2014 年 段 海 滨 教 授 通 过 归 纳 总 结 , 提 出 鸽 群 算 法(Pigeon-inspired Optimization PIO),PIO 是模拟鸽子归巢行为而设计出来的群智能优化算法。PIO 具有原理简明的特点、需要调整参数极少、易于被实现。与其他算法比较有着计算相对简单,鲁棒性相对较强等明显的优点。
算法原理
鸽子在距离目的地较远时,是在地磁场和地标建筑的帮助下到达目的地。影响鸽群归巢的关键原因可分为 3 类,第一个原因是太阳,第二个原因是地球的磁场,第三个原因是地貌景观,而鸽子在飞行的过程中,根据不同的情况会使用不同的巡航工具。首先通过地磁场来对一个大概的方向进行辨别,然后利用地貌景象对目前的方向实施修正,直到到达精确的目的地。所以 PIO 算法中鸽子归巢有两个基本部分组成:指南针算子和地标算子。当鸽子距离自己目的地较远时是利用地磁场来辨别方向,当距离目的地比较近时就利用当地地标来进行导航。在 PIO 中地图和指针算子模型的提出就是基于地磁场和太阳,而地标算子模型的提出是基于地标。
** 指南针算子**
地标算子
%_________________________________________________________________________% % 鸽群优化算法 % %_________________________________________________________________________% % 使用方法 %__________________________________________ % fobj = @YourCostFunction 设定适应度函数 % dim = number of your variables 设定维度 % Max_iteration = maximum number of generations 设定最大迭代次数 % SearchAgents_no = number of search agents 种群数量 % lb=[lb1,lb2,...,lbn] where lbn is the lower bound of variable n 变量下边界 % ub=[ub1,ub2,...,ubn] where ubn is the upper bound of variable n 变量上边界 % If all the variables have equal lower bound you can just % define lb and ub as two single number numbers % To run PIO: [Best_pos,Best_score,curve]=PIO(pop,Max_iter,lb,ub,dim,fobj) %__________________________________________ clear all clc % rng('default'); SearchAgents_no=50; % Number of search agents 种群数量 Function_name='F9'; % Name of the test function that can be from F1 to F23 (Table 1,2,3 in the paper) 设定适应度函数 Max_iteration=1000; % Maximum numbef of iterations 设定最大迭代次数 % Load details of the selected benchmark function [lb,ub,dim,fobj]=Get_Functions_details(Function_name); %设定边界以及优化函数 [Best_score,Best_pos,PIO_curve]=PIO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj); %开始优化 % This function containts full information and implementations of the benchmark % lb is the lower bound: lb=[lb_1,lb_2,...,lb_d] % up is the uppper bound: ub=[ub_1,ub_2,...,ub_d] % dim is the number of variables (dimension of the problem) function [lb,ub,dim,fobj] = Get_Functions_details(F) switch F case 'F1' fobj = @F1; lb=-100; ub=100; dim=30; case 'F2' fobj = @F2; lb=-10; ub=10; dim=30; case 'F3' fobj = @F3; lb=-100; ub=100; dim=30; case 'F4' fobj = @F4; lb=-100; ub=100; dim=30; case 'F5' fobj = @F5; lb=-30; ub=30; dim=30; case 'F6' fobj = @F6; lb=-100; ub=100; dim=30; case 'F7' fobj = @F7; lb=-1.28; ub=1.28; dim=30; case 'F8' fobj = @F8; lb=-500; ub=500; dim=30; case 'F9' fobj = @F9; lb=-5.12; ub=5.12; dim=30; case 'F10' fobj = @F10; lb=-32; ub=32; dim=30; case 'F11' fobj = @F11; lb=-600; ub=600; dim=30; case 'F12' fobj = @F12; lb=-50; ub=50; dim=30; case 'F13' fobj = @F13; lb=-50; ub=50; dim=30; case 'F14' fobj = @F14; lb=-65.536; ub=65.536; dim=2; case 'F15' fobj = @F15; lb=-5; ub=5; dim=4; case 'F16' fobj = @F16; lb=-5; ub=5; dim=2; case 'F17' fobj = @F17; lb=[-5,0]; ub=[10,15]; dim=2; case 'F18' fobj = @F18; lb=-2; ub=2; dim=2; case 'F19' fobj = @F19; lb=0; ub=1; dim=3; case 'F20' fobj = @F20; lb=0; ub=1; dim=6; case 'F21' fobj = @F21; lb=0; ub=10; dim=4; case 'F22' fobj = @F22; lb=0; ub=10; dim=4; case 'F23' fobj = @F23; lb=0; ub=10; dim=4; end end
版本:2014a