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

版权声明：© 2020 • OmegaXYZ.com-版权所有 转载请注明出处 https://blog.csdn.net/xyisv/article/details/88116355

BA算法简介

http://www.omegaxyz.com/2019/02/12/ba-matlab/

该论文修改

作者在原有BA算法上进行3个修改

1. 跳出局部最优（扰动个体）
2. 使得算法变得稳定
3. 脉搏和响度修改，平衡局部与全局搜索

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

更多内容访问omegaxyz.com
网站所有代码采用Apache 2.0授权
网站文章采用知识共享许可协议BY-NC-SA4.0授权
© 2019 • OmegaXYZ-版权所有 转载请注明出处