【优化求解】基于蜉蝣算法MA(mayfly algorithm)求解单目标问题matlab源码

1 简介

2 部分代码

%%
clc; clear; close all;
%% Problem Definition
% Objective Function
ANSWER=listdlg('PromptString','Choose Objective Function','SelectionMode','single', 'ListString', {'1. Sphere', '2. Rastrigin'});
if eq(ANSWER,1); ObjectiveFunction=@(x) Sphere(x); funcname='Sphere';
elseif eq(ANSWER,2); ObjectiveFunction=@(x) Rastrigin(x); funcname='Rastrigin';
else; disp('Terminated'); return
end
ProblemSize=[1 50];         % Decision Variables Size
LowerBound=-10;             % Decision Variables Lower Bound
UpperBound= 10;             % Decision Variables Upper Bound
%% Mayfly Parameters
methname='Mayfly Algorithm';
MaxIt=2000;                 % Maximum Number of Iterations
nPop=20; nPopf=20;          % Population Size (males and females)
g=0.8;                      % Inertia Weight
gdamp=1;                    % Inertia Weight Damping Ratio
a1=1.0;                     % Personal Learning Coefficient
a2=1.5; a3=1.5;             % Global Learning Coefficient
beta=2;                     % Distance sight Coefficient
dance=5;                    % Nuptial Dance
fl=1;                       % Random flight
dance_damp=0.8;             % Damping Ratio
fl_damp=0.99;
% Mating Parameters
nc=20;                      % Number of Offsprings (also Parnets)
nm=round(0.05*nPop);        % Number of Mutants
mu=0.01;                    % Mutation Rate
% Velocity Limits
VelMax=0.1*(UpperBound-LowerBound); VelMin=-VelMax;
%% Initialization
empty_mayfly.Position=[];
empty_mayfly.Cost=[];
empty_mayfly.Velocity=[];
empty_mayfly.Best.Position=[];
empty_mayfly.Best.Cost=[];
Mayfly=repmat(empty_mayfly,nPop,1);   % Males
Mayflyf=repmat(empty_mayfly,nPopf,1); % Females
GlobalBest.Cost=inf;
funccount=0;
for i=1:nPop% Initialize Position of MalesMayfly(i).Position=unifrnd(LowerBound,UpperBound,ProblemSize);% Initialize VelocityMayfly(i).Velocity=zeros(ProblemSize);% EvaluationMayfly(i).Cost=ObjectiveFunction(Mayfly(i).Position);% Update Personal BestMayfly(i).Best.Position=Mayfly(i).Position;Mayfly(i).Best.Cost=Mayfly(i).Cost;funccount=funccount+1;% Update Global Bestif Mayfly(i).Best.Cost<GlobalBest.CostGlobalBest=Mayfly(i).Best;end
end
for i=1:nPopf% Initialize Position of FemalesMayflyf(i).Position=unifrnd(LowerBound,UpperBound,ProblemSize);Mayflyf(i).Velocity=zeros(ProblemSize);Mayflyf(i).Cost=ObjectiveFunction(Mayflyf(i).Position);funccount=funccount+1;% Update Global Best (Uncomment if you use the PGB-IMA version)%if Mayflyf(i).Best.Cost<GlobalBest.Cost%   GlobalBest=Mayflyf(i).Best;%end
end
BestSolution=zeros(MaxIt,1);
%% Mayfly Main Loop
for it=1:MaxItfor i=1:nPopf% Update Femalese=unifrnd(-1,+1,ProblemSize);rmf=(Mayfly(i).Position-Mayflyf(i).Position);if Mayflyf(i).Cost>Mayfly(i).CostMayflyf(i).Velocity = g*Mayflyf(i).Velocity ...+a3*exp(-beta.*rmf.^2).*(Mayfly(i).Position-Mayflyf(i).Position);elseMayflyf(i).Velocity = g*Mayflyf(i).Velocity+fl*(e);disp([methname ' on the ' funcname  ' Function: Iteration = ' num2str(it)  ', ' funcname ', Evaluations = ' num2str(funccount)  '. Best Cost = ' num2str(BestSolution(it))]);g=g*gdamp;dance = dance*dance_damp;fl = fl*fl_damp;
end
%% Results
figure;
plot(BestSolution,'LineWidth',2); semilogy(BestSolution,'LineWidth',2);
xlabel('Iterations'); ylabel('Objective function'); grid on;
%%img =gcf;  %获取当前画图的句柄
print(img, '-dpng', '-r600', './img.png')         %即可得到对应格式和期望dpi的图像

3 仿真结果

4 参考文献

[1]陈伟超, and 符强. "基于倒位变异的蜉蝣优化算法." 计算机系统应用 30.8:7.