人工蜂群算法(ABC)优化BP神经网络分类预测，ABC-BP分类预测，多特征输入模型 多特征输入单输出的二分类及多分类模型

function [bestcord,bestpos,BestCost] = ABC(nPop,MaxIt,VarMin,VarMax,nVar,fobj) % MaxIt = 200; % 最大迭代次数 % nPop = 100; % 蜂群大小 VarSize = [1 nVar]; % 变量矩阵 nOnlooker = nPop; % 侦察蜂个数 L = round(0.6*nVar*nPop); % 探索极值限制参数 a = 1; % 加速度系数上限 %% Initialization %% 初始化 % 置空蜜蜂矩阵 empty_bee.Position = []; empty_bee.Cost = []; % 初始化蜂群数组 pop = repmat(empty_bee, nPop, 1); % 初始化最优解 BestSol.Cost = inf; % 产生初始种群 for i = 1:nPop pop(i).Position = unifrnd(VarMin, VarMax, VarSize); pop(i).Cost = fobj(pop(i).Position); if pop(i).Cost <= BestSol.Cost BestSol = pop(i); end end % 丢解计数器 C = zeros(nPop, 1); % 保存最优函数值的数组 BestCost = zeros(MaxIt, 1); %% ABC迭代 for it = 1:MaxIt % 引领蜂 for i = 1:nPop % 随机选择不等于i的k K = [1:i-1 i+1:nPop]; k = K(randi([1 numel(K)])); % 定义加速度系数 phi = a*unifrnd(-1, +1, VarSize); % 新的蜜蜂位置 newbee.Position = pop(i).Position+phi.*(pop(i).Position-pop(k).Position); % 边界处理 newbee.Position = max(newbee.Position, VarMin); newbee.Position = min(newbee.Position, VarMax); % 新的蜜蜂函数值 newbee.Cost = fobj(newbee.Position); % 比较 if newbee.Cost <= pop(i).Cost pop(i) = newbee; else C(i) = C(i)+1; end end % 计算适应度值和选择概率 F = zeros(nPop, 1); MeanCost = mean([pop.Cost]); for i = 1:nPop % 将函数值转换为适应度 if pop(i).Cost >= 0 F(i) = 1/(1+pop(i).Cost); else F(i) = 1+abs(pop(i).Cost); end end P = F/sum(F); % 跟随蜂 for m = 1:nOnlooker % 选择食物源 i = RouletteWheelSelection(P); % 随机选择不等于i的k K = [1:i-1 i+1:nPop]; k = K(randi([1 numel(K)])); % 定义加速度系数 phi = a*unifrnd(-1, +1, VarSize); % 新的蜜蜂位置 newbee.Position = pop(i).Position+phi.*(pop(i).Position-pop(k).Position); % 边界处理 newbee.Position = max(newbee.Position, VarMin); newbee.Position = min(newbee.Position, VarMax); % 新的蜜蜂函数值 newbee.Cost = fobj(newbee.Position); % 比较 if newbee.Cost <= pop(i).Cost pop(i) = newbee; else C(i) = C(i) + 1; end end % 侦察蜂 for i = 1:nPop if C(i) >= L % 超出探索极值参数 maxPos = max(pop(i).Position); minPos = min(pop(i).Position); for j = 1:numel(pop(i).Position) pop(i).Position(j) = minPos+rand*(maxPos-minPos); end pop(i).Cost = fobj(pop(i).Position); C(i) = 0; end end % 更新每轮最优解 for i = 1:nPop if pop(i).Cost <= BestSol.Cost BestSol = pop(i); end end % 保存每轮最优解 BestCost(it) = BestSol.Cost; % 显示迭代信息 disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]); end bestcord = BestSol.Cost bestpos = BestSol.Position; end