1 模型简介
2 部分代码
clc;
clear;
close all;
%% Problem Definition
data=load('mydata');
R=data.R;
model.R=R;
model.method='cvar';
model.alpha=0.95;
CostFunction=@(x) PortMOC(x,model); % Cost Function
nVar=size(R,2); % Number of Decision Variables
VarSize=[1 nVar]; % Size of Decision Variables Matrix
VarMin=0; % Lower Bound of Variables
VarMax=1; % Upper Bound of Variables
% Number of Objective Functions
nObj=numel(CostFunction(unifrnd(VarMin,VarMax,VarSize)));
%% NSGA-II Parameters
MaxIt=100; % Maximum Number of Iterations
nPop=50; % Population Size
pCrossover=0.7; % Crossover Percentage
nCrossover=2round(pCrossovernPop/2); % Number of Parnets (Offsprings)
pMutation=0.4; % Mutation Percentage
nMutation=round(pMutation*nPop); % Number of Mutants
mu=0.02; % Mutation Rate
sigma=0.1*(VarMax-VarMin); % Mutation Step Size
%% Initialization
empty_individual.Position=[];
empty_individual.Cost=[];
empty_individual.Out=[];
empty_individual.Rank=[];
empty_individual.DominationSet=[];
empty_individual.DominatedCount=[];
empty_individual.CrowdingDistance=[];
pop=repmat(empty_individual,nPop,1);
for i=1:nPop
pop(i).Position=unifrnd(VarMin,VarMax,VarSize);
[pop(i).Cost, pop(i).Out]=CostFunction(pop(i).Position);
end
% Non-Dominated Sorting
[pop, F]=NonDominatedSorting(pop);
% Calculate Crowding Distance
pop=CalcCrowdingDistance(pop,F);
% Sort Population
[pop, F]=SortPopulation(pop);
%% NSGA-II Main Loop
for it=1:MaxIt
% Crossover
popc=repmat(empty_individual,nCrossover/2,2);
for k=1:nCrossover/2
i1=randi([1 nPop]);
p1=pop(i1);
i2=randi([1 nPop]);
p2=pop(i2);
[popc(k,1).Position, popc(k,2).Position]=Crossover(p1.Position,p2.Position,VarMin,VarMax);
[popc(k,1).Cost, popc(k,1).Out]=CostFunction(popc(k,1).Position);
[popc(k,2).Cost, popc(k,2).Out]=CostFunction(popc(k,2).Position);
end
popc=popc(:);
% Mutation
popm=repmat(empty_individual,nMutation,1);
for k=1:nMutation
i=randi([1 nPop]);
p=pop(i);
popm(k).Position=Mutate(p.Position,mu,sigma,VarMin,VarMax);
[popm(k).Cost, popm(k).Out]=CostFunction(popm(k).Position);
end
% Merge
pop=[pop
popc
popm]; %#ok
% Non-Dominated Sorting
[pop, F]=NonDominatedSorting(pop);
% Calculate Crowding Distance
pop=CalcCrowdingDistance(pop,F);
% Sort Population
pop=SortPopulation(pop);
% Truncate
pop=pop(1:nPop);
% Non-Dominated Sorting
[pop, F]=NonDominatedSorting(pop);
% Calculate Crowding Distance
pop=CalcCrowdingDistance(pop,F);
% Sort Population
[pop, F]=SortPopulation(pop);
% Store F1
F1=pop(F{1});
% Show Iteration Information
disp(['Iteration ' num2str(it) ': Number of F1 Members = ' num2str(numel(F1))]);
% Plot F1 Costs
figure(1);
PlotCosts(F1);
pause(0.01);
end
%% Results
3 仿真结果
4 参考文献
[1]张利. NSGA2算法及其在电力系统稳定器参数优化中的应用[D]. 西南交通大学, 2013.
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，有科研问题可私信交流。
部分理论引用网络文献，若有侵权联系博主删除。
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，有科研问题可私信交流。
部分理论引用网络文献，若有侵权联系博主删除。
