单目标应用：进化场优化算法（Evolutionary Field Optimization，EFO）求解微电网优化MATLAB
一、微网系统运行优化模型
微电网优化模型介绍：
微电网多目标优化调度模型简介_IT猿手的博客-CSDN博客
二、进化场优化算法EFO
进化场优化算法（Evolutionary Field Optimization，EFO）由Baris Baykant Alagoz等人于2022年提出，该算法采用场适应差分交叉机制，具有搜索速度快等优势。
参考文献：
[1]Alagoz BB, Simsek OI, Ari D, Tepljakov A, Petlenkov E, Alimohammadi H. An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications. Sensors. 2022; 22(10):3836. Sensors | Free Full-Text | An Evolutionary Field Theorem: Evolutionary Field Optimization in Training of Power-Weighted Multiplicative Neurons for Nitrogen Oxides-Sensitive Electronic Nose Applications
三、进化场优化算法EFO求解微电网优化
（1）部分代码
close all;
clear ;
clc;
global P_load; %电负荷
global WT;%风电
global PV;%光伏
%%
TestProblem=1;
[lb,ub,dim,fobj] = GetFunInfo(TestProblem);
SearchAgents_no=50; % Number of search agents
Max_iteration=800; % Maximum number of iterations
[Best_score,Xbest,Convergence_curve]=EFO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);%% 画结果图
figure(1)
semilogy(Convergence_curve,'k-','linewidth',2);
legend('EFO');
xlabel('迭代次数')
ylabel('运行成本与环境保护成本之和')
（2）部分结果
