测附matlab代码
1简介
对开盘指数预测的精度与可靠性提出更⾼要求.为解决⽀持向量机(SVM)算法中核参数和惩戒参数依赖经验选取导致的⼤盘指数预测精度较低，收敛速度较慢的问题，提出⼀种基于鲸鱼算法优化最⼩⼆乘⽀持向量机(WOASVM)的负荷预测⽅法.⾸先通过鲸鱼算法对SVM参数进⾏寻优，最后建⽴⼤盘指数预测模型.结合某地区的实测数据进⾏预测分析，结果表明，相较于SVM改进模型预测精度和收敛速度均有⼤幅提⾼，在⾦融企业中具有良好的实际应⽤价值.
断阈值p选择包围猎物还是狩猎⾏为。为了减少控制变量，该算法只有位置向量，去掉了速度向量，所以使得算法的寻优能⼒得到增强。
菲奥娜
2 部分代码
%_________________________________________________________________________%
% Whale Optimization Algorithm (WOA) source codes demo 1.0 %
% The Whale Optimization Algorithm
function [Leader_score,Leader_pos,Convergence_curve]=WOA(SearchAgents_no,Max_iter,lb,ub,dim,fobj)
% initialize position vector and score for the leader
Leader_pos=zeros(1,dim);
Leader_score=inf; %change this to -inf for maximization problems
%Initialize the positions of search agents
% Positions=initialization(SearchAgents_no,dim,ub,lb);
Positions=ceil(rand(SearchAgents_no,dim).*(ub-lb)+lb);
Convergence_curve=zeros(1,Max_iter);
t=0;% Loop counter
% Main loop
while t<Max_iter
for i=1:size(Positions,1)
% Return back the search agents that go beyond the boundaries of the search space Flag4ub=Positions(i,:)>ub;
Flag4lb=Positions(i,:)<lb;
Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
% Calculate objective function for each search agent
fitness=fobj(Positions(i,:));
% Update the leader
if fitness<Leader_score % Change this to > for maximization problem
Leader_score=fitness; % Update alpha
Leader_pos=Positions(i,:);
end
end
a=2-t*((2)/Max_iter); % a decreases linearly fron 2 to 0 in Eq. (2.3)乡村社会学
% a2 linearly dicreases from -1 to -2 to calculate t in Eq. (3.12)
我心目中的春天a2=-1+t*((-1)/Max_iter);
% Update the Position of search agents
for i=1:size(Positions,1)
r1=rand(); % r1 is a random number in [0,1]
r2=rand(); % r2 is a random number in [0,1]
液态镁
A=2*a*r1-a; % Eq. (2.3) in the paper
C=2*r2; % Eq. (2.4) in the paper
裂隙水
b=1; % parameters in Eq. (2.5)
l=(a2-1)*rand+1; % parameters in Eq. (2.5)
p = rand(); % p in Eq. (2.6)
for j=1:size(Positions,2)
if p<0.5
if abs(A)>=1
rand_leader_index = floor(SearchAgents_no*rand()+1);
X_rand = Positions(rand_leader_index, :);
D_X_rand=abs(C*X_rand(j)-Positions(i,j)); % Eq. (2.7)
Positions(i,j)=X_rand(j)-A*D_X_rand; % Eq. (2.8)
elseif abs(A)<1
D_Leader=abs(C*Leader_pos(j)-Positions(i,j)); % Eq. (2.1)
Positions(i,j)=Leader_pos(j)-A*D_Leader; % Eq. (2.2)
end
elseif p>=0.5
distance2Leader=abs(Leader_pos(j)-Positions(i,j));
% Eq. (2.5)
Positions(i,j)=distance2Leader*exp(b.*l).*cos(l.*2*pi)+Leader_pos(j); end
end
end
t=t+1;
Convergence_curve(t)=Leader_score;
% [t Leader_score]
电子商城end
3 仿真结果
4 参考⽂献
[1]张⽂喜. 基于鲸鱼算法优化LSSVM的抽油井动液⾯预测[J]. 化⼯管理, 2018(2):1.
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，相关matlab代码问题可私信交流。
部分理论引⽤⽹络⽂献，若有侵权联系博主删除。
豫公网安备41160202000603
站长QQ:729038198
关于我们
投诉建议