基于迭代局部搜索和随机惯性权重的BA算法MATLAB实现（ILSSIWBA）

uperrua · 发表于 2020-5-14 10:12

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本帖最后由 uperrua 于 2020-5-14 10:16 编辑

主要思想

流程图

function [bestMin, bestID] = ILSSIWBA()
%A new bat algorithm based on iterative local search and stochastic inertia weight

%omegaxyz.com QQ: 644327005

clc;
%% 经典BA参数设置

t = 1;
maxT = 100; %最大迭代次数
dim = 30; %问题的维度
sizep = 50; %种群大小
xmin = -0.5;
xmax = 0.5; %位置向量的范围

A = 0.6.*ones(sizep,1); % 响度 (不变或者减小)
r = zeros(sizep,1);    % 脉冲率 (不变或增加))
Qmin = 0;       % 最小频率
Qmax = 1;       % 最大频率

%% 初始化

Lb = xmin*ones(1,dim);
Ub = xmax*ones(1,dim);
pop = Lb+(Ub-Lb).*rand(sizep,dim); %种群初始化
popv = zeros(sizep,dim); % 速度
Q = zeros(sizep,1); % 频率

pfitness = zeros(dim,1);
for i = 1:sizep
pfitness(i) = evaluate2(pop(i,: )); %评价
end
[bestMin, bestID]=min(pfitness);
bestS = pop(bestID, : );
bestArchive = zeros(maxT,1);

%% 论文中的新策略参数

umin = 0.4; %The second modification (SIW method)
umax = 0.9;
sigma = 0.2;

R0 = 0.1; %The 3th modification
Rupper = 0.7;
A0 = 0.9;
Alower = 0.6;

%% 具体迭代过程

while t <= maxT
for i = 1:sizep
      Q(i)=Qmin+(Qmin-Qmax)*rand();
      w = umin +(umax-umin)*rand()+sigma*randn(); %The second modification (SIW method)
      popv(i,: )=w*popv(i,: )+(pop(i,: )-bestS)*Q(i); %The second modification (SIW method)
      Stemp = pop(i, : )+popv(i,: );
      % 脉冲率
      if rand>r(i)
         Stemp=bestS-1+2*rand(1,dim);
      end
      fitTemp = evaluate2(Stemp);
      if (fitTemp<=pfitness(i))&&(rand()<A(i))
         pop(i,: ) = Stemp;
         pfitness(i) = fitTemp;
         A(i) = (A0-Alower)/(1-maxT)*(t-maxT)+Alower; %The 3th modification
         r(i) = (R0-Rupper)/(1-maxT)*(t-maxT)+Rupper; %The 3th modification
      end
      if fitTemp <= bestMin
         bestID = i;
         bestMin = fitTemp;
               bestS = Stemp;
      end
end

interX = bestS.*rand(); %The 1th modification (ILS method)
interXfit = evaluate2(interX);
if interXfit < bestMin
      bestMin = interXfit;
      bestS = interX;
      pop(bestID,: ) = bestS;
      pfitness(bestID,: ) = bestMin;
else
      if exp(bestMin-interXfit) > rand()
         bestMin = interXfit;
         bestS = interX;
         pop(bestID,: ) = bestS;
         pfitness(bestID,: ) = bestMin;
      end
end

bestArchive(t) = bestMin;
fprintf('GEN: %d  min: %.4f\n', t, bestMin);
t = t +1;
end

end%.m end

评价函数：

function z = evaluate2(u)
%Weierstrass

dim= length(u);
sum_1=0;
sum_2=0;
sum_3=0;
a = 0.5;
b = 3;
for i=1:dim
for k=0:30
   sum_1=sum_1+(a^k)*(cos(2*pi*(b^k)*(u(i)+0.5)));
end
sum_2=sum_2+sum_1;
sum_1 = 0;
end
for k=0:30
   sum_3=sum_3+(a^k)*(cos(2*pi*(b^k)*0.5));
end
z=sum_2-dim*sum_3;
end

流程图

function [bestMin, bestID] = ILSSIWBA()
%A new bat algorithm based on iterative local search and stochastic inertia weight

%omegaxyz.com QQ: 644327005

clc;
%% 经典BA参数设置

t = 1;
maxT = 100; %最大迭代次数
dim = 30; %问题的维度
sizep = 50; %种群大小
xmin = -0.5;
xmax = 0.5; %位置向量的范围

A = 0.6.*ones(sizep,1); % 响度 (不变或者减小)
r = zeros(sizep,1);    % 脉冲率 (不变或增加))
Qmin = 0;       % 最小频率
Qmax = 1;       % 最大频率

%% 初始化

Lb = xmin*ones(1,dim);
Ub = xmax*ones(1,dim);
pop = Lb+(Ub-Lb).*rand(sizep,dim); %种群初始化
popv = zeros(sizep,dim); % 速度
Q = zeros(sizep,1); % 频率

pfitness = zeros(dim,1);
for i = 1:sizep
pfitness(i) = evaluate2(pop(i,: )); %评价
end
[bestMin, bestID]=min(pfitness);
bestS = pop(bestID, : ) ;
bestArchive = zeros(maxT,1);

%% 论文中的新策略参数

umin = 0.4; %The second modification (SIW method)
umax = 0.9;
sigma = 0.2;

R0 = 0.1; %The 3th modification
Rupper = 0.7;
A0 = 0.9;
Alower = 0.6;

%% 具体迭代过程

while t <= maxT
for i = 1:sizep
      Q(i)=Qmin+(Qmin-Qmax)*rand();
      w = umin +(umax-umin)*rand()+sigma*randn(); %The second modification (SIW method)
      popv(i,: )=w*popv(i,: )+(pop(i,: )-bestS)*Q(i); %The second modification (SIW method)
      Stemp = pop(i,: )+popv(i,: );
      % 脉冲率
      if rand>r(i)
         Stemp=bestS-1+2*rand(1,dim);
      end
      fitTemp = evaluate2(Stemp);
      if (fitTemp<=pfitness(i))&&(rand()<A(i))
         pop(i,: ) = Stemp;
         pfitness(i) = fitTemp;
         A(i) = (A0-Alower)/(1-maxT)*(t-maxT)+Alower; %The 3th modification
         r(i) = (R0-Rupper)/(1-maxT)*(t-maxT)+Rupper; %The 3th modification
      end
      if fitTemp <= bestMin
         bestID = i;
         bestMin = fitTemp;
               bestS = Stemp;
      end
end

interX = bestS.*rand(); %The 1th modification (ILS method)
interXfit = evaluate2(interX);
if interXfit < bestMin
      bestMin = interXfit;
      bestS = interX;
      pop(bestID,: ) = bestS;
      pfitness(bestID,: ) = bestMin;
else
      if exp(bestMin-interXfit) > rand()
         bestMin = interXfit;
         bestS = interX;
         pop(bestID,: ) = bestS;
         pfitness(bestID,: ) = bestMin;
      end
end

bestArchive(t) = bestMin;
fprintf('GEN: %d  min: %.4f\n', t, bestMin);
t = t +1;
end

end%.m end

评价函数：

function z = evaluate2(u)
%Weierstrass

dim= length(u);
sum_1=0;
sum_2=0;
sum_3=0;
a = 0.5;
b = 3;
for i=1:dim
for k=0:30
   sum_1=sum_1+(a^k)*(cos(2*pi*(b^k)*(u(i)+0.5)));
end
sum_2=sum_2+sum_1;
sum_1 = 0;
end
for k=0:30
   sum_3=sum_3+(a^k)*(cos(2*pi*(b^k)*0.5));
end
z=sum_2-dim*sum_3;
end

uerriup · 发表于 2020-5-14 10:56

基于迭代局部搜索和随机惯性权重的BA算法MATLAB实现（ILSSIWBA）

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