【优化算法】蛙跳算法 (SFLA)【含Matlab源码 1839期】.zip

% % % function pop = RunFLA(pop, params) %%FLA 参数 q = params.q; % 父母人数 alpha = params.alpha; % 后代数量 beta = params.beta; %最大迭代次数 sigma = params.sigma; CostFunction = params.CostFunction; VarMin = params.VarMin; VarMax = params.VarMax; VarSize = size(pop(1).Position); BestSol = params.BestSol; nPop = numel(pop); % 选择概率 P = 2*(nPop+1-(1:nPop))/(nPop*(nPop+1)); % 选择概率 % 计算种群范围（最小超立方体） LowerBound = pop(1).Position; UpperBound = pop(1).Position; for i = 2:nPop LowerBound = min(LowerBound, pop(i).Position); UpperBound = max(UpperBound, pop(i).Position); end %%FLA 主循环 for it = 1:beta %选择父母 L = RandSample(P,q); B = pop(L); %产生后代 for k=1:alpha %对人口进行排序 [B, SortOrder] = SortPopulation(B); L = L(SortOrder); % 标志 ImprovementStep2 = false; Censorship = false; % 改进步骤 1 NewSol1 = B(end); Step = sigma*rand(VarSize).*(B(1).Position-B(end).Position); NewSol1.Position = B(end).Position + Step; if IsInRange(NewSol1.Position, VarMin, VarMax) NewSol1.Cost = CostFunction(NewSol1.Position); if NewSol1.Cost<B(end).Cost B(end) = NewSol1; else ImprovementStep2 = true; end else ImprovementStep2 = true; end %改进步骤 2 if ImprovementStep2 NewSol2 = B(end); Step = sigma*rand(VarSize).*(BestSol.Position-B(end).Position); NewSol2.Position = B(end).Position + Step; if IsInRange(NewSol2.Position, VarMin, VarMax) NewSol2.Cost = CostFunction(NewSol2.Position); if NewSol2.Cost<B(end).Cost B(end) = NewSol2; else Censorship = true; end else Censorship = true; end end % 审查制度 if Censorship B(end).Position = unifrnd(LowerBound, UpperBound); B(end).Cost = CostFunction(B(end).Position); end end % 将子复合体返回到主复合体 pop(L) = B; end end