【蚁狮算法】基于具有自适应边界与最优引导的莱维飞行蚁狮优化算法（ABLALO）求解单目标优化问题matlab代码

1 简介

针对蚁狮算法存在探索与开发能力不平衡的缺点,提出了具有自适应边界与最优引导的莱维飞行改进算法.首先蚁狮调整边界范围,蚂蚁做莱维飞行,以此平衡探索与开发能力;其次较差蚁狮做高斯变异,并通过自适应最优引导方程,提高收敛速度和全局搜索能力.6个标准测试函数的仿真结果表明,相比其它算法,提出的改进算法提高了最优解的精度和收敛速度.

2 部分代码

%___________________________________________________________________%%  Ant Lion Optimizer (ALO) source codes demo version 1.0           %% You can simply define your cost in a seperate file and load its handle to fobj % The initial parameters that you need are:%__________________________________________% fobj = @YourCostFunction% dim = number of your variables% Max_iteration = maximum number of generations% SearchAgents_no = number of search agents% lb=[lb1,lb2,...,lbn] where lbn is the lower bound of variable n% ub=[ub1,ub2,...,ubn] where ubn is the upper bound of variable n% If all the variables have equal lower bound you can just% define lb and ub as two single number numbers% To run ALO: [Best_score,Best_pos,cg_curve]=ALO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj)function [min_value,Elite_antlion_fitness,Elite_antlion_position,Convergence_curve]=ALO(N,Max_iter,lb,ub,dim,fobj)% Initialize the positions of antlions and antsantlion_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 fitnessSorted_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 themfor 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 antlionsCurrent_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        % Update antlion positions and fitnesses based of the ants (if an ant     % becomes fitter than an antlion we assume it was cought by the antlion      % and the antlion update goes to its position to build the trap)    double_population=[Sorted_antlions;ant_position];    double_fitness=[sorted_antlion_fitness ants_fitness];            [double_fitness_sorted I]=sort(double_fitness);    double_sorted_population=double_population(I,:);            antlions_fitness=double_fitness_sorted(1:N);    Sorted_antlions=double_sorted_population(1:N,:);            % Update the position of elite if any antlinons becomes fitter than it    if antlions_fitness(1)<Elite_antlion_fitness         Elite_antlion_position=Sorted_antlions(1,:);        Elite_antlion_fitness=antlions_fitness(1);    end          % Keep the elite in the population    Sorted_antlions(1,:)=Elite_antlion_position;    antlions_fitness(1)=Elite_antlion_fitness;      % Update the convergence curve    Convergence_curve(Current_iter)=Elite_antlion_fitness;    % Display the iteration and best optimum obtained so far    if mod(Current_iter,1)==0        display(['At iteration ', num2str(Current_iter), ' the elite fitness is ', num2str(Elite_antlion_fitness)]);    endmin_value(Current_iter)=Elite_antlion_fitness;    Current_iter=Current_iter+1; end

3 仿真结果

4 参考文献

[1]王若安, 周越文, 韩博,等. 具有自适应边界与最优引导的莱维飞行蚁狮优化算法[J]. 微电子学与计算机, 2018, 35(9):7.

博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。

部分理论引用网络文献，若有侵权联系博主删除。

【蚁狮算法】基于具有自适应边界与最优引导的莱维飞行蚁狮优化算法（ABLALO）求解单目标优化问题matlab代码相关推荐

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