【优化算法】蚁狮优化算法(ALO)【含Matlab源码 1307期】-CFANZ编程社区

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二、蚁狮优化算法简介

蚁狮算法是一种模仿自然界中蚁狮的捕猎机制的智能算法。蚁狮在沙子中，利用它的下颚挖出一个圆锥形的沙坑作为捕猎陷阱。一旦有猎物落陷阱，蚁狮便会将它拖入沙子底部并吃掉。通过与一些其他流行的智能算法比较，例如PSO、GA和杜鹃算法（CS),ALO显示出更好的收敛性、准确性和鲁棒性，但依然存在着收敛准确度低、易陷入局部最优解的缺陷。

(1）蚂蚁随机游走

首先假设由n个蚂蚁组成的蚂蚁种群Xant=(XA,1,XA,n,…,XA,N)T,XdA,n是第n个蚂蚁的第d个变量。蚂蚁移动的数学表达为

【优化算法】蚁狮优化算法(ALO)【含Matlab源码 1307期】_迭代

式中，XA,n(t)为迭代t次时第n个蚂蚁的位置；cums m为累积和；tm a x为最大迭代次数。

为防止个体越限，对其进行标准化处理，即

【优化算法】蚁狮优化算法(ALO)【含Matlab源码 1307期】_神经网络_02

式中，min C(XdA,n)、max C(XdA,n)分别为第n只蚂蚁随机游走时的最小和最大步长；ud(t)、ld(t)分别为第t次迭代时第d个变量的上界和下界。

三、部分源代码

clear all 
clc

SearchAgents_no=40; % Number of search agents

Function_name='F1'; % Name of the test function that can be from F1 to F23 (Table 1,2,3 in the paper)

Max_iteration=500; % Maximum numbef of iterations

% Load details of the selected benchmark function
[lb,ub,dim,fobj]=Get_Functions_details(Function_name);

[Best_score,Best_pos,cg_curve]=ALO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
function [Elite_antlion_fitness,Elite_antlion_position,Convergence_curve]=ALO(N,Max_iter,lb,ub,dim,fobj)

% Initialize the positions of antlions and ants
antlion_position=initialization(N,dim,ub,lb);
ant_position=initialization(N,dim,ub,lb);

% Initialize variables to save the position of elite, sorted antlions, 
% convergence curve, antlions fitness, and ants fitness
Sorted_antlions=zeros(N,dim);
Elite_antlion_position=zeros(1,dim);
Elite_antlion_fitness=inf;
Convergence_curve=zeros(1,Max_iter);
antlions_fitness=zeros(1,N);
ants_fitness=zeros(1,N);

% Calculate the fitness of initial antlions and sort them
for i=1:size(antlion_position,1)
    antlions_fitness(1,i)=fobj(antlion_position(i,:)); 
end

[sorted_antlion_fitness,sorted_indexes]=sort(antlions_fitness);
    
for newindex=1:N
     Sorted_antlions(newindex,:)=antlion_position(sorted_indexes(newindex),:);
end
    
Elite_antlion_position=Sorted_antlions(1,:);
Elite_antlion_fitness=sorted_antlion_fitness(1);

% Main loop start from the second iteration since the first iteration 
% was dedicated to calculating the fitness of antlions
Current_iter=2; 
while Current_iter<Max_iter+1
    
    % This for loop simulate random walks
    for i=1:size(ant_position,1)
        % Select ant lions based on their fitness (the better anlion the higher chance of catching ant)
        Rolette_index=RouletteWheelSelection(1./sorted_antlion_fitness);
        if Rolette_index==-1  
            Rolette_index=1;
        end
      
        % RA is the random walk around the selected antlion by rolette wheel
        RA=Random_walk_around_antlion(dim,Max_iter,lb,ub, Sorted_antlions(Rolette_index,:),Current_iter);
        
        % RA is the random walk around the elite (best antlion so far)
        [RE]=Random_walk_around_antlion(dim,Max_iter,lb,ub, Elite_antlion_position(1,:),Current_iter);
        
        ant_position(i,:)= (RA(Current_iter,:)+RE(Current_iter,:))/2; % Equation (2.13) in the paper          
    end
    
    for i=1:size(ant_position,1)  
        
        % Boundar checking (bring back the antlions of ants inside search
        % space if they go beyoud the boundaries
        Flag4ub=ant_position(i,:)>ub;
        Flag4lb=ant_position(i,:)<lb;
        ant_position(i,:)=(ant_position(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;  
        
        ants_fitness(1,i)=fobj(ant_position(i,:));        
       
    end
    
function func_plot(func_name)

[lb,ub,dim,fobj]=Get_Functions_details(func_name);

switch func_name 
    case 'F1' 
        x=-100:2:100; y=x; %[-100,100]
        
    case 'F2' 
        x=-100:2:100; y=x; %[-10,10]
        
    case 'F3' 
        x=-100:2:100; y=x; %[-100,100]
        
    case 'F4' 
        x=-100:2:100; y=x; %[-100,100]
    case 'F5' 
        x=-200:2:200; y=x; %[-5,5]
    case 'F6' 
        x=-100:2:100; y=x; %[-100,100]
    case 'F7' 
        x=-1:0.03:1;  y=x  %[-1,1]
    case 'F8' 
        x=-500:10:500;y=x; %[-500,500]
    case 'F9' 
        x=-5:0.1:5;   y=x; %[-5,5]    
    case 'F10' 
        x=-20:0.5:20; y=x;%[-500,500]
    case 'F11' 
        x=-500:10:500; y=x;%[-0.5,0.5]
    case 'F12' 
        x=-10:0.1:10; y=x;%[-pi,pi]
    case 'F13' 
        x=-5:0.08:5; y=x;%[-3,1]
    case 'F14' 
        x=-100:2:100; y=x;%[-100,100]
    case 'F15' 
        x=-5:0.1:5; y=x;%[-5,5]
    case 'F16' 
        x=-1:0.01:1; y=x;%[-5,5]
    case 'F17' 
        x=-5:0.1:5; y=x;%[-5,5]
    case 'F18' 
        x=-5:0.06:5; y=x;%[-5,5]
    case 'F19' 
        x=-5:0.1:5; y=x;%[-5,5]
    case 'F20' 
        x=-5:0.1:5; y=x;%[-5,5]        
    case 'F21' 
        x=-5:0.1:5; y=x;%[-5,5]
    case 'F22' 
        x=-5:0.1:5; y=x;%[-5,5]     
    case 'F23' 
        x=-5:0.1:5; y=x;%[-5,5]  
end    

    

L=length(x);
f=[];

for i=1:L
    for j=1:L
        if strcmp(func_name,'F15')==0 && strcmp(func_name,'F19')==0 && strcmp(func_name,'F20')==0 && strcmp(func_name,'F21')==0 && strcmp(func_name,'F22')==0 && strcmp(func_name,'F23')==0
            f(i,j)=fobj([x(i),y(j)]);
        end
        if strcmp(func_name,'F15')==1
            f(i,j)=fobj([x(i),y(j),0,0]);
        end
        if strcmp(func_name,'F19')==1
            f(i,j)=fobj([x(i),y(j),0]);
        end
        if strcmp(func_name,'F20')==1
            f(i,j)=fobj([x(i),y(j),0,0,0,0]);
        end       
        if strcmp(func_name,'F21')==1 || strcmp(func_name,'F22')==1 ||strcmp(func_name,'F23')==1
            f(i,j)=fobj([x(i),y(j),0,0]);
        end          
    end
end

surfc(x,y,f,'LineStyle','none');

end