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⛄ 内容介绍
为了提高数据分类准确率,提出一种基于人工蜂群算法和BP神经网络的分类方法.针对反向传播(BP)神经网络存在全局搜索能力差,人工蜂群算法来优化BP神经网络对初始权重敏感的问题,进而实现对数据的分类.实验结果表明,所提算法对数据的分类准确率更高,分类准确率达到94.5%,而且可以加快收敛速度
⛄ 部分代码
clc;
clear;
close all;
%% Problem Definition
CostFunction=@(x) Sphere(x); % Cost Function
nVar=5; % Number of Decision Variables
VarSize=[1 nVar]; % Decision Variables Matrix Size
VarMin=-10; % Decision Variables Lower Bound
VarMax= 10; % Decision Variables Upper Bound
%% ABC Settings
MaxIt=200; % Maximum Number of Iterations
nPop=100; % Population Size (Colony Size)
nOnlooker=nPop; % Number of Onlooker Bees
L=round(0.6*nVar*nPop); % Abandonment Limit Parameter (Trial Limit)
a=1; % Acceleration Coefficient Upper Bound
%% Initialization
% Empty Bee Structure
empty_bee.Position=[];
empty_bee.Cost=[];
% Initialize Population Array
pop=repmat(empty_bee,nPop,1);
% Initialize Best Solution Ever Found
BestSol.Cost=inf;
% Create Initial Population
for i=1:nPop
pop(i).Position=unifrnd(VarMin,VarMax,VarSize);
pop(i).Cost=CostFunction(pop(i).Position);
if pop(i).Cost<=BestSol.Cost
BestSol=pop(i);
end
end
% Abandonment Counter
C=zeros(nPop,1);
% Array to Hold Best Cost Values
BestCost=zeros(MaxIt,1);
%% ABC Main Loop
for it=1:MaxIt
% Recruited Bees
for i=1:nPop
% Choose k randomly, not equal to i
K=[1:i-1 i+1:nPop];
k=K(randi([1 numel(K)]));
% Define Acceleration Coeff.
phi=a*unifrnd(-1,+1,VarSize);
% New Bee Position
newbee.Position=pop(i).Position+phi.*(pop(i).Position-pop(k).Position);
% Evaluation
newbee.Cost=CostFunction(newbee.Position);
% Comparision
if newbee.Cost<=pop(i).Cost
pop(i)=newbee;
else
C(i)=C(i)+1;
end
end
% Calculate Fitness Values and Selection Probabilities
F=zeros(nPop,1);
MeanCost = mean([pop.Cost]);
for i=1:nPop
F(i) = exp(-pop(i).Cost/MeanCost); % Convert Cost to Fitness
end
P=F/sum(F);
% Onlooker Bees
for m=1:nOnlooker
% Select Source Site
i=RouletteWheelSelection(P);
% Choose k randomly, not equal to i
K=[1:i-1 i+1:nPop];
k=K(randi([1 numel(K)]));
% Define Acceleration Coeff.
phi=a*unifrnd(-1,+1,VarSize);
% New Bee Position
newbee.Position=pop(i).Position+phi.*(pop(i).Position-pop(k).Position);
% Evaluation
newbee.Cost=CostFunction(newbee.Position);
% Comparision
if newbee.Cost<=pop(i).Cost
pop(i)=newbee;
else
C(i)=C(i)+1;
end
end
% Scout Bees
for i=1:nPop
if C(i)>=L
pop(i).Position=unifrnd(VarMin,VarMax,VarSize);
pop(i).Cost=CostFunction(pop(i).Position);
C(i)=0;
end
end
% Update Best Solution Ever Found
for i=1:nPop
if pop(i).Cost<=BestSol.Cost
BestSol=pop(i);
end
end
% Store Best Cost Ever Found
BestCost(it)=BestSol.Cost;
% Display Iteration Information
disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
end
%% Results
figure;
%plot(BestCost,'LineWidth',2);
semilogy(BestCost,'LineWidth',2);
xlabel('Iteration');
ylabel('Best Cost');
grid on;
img =gcf; %获取当前画图的句柄
print(img, '-dpng', '-r600', './运行结果.png') %即可得到对应格式和期望dpi的图像
⛄ 运行结果
⛄ 参考文献
[1]李文越, 周思源, 庞京城. 基于人工蜂群算法优化BP神经网络的交通流预测[J]. 山东交通学院学报, 2017, 25(1):6.
[2]徐健, 陈倩倩, 刘秀平. 基于交叉运算的人工蜂群优化BP神经网络的脑电信号分类[J]. 激光与光电子学进展, 2020.
