✅作者简介热爱科研的Matlab仿真开发者擅长毕业设计辅导、数学建模、数据处理、程序设计科研仿真。完整代码获取 定制创新 论文复现点击Matlab科研工作室 关注我领取海量matlab电子书和数学建模资料个人信条做科研博学之、审问之、慎思之、明辨之、笃行之是为博学慎思明辨笃行。 内容介绍一、引言负荷预测在电力系统的规划、运行和管理中起着关键作用。准确的负荷预测有助于优化发电计划、合理分配电力资源以及提高电网运行的稳定性和经济性。传统的负荷预测方法在处理复杂的负荷变化模式时存在局限性而基于机器学习的方法展现出了强大的潜力。极限学习机ELM作为一种快速且有效的单隐层前馈神经网络但其随机生成的输入权重和偏置可能导致预测性能的不稳定。本文提出利用凌日优化算法TSOA对 ELM 进行优化旨在提高负荷预测的精度和可靠性。二、相关理论基础凌日优化算法TSOA灵感来源凌日优化算法是一种受天文现象凌日启发而提出的新型元启发式优化算法。在天文学中凌日是指一个天体在另一个天体与观测者之间通过的现象。TSOA 模拟了行星在围绕恒星运行过程中受恒星引力和其他行星引力影响下的运动轨迹。算法流程在 TSOA 中每个优化问题的解被看作是一个 “行星”其位置代表了问题的一个潜在解。算法通过模拟行星的运动来搜索最优解。行星的运动受到引力的影响引力的大小与行星之间的距离以及目标函数值相关。距离较近且目标函数值较好的行星对其他行星产生更强的引力。行星根据引力的作用更新自己的位置在搜索空间中不断移动逐渐向最优解靠近。同时算法引入了一定的随机扰动以避免算法陷入局部最优解增强全局搜索能力。例如在每次迭代中行星的位置更新公式结合了引力作用和随机因素使得算法能够在探索新的搜索区域和利用已发现的较好解之间取得平衡。三、基于 TSOA 优化 ELM 的负荷预测算法⛳️ 运行结果 部分代码function [Best_Planet , best,Bestss] TransitSearch (ns,maxcycle,Vmin,Vmax,nvar,CostFunction)SN10;%% InitializationEmpty.Location [];Empty.Cost inf;Galaxy_Center repmat (Empty, 1, 1);region repmat (Empty, ns*SN, 1);selested_regions repmat (Empty, ns, 1);Stars repmat (Empty, ns, 1);Stars_sorted zeros(ns,1);Ranks 1:1:ns;Stars_Ranks zeros(ns,1);Luminosity zeros(ns,1);Star_RanksNormal zeros(ns,1);Distance zeros(ns,1);Transit0 zeros(ns,1);SN_P repmat (Empty, SN, 1);Bestsregion;if length(Vmin) 1VminVmin;VmaxVmax;elseVminVmin*ones(1,nvar);VmaxVmax*ones(1,nvar);end%% Galaxy Phase% Initial Location of The Center of the GalaxyGalaxy_Center.Location unifrnd(Vmin,Vmax,1,nvar);% Galaxy_Center.Locationinitialization(ns,nvar,Vmax,Vmin);Galaxy_Center.Cost CostFunction(Galaxy_Center.Location);% Galactic Habitate Zone of the Galaxyfor l 1:ns*SNzone randi(2);if zone 1difference rand().*(Galaxy_Center.Location)-(unifrnd(Vmin,Vmax,1,nvar));elsedifference rand().*(Galaxy_Center.Location)(unifrnd(Vmin,Vmax,1,nvar));endNoise ((rand(1,nvar)).^3).*(unifrnd(Vmin,Vmax,1,nvar));region(l).Location Galaxy_Center.Location difference - Noise;region(l).Location max(region(l).Location, Vmin);region(l).Location min(region(l).Location, Vmax);region(l).Cost CostFunction(region(l).Location);end% Selection of Stars from the Galactic Habitate Zone of the Galaxy[Sort,index]sort([region.Cost]);for i 1:nsselested_regions(i) region(i);for k 1:SNzone randi(2);if zone 1difference rand().*(selested_regions(i).Location)-rand().*(unifrnd(Vmin,Vmax,1,nvar));elsedifference rand().*(selested_regions(i).Location)rand().*(unifrnd(Vmin,Vmax,1,nvar));endNoise ((rand(1,nvar)).^3).*(unifrnd(Vmin,Vmax,1,nvar));new.Location selested_regions(i).Location difference - Noise;new.Location max(new.Location, Vmin);new.Location min(new.Location, Vmax);new.Cost CostFunction(new.Location);if new.Cost Stars(i).CostStars(i) new;endendend% Initial Location of the Best Planets (Start Point: Its Star)Best_Planets Stars;% Specification of the Best Planet[Sort,index]sort([Best_Planets(1).Cost]);Best_Planet Best_Planets(index(1,1));% Telescope LocationTelescope.Location unifrnd(Vmin,Vmax,1,nvar);% Determination of the Luminosity of the Starsfor i 1:nsStars_sorted(i,1) Stars(i).Cost;endStars_sorted sort (Stars_sorted);for i 1:nsfor ii 1:nsif Stars(i).Cost Stars_sorted(ii,1)Stars_Ranks(i,1) Ranks(1,ii);Star_RanksNormal(i,1) (Stars_Ranks(i,1))./ns;endendDistance(i,1) sum((Stars(i).Location-Telescope.Location).^2).^0.5;Luminosity(i,1) Star_RanksNormal(i,1)/((Distance(i,1))^2);endLuminosity_new Luminosity;Stars2 Stars;%% Loops of the TS Algorithmfor it 1:maxcycle%% Transit PhaseTransit Transit0;Luminosity Luminosity_new;for i 1:nsdifference (2*rand()-1).*(Stars(i).Location);Noise ((rand(1,nvar)).^3).*(unifrnd(Vmin,Vmax,1,nvar));Stars2(i).Location Stars(i).Location difference - Noise;Stars2(i).Location max(Stars2(i).Location, Vmin);Stars2(i).Location min(Stars2(i).Location, Vmax);Stars2(i).Cost CostFunction(Stars2(i).Location);endfor i 1:nsStars_sorted(i,1) Stars2(i).Cost;endStars_sorted sort (Stars_sorted);for i 1:nsfor ii 1:nsif Stars2(i).Cost Stars_sorted(ii,1)Stars_Ranks(i,1) Ranks(1,ii);Star_RanksNormal(i,1) (Stars_Ranks(i,1))./ns;endendDistance(i,1) sum((Stars2(i).Location-Telescope.Location).^2).^0.5;Luminosity_new(i,1) Star_RanksNormal(i,1)/((Distance(i,1))^2);if Luminosity_new(i,1) Luminosity(i,1)Transit (i,1) 1; % Has transit been observed? 0 No; 1 YesendendStars Stars2;%% Location Phase (Exploration)for i 1:nsif Transit (i,1) 1% Determination of the Location of the PlanetLuminosity_Ratio Luminosity_new(i,1)/Luminosity(i,1);Planet.Location (rand().*Telescope.Location Luminosity_Ratio.*Stars(i).Location)./2;for k 1:SNzone randi(3);if zone 1new.Location Planet.Location - (2*rand()-1).*(unifrnd(Vmin,Vmax,1,nvar));elseif zone 2new.Location Planet.Location (2*rand()-1).*(unifrnd(Vmin,Vmax,1,nvar));elsenew.Location Planet.Location (2.*rand(1,nvar)-1).*(unifrnd(Vmin,Vmax,1,nvar));endnew.Location max(new.Location, Vmin);new.Location min(new.Location, Vmax);% new.Cost CostFunction(new.Location);SN_P(k) new;endSUM 0;for k 1:SNSUM SUMSN_P(k).Location;endnew.Location SUM./SN;new.Cost CostFunction(new.Location);if new.Cost Best_Planets(i).CostBest_Planets(i) new;endelse % No Transit observed: Neighbouring planetsNeighbor.Location (rand().*Stars(i).Location rand().*(unifrnd(Vmin,Vmax,1,nvar)))./2;for k 1:SNzone randi(3);if zone 1Neighbor.Location Neighbor.Location - (2*rand()-1).*(unifrnd(Vmin,Vmax,1,nvar));elseif zone 2Neighbor.Location Neighbor.Location (2*rand()-1).*(unifrnd(Vmin,Vmax,1,nvar));elseNeighbor.Location Neighbor.Location (2.*rand(1,nvar)-1).*(unifrnd(Vmin,Vmax,1,nvar));endNeighbor.Location max(Neighbor.Location, Vmin);Neighbor.Location min(Neighbor.Location, Vmax);Neighbor.Cost CostFunction (Neighbor.Location);SN_P(k) Neighbor;endSUM 0;for k 1:SNSUM SUMSN_P(k).Location;endNeighbor.Location SUM./SN;Neighbor.Cost CostFunction (Neighbor.Location);if Neighbor.Cost Best_Planets(i).CostBest_Planets(i) Neighbor;endendend%% Signal Amplification of the Best Planets (Exploitation)for i 1:nsfor k 1:SNRAND randi(2 );if RAND 1Power randi(SN*ns);Coefficient 2*rand();Noise ((rand(1,nvar)).^Power).*(unifrnd(Vmin,Vmax,1,nvar));elsePower randi(SN*ns);Coefficient 2*rand();Noise -((rand(1,nvar)).^Power).*(unifrnd(Vmin,Vmax,1,nvar));end% new.Location (rand().*Best_Planets(i).Location) - Coefficient.*Noise;chance randi(2);if chance 1new.Location Best_Planets(i).Location - Coefficient.*Noise;elsenew.Location (rand().*Best_Planets(i).Location) - Coefficient.*Noise;endnew.Location max(new.Location, Vmin);new.Location min(new.Location, Vmax);new.Cost CostFunction(new.Location);% new.Cost Penalty(new,ConstraintFunction,CostFunction);if new.Cost Best_Planets(i).CostBest_Planets(i) new;bestBest_Planets(i).Location;endendif Best_Planets(i).Cost Best_Planet.CostBest_Planet Best_Planets(i);endend% ResultsBestss(it)Best_Planet.Cost;endend 参考文献更多免费数学建模和仿真教程关注领取
