fitness6,g6] = min(local_best_fitness6);f_b_f=[global_best_fitness1 global_best_fitness2 global_best_fitness3 global_best_fitness4 global_best_fitness5 global_best_fitness6];
[final_best_fitness,no]= min(f_b_f);
% final_best_fitnessglobal_best_position1 = local_best_position1(:,g1);
global_best_position2 = local_best_position2(:,g2);
global_best_position3 = local_best_position3(:,g3);
global_best_position4 = local_best_position4(:,g4);
global_best_position5 = local_best_position5(:,g5);
global_best_position6 = local_best_position6(:,g6);f_b_p=[global_best_position1 global_best_position2 global_best_position3 global_best_position4 global_best_position5 global_best_position6];
final_best_position=f_b_p(:,no);                                                                        %%%%%%%%%%%%%[sort_best_fitness1]= sort(local_best_fitness1,'descend');
[sort_best_fitness2]= sort(local_best_fitness2,'descend');
[sort_best_fitness3]= sort(local_best_fitness3,'descend');
[sort_best_fitness4]= sort(local_best_fitness4,'descend');
[sort_best_fitness5]= sort(local_best_fitness5,'descend');
[sort_best_fitness6]= sort(local_best_fitness6,'descend');
s_b_f=[sort_best_fitness1;sort_best_fitness2;sort_best_fitness3;sort_best_fitness4;sort_best_fitness5;sort_best_fitness6];pf1=find(local_best_fitness1==sort_best_fitness1(end));
pf2=find(local_best_fitness2==sort_best_fitness2(end));
pf3=find(local_best_fitness3==sort_best_fitness3(end));
pf4=find(local_best_fitness4==sort_best_fitness4(end));
pf5=find(local_best_fitness5==sort_best_fitness5(end));
pf6=find(local_best_fitness6==sort_best_fitness6(end));%----------Rating----------------------------------------------------------
s=abs(std((l_b_f)'));           % Standard deviation of the local best fitness
m=abs(min((l_b_f)'));           % Mean Value of the local best fitness
mm=(s+m)/2;                     % Rating
[so cn]=sort(mm);               % Sort & country number  ucn=cn(1:ra);                   % Up Country No.
dcn=cn(ra+1:end);               % Down Country No.n1=s_b_f(ucn,end-ra+1:end);
n2=s_b_f(dcn,end-ra+1:end);
n=[n1(:);n2(:)];if length(n)<popsize
t=popsize-length(n);
n3=s_b_f(1:t);n=[n1(:);n2(:);n3(:)];
else if length(n)>popsizen=n(1:popsize,:);end
end

三、运行结果

四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1] 包子阳,余继周,杨杉.智能优化算法及其MATLAB实例（第2版）[M].电子工业出版社，2016.
[2]张岩,吴水根.MATLAB优化算法源代码[M].清华大学出版社，2017.

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