一、 概述
来源：
多层感知器（MLP）作为使用最广泛的神经网络（NN）之一，已被应用于许多实际问题。MLP 需要针对特定应用程序进行培训，经常会遇到局部最小值、收敛速度和初始化敏感性问题。本文建议使用最近开发的基于生物地理学的优化（BBO）算法来训练MLP以减少这些问题。为了研究BBO在训练MLP中的效率，使用了五个分类数据集以及六个函数近似数据集。将结果与5种著名的启发式算法反向传播（BP）和极限学习机（ELM）在局部最小值的捕获、结果精度和收敛率方面进行了比较。结果表明，利用BBO训练MLP明显优于目前的启发式学习算法和BP。此外，结果表明，与ELM相比，BBO能够提供非常有竞争力的结果。
二、运行结果
三、参考文献
[1]Seyedali Mirjalili (2022). Biogeography-Based Optimizer (BBO) for training Multi-Layer Perceptron (MLP) .
https://doi.org/10.1016/j.ins.2014.01.038
部分代码：
完整代码：回复关键字——基于生物地理学的优化算法
function [MinCost,Best] = ACO(ProblemFunction, DisplayFlag) % Ant colony optimization algorithm for optimizing a general function. % INPUTS: ProblemFunction is the handle of the function that returns % the handles of the initialization, cost, and feasibility functions. % DisplayFlag says whether or not to display information during iterations and plot results. if ~exist('DisplayFlag', 'var') DisplayFlag = true; end [OPTIONS, MinCost, AvgCost, InitFunction, CostFunction, FeasibleFunction, ... MaxParValue, MinParValue, Population] = Init(DisplayFlag, ProblemFunction); Keep = 2; % elitism parameter: how many of the best individuals to keep from one generation to the next % ACO parameter initialization tau0 = 1e-6; % initial pheromone value, between 0 and 0.5 Q = 20; % pheromonone update constant, between 0 and 100 q0 = 1; % exploration constant, between 0 and 1 rhog = 0.9; % global pheromone decay rate, between 0 and 1 rhol = 0.5; % local pheromone decay rate, between 0 and 1 alpha = 1; % pheromone sensitivity, between 1 and 5 beta = 5; % visibility sensitivity, between 0 and 15 tau = tau0 * ones(MaxParValue-MinParValue+1, 1); % initial pheromone values p = zeros(size(tau)); % allocate array for probabilities % Begin the optimization loop for GenIndex = 1 : OPTIONS.Maxgen % pheromone decay tau = (1 - rhog) * tau; % Use each solution to update the pheromone for each parameter value for k = 1 : OPTIONS.popsize Cost = Population(k).cost; Chrom = Population(k).chrom; for i = 1 : length(Chrom) j = Chrom(i); j=floor(j); if (Cost == 0) tau(j-MinParValue+1) = max(tau); else tau(j-MinParValue+1) = tau(j-MinParValue+1) + Q / Cost; end end end % Use the probabilities to generate new solutions for k = Keep+1 : OPTIONS.popsize for j = 1 : OPTIONS.numVar % Generate probabilities based on pheromone amounts p = tau .^ alpha; p = p / sum(p); [Maxp, Maxpindex] = max(p); if rand < q0 Select_index = Maxpindex; else SelectProb = p(1); Select_index = 1; RandomNumber = rand; while SelectProb < RandomNumber Select_index = Select_index + 1; if Select_index >= MaxParValue - MinParValue + 1 break; end SelectProb = SelectProb + p(Select_index); end end Population(k).chrom(j) = MinParValue + Select_index - 1; % local pheromone update tau(Select_index) = (1 - rhol) * tau(Select_index) + rhol * tau0; end end % Make sure the population does not have duplicates. Population = ClearDups(Population, MaxParValue, MinParValue); % Make sure each individual is legal. Population = FeasibleFunction(OPTIONS, Population); % Calculate cost Population = CostFunction(OPTIONS, Population); % Sort from best to worst Population = PopSort(Population); % Compute the average cost of the valid individuals [AverageCost, nLegal] = ComputeAveCost(Population); % Display info to screen MinCost = [MinCost Population(1).cost]; AvgCost = [AvgCost AverageCost]; if DisplayFlag disp(['The best and mean of Generation # ', num2str(GenIndex), ' are ',... num2str(MinCost(end)), ' and ', num2str(AvgCost(end))]); end end Best=Conclude(DisplayFlag, OPTIONS, Population, nLegal, MinCost); return;
