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 Males
Mayfly(i).Position=unifrnd(LowerBound,UpperBound,ProblemSize);
% Initialize Velocity
Mayfly(i).Velocity=zeros(ProblemSize);
% Evaluation
Mayfly(i).Cost=ObjectiveFunction(Mayfly(i).Position);
% Update Personal Best
Mayfly(i).Best.Position=Mayfly(i).Position;
Mayfly(i).Best.Cost=Mayfly(i).Cost;
funccount=funccount+1;
% Update Global Best
if Mayfly(i).Best.Cost<GlobalBest.Cost
GlobalBest=Mayfly(i).Best;
end
end
for i=1:nPopf
% Initialize Position of Females
Mayflyf(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:MaxIt
for i=1:nPopf
% Update Females
e=unifrnd(-1,+1,ProblemSize);
rmf=(Mayfly(i).Position-Mayflyf(i).Position);
if Mayflyf(i).Cost>Mayfly(i).Cost
Mayflyf(i).Velocity = g*Mayflyf(i).Velocity ...
+a3*exp(-beta.*rmf.^2).*(Mayfly(i).Position-Mayflyf(i).Position);
else
Mayflyf(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.
5 MATLAB代码与数据下载地址
见博客主页
