【优化算法】海洋捕食者算法（MPA）【含Matlab源码 478期】
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= where lbn is the lower bound of variable n
% ub= 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
=Get_Functions_details(Function_name);
=MPA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
% function topology
figure('Position',)
subplot(1,2,1);
func_plot(Function_name);
title('Function Topology')
xlabel('x_1');
ylabel('x_2');
zlabel()
% 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=
% up is the uppper bound: ub=
% dim is the number of variables (dimension of the problem)
function = 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=;
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
页:
[1]