✅作者简介：热爱科研的Matlab仿真开发者，修心和技术同步精进，matlab项目合作可私信。
🍎个人主页：Matlab科研工作室
🍊个人信条：格物致知。
⛄ 内容介绍
近些年来,随着智能交通系统(ITS)的蓬勃发展,智能交通控制与诱导系统逐渐成为ITS研究的热门课题.但是无论是对于交通控制还是交通诱导系统来说,实时准确的交通流量预测是这些系统实现的前提及关键.一种基于麻雀算法极限学习机的短时交通流预测模型,仿真结果表示.所发明的短时交通流预测模型具有较高的预测精度和较强的泛化能力,预测值与实际值的拟合程度好.
⛄ 部分代码
function [Convergence_curve,bestX]=SSA(N, dim, ub, lb,M,hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test)
P_percent = 0.2; % 发现者的种群规模占总种群规模的百分比
pNum = round(N*P_percent); % 发现者数量20%
SD = pNum/2; % 警戒者数量10%
ST = 0.8; % 安全阈值
% 初始化
X = initialization(N, dim, ub, lb);
for i = 1:N
% X(i, :) = lb + (ub - lb) .* rand(1, dim);
fitness1(i) = fitness(X(i, :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
end
pFit = fitness1;
pX = X; % 与pFit相对应的个体最佳位置
[fMin, bestI] = min(fitness1); % fMin表示全局最优解
bestX = X(bestI, :); % bestX表示全局最优位置
%% 开始进化
Convergence_curve = ones(M,1); % 初始化每次迭代得到的最佳的适应度
%% 迭代寻优
for t = 1 : M
[~, sortIndex] = sort(pFit); % 排序
[fmax, B] = max(pFit);
worst = X(B, :);
%% 发现者位置更新
r2 = rand(1);
if r2 < ST
for i = 1:pNum % Equation (3)
r1 = rand(1);
X(sortIndex(i), :) = pX(sortIndex(i), :)*exp(-(i)/(r1*M));
X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);
fitness1(sortIndex(i)) = fitness(X(sortIndex(i), :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
end
else
for i = 1:pNum
X(sortIndex(i), :) = pX(sortIndex(i), :)+randn(1)*ones(1, dim);
X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);
fitness1(sortIndex(i)) = fitness(X(sortIndex(i), :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
end
end
[~, bestII] = min(fitness1);
bestXX = X(bestII, :);
%% 跟随者位置更新
for i = (pNum+1):N % Equation (4)
A = floor(rand(1, dim)*2)*2-1;
if i > N/2
X(sortIndex(i), :) = randn(1)*exp((worst-pX(sortIndex(i), :))/(i)^2);
else
X(sortIndex(i), :) = bestXX+(abs((pX(sortIndex(i), :)-bestXX)))*(A'*(A*A')^(-1))*ones(1, dim);
end
X(sortIndex(i), :) = Bounds(X(sortIndex(i), :), lb, ub);
fitness1(sortIndex(i)) = fitness(X(sortIndex(i), :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
end
%% 警戒者位置更新
c = randperm(numel(sortIndex));
b = sortIndex(c(1:SD));
for j = 1:length(b) % Equation (5)
if pFit(sortIndex(b(j))) > fMin
X(sortIndex(b(j)), :) = bestX+(randn(1, dim)).*(abs((pX(sortIndex(b(j)), :) -bestX)));
else
X(sortIndex(b(j)), :) = pX(sortIndex(b(j)), :)+(2*rand(1)-1)*(abs(pX(sortIndex(b(j)), :)-worst))/(pFit(sortIndex(b(j)))-fmax+1e-50);
end
X(sortIndex(b(j)), :) = Bounds(X(sortIndex(b(j)), :), lb, ub);
fitness1(sortIndex(b(j))) = fitness(X(sortIndex(b(j)), :),hiddennum_best, inputn, outputn, output_train, inputn_test ,outputps, output_test);
end
for i = 1:N
% 更新个体最优
if fitness1(i) < pFit(i)
pFit(i) = fitness1(i);
pX(i, :) = X(i, :);
end
% 更新全局最优
if pFit(i) < fMin
fMin = pFit(i);
bestX = pX(i, :);
end
end
Convergence_curve(t) = fMin;
disp(['SSA: At iteration ', num2str(t), ' ,the best fitness is ', num2str(fMin)]);
end
⛄ 运行结果
⛄ 参考文献
[1]李军, 李大超. 基于优化核极限学习机的风电功率时间序列预测[J]. 物理学报, 2016(13):10.
[2]冯磊华, 张杰, 詹毅. 基于改进麻雀搜索算法和核极限学习机的电站锅炉燃烧优化[J]. 热力发电, 2022(009):051.
