生物地理学基础优化源代码.zip

function [MinCost] = BBO(ProblemFunction, DisplayFlag, RandSeed) % Biogeography-based optimization (BBO) software for minimizing a continuous function % INPUTS: ProblemFunction = the handle of the function that returns the handles of the initialization and cost functions. % DisplayFlag = true or false, whether or not to display and plot results. % RandSeed = random number seed % OUTPUTS: MinCost = array of best solution, one element for each generation if ~exist('DisplayFlag', 'var') DisplayFlag = true; end if ~exist('RandSeed', 'var') RandSeed = round(sum(100*clock)); end OPTIONS.Maxgen = 50; % generation count limit OPTIONS.popsize = 50; % population size OPTIONS.numVar = 20; % number of variables in each population member (i.e., problem dimension) PMutate = 0.01; % mutation probability Keep = 2; % elitism parameter: how many of the best habitats to keep from one generation to the next % Initialization [MinCost, AvgCost, CostFunction, MaxParValue, MinParValue, Population, OPTIONS] = ... Init(DisplayFlag, ProblemFunction, RandSeed, OPTIONS); EliteSolution = zeros(Keep, OPTIONS.numVar); EliteCost = zeros(Keep, 1); Island = zeros(OPTIONS.popsize, OPTIONS.numVar); % Compute immigration and emigration rates. % lambda(i) is the immigration rate for habitat i. % mu(i) is the emigration rate for habitat i. % This assumes the population is sorted from most fit to least fit. mu = (OPTIONS.popsize + 1 - (1:OPTIONS.popsize)) / (OPTIONS.popsize + 1); lambda = 1 - mu; % Begin the optimization loop for GenIndex = 1 : OPTIONS.Maxgen % Save the best habitats in a temporary array. for j = 1 : Keep EliteSolution(j,:) = Population(j).chrom; EliteCost(j) = Population(j).cost; end % Use lambda and mu to decide how much information to share between habitats. for k = 1 : length(Population) % Probabilistically input new information into habitat i for j = 1 : OPTIONS.numVar if rand < lambda(k) % Pick a habitat from which to obtain a feature (roulette wheel selection) RandomNum = rand * sum(mu); Select = mu(1); SelectIndex = 1; while (RandomNum > Select) && (SelectIndex < OPTIONS.popsize) SelectIndex = SelectIndex + 1; Select = Select + mu(SelectIndex); end Island(k,j) = Population(SelectIndex).chrom(j); else Island(k,j) = Population(k).chrom(j); end end end % Mutation for k = 1 : length(Population) for parnum = 1 : OPTIONS.numVar if PMutate > rand Island(k,parnum) = MinParValue + (MaxParValue - MinParValue) * rand; end end end % Replace the habitats with their new versions. for k = 1 : length(Population) Population(k).chrom = Island(k,:); end % Calculate cost Population = CostFunction(Population); % Sort from best to worst Population = PopSort(Population); % Replace the current generation's worst individuals with the previous generation's elites. n = length(Population); for k = 1 : Keep Population(n-k+1).chrom = EliteSolution(k,:); Population(n-k+1).cost = EliteCost(k); end % Make sure the population does not have duplicates. Population = ClearDups(Population, OPTIONS, MaxParValue, MinParValue, CostFunction); % Sort from best to worst Population = PopSort(Population); % Display info to screen MinCost(GenIndex+1) = Population(1).cost; AvgCost(GenIndex+1) = mean([Population.cost]); if DisplayFlag disp(['The best and mean of Generation # ', num2str(GenIndex), ' are ',... num2str(MinCost(GenIndex+1)), ' and ', num2str(AvgCost(GenIndex+1))]); end end Conclude(DisplayFlag, OPTIONS, Population, MinCost, AvgCost); return