【优化算法】海洋捕食者算法（MPA）【含Matlab源码 478期】

江南才子

　　
一、简介
　　海洋捕食者算法(MPA)是一种自然启发式的优化算法,它遵循在最佳觅食策略中自然支配的规则,并且在海洋生态系统中遇到捕食者与猎物之间的速率策略。

二、源代码

%_________________________________________________________________________
%  Marine Predators Algorithm source code (Developed in MATLAB R2015a)
%
%  programming: Afshin Faramarzi & Seyedali Mirjalili
%
% paper:
%  A. Faramarzi, M. Heidarinejad, S. Mirjalili, A.H. Gandomi, 
%  Marine Predators Algorithm: A Nature-inspired Metaheuristic
%  Expert Systems with Applications
%  DOI: doi.org/10.1016/j.eswa.2020.113377
%  
%  E-mails: afaramar@hawk.iit.edu      (Afshin Faramarzi)
%       muh182@iit.edu           (Mohammad Heidarinejad)
%       ali.mirjalili@laureate.edu.au  (Seyedali Mirjalili) 
%       gandomi@uts.edu.au         (Amir H Gandomi)
%_________________________________________________________________________
% --------------------------------------------
% fobj = @YourCostFunction
% dim = number of your variables
% Max_iteration = maximum number of iterations
% 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
% ---------------------------------------------------------
clear all
clc
format long
SearchAgents_no=25; % Number of search agents
Function_name='F23';
   
Max_iteration=500; % Maximum number of iterations
[lb,ub,dim,fobj]=Get_Functions_details(Function_name);
[Best_score,Best_pos,Convergence_curve]=MPA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
% function topology
figure('Position',[500 400 700 290])
subplot(1,2,1);
func_plot(Function_name);
title('Function Topology')
xlabel('x_1');
ylabel('x_2');
zlabel([Function_name,'( x_1 , x_2 )'])
% Convergence curve
subplot(1,2,2);
semilogy(Convergence_curve,'Color','r')
title('Objective space')
xlabel('Iteration');
ylabel('Best score obtained so far');
%_________________________________________________________________________
%  Marine Predators Algorithm source code (Developed in MATLAB R2015a)
%
%  programming: Afshin Faramarzi & Seyedali Mirjalili
%
% paper:
%  A. Faramarzi, M. Heidarinejad, S. Mirjalili, A.H. Gandomi, 
%  Marine Predators Algorithm: A Nature-inspired Metaheuristic
%  Expert Systems with Applications
%  DOI: doi.org/10.1016/j.eswa.2020.113377
%  
%  E-mails: afaramar@hawk.iit.edu      (Afshin Faramarzi)
%       muh182@iit.edu           (Mohammad Heidarinejad)
%       ali.mirjalili@laureate.edu.au  (Seyedali Mirjalili) 
%       gandomi@uts.edu.au         (Amir H Gandomi)
%_________________________________________________________________________
% This function containts full information and implementations of the benchmark 
% functions in Table 1, Table 2, and Table 3 in the paper
% 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=50;
    
  case 'F2'
    fobj = @F2;
    lb=-10;
    ub=10;
    dim=50;
    
  case 'F3'
    fobj = @F3;
    lb=-100;
    ub=100;
    dim=50;
    
  case 'F4'
    fobj = @F4;
    lb=-100;
    ub=100;
    dim=50;
    
  case 'F5'
    fobj = @F5;
    lb=-30;
    ub=30;
    dim=50;
    
  case 'F6'
    fobj = @F6;
    lb=-100;
    ub=100;
    dim=50;
    
  case 'F7'
    fobj = @F7;
    lb=-1.28;
    ub=1.28;
    dim=50;
    
  case 'F8'
    fobj = @F8;
    lb=-500;
    ub=500;
    dim=50;
    
  case 'F9'
    fobj = @F9;
    lb=-5.12;
    ub=5.12;
    dim=50;
    
  case 'F10'
    fobj = @F10;
    lb=-32;
    ub=32;
    dim=50;
    
  case 'F11'
    fobj = @F11;
    lb=-600;
    ub=600;
    dim=50;
    
  case 'F12'
    fobj = @F12;
    lb=-50;
    ub=50;
    dim=50;
    
  case 'F13'
    fobj = @F13;
    lb=-50;
    ub=50;
    dim=50;
    
  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
% F1
function o = F1(x)
o=sum(x.^2);
end
% F2
function o = F2(x)
o=sum(abs(x))+prod(abs(x));
end
% F3
function o = F3(x)
dim=size(x,2);
o=0;
for i=1:dim
  o=o+sum(x(1:i))^2;
end
end
% F4
function o = F4(x)
o=max(abs(x));
end

三、运行结果

四、备注
　　版本：2014a