【WSN】无线传感器网络模拟器研究Matlab代码实现

function [MinCost,Best] = PBIL(ProblemFunction, DisplayFlag) % Probability Based Incremental Learning (PBIL) 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); MinCost = []; AvgCost = []; LearningRate = 0.05; % PBIL learning rate UpdateFromBest = 1; % number of good population members to use to update the probability vector each generation UpdateFromWorst = 0; % number of bad population members to use to update the probability vector each generation Keep = 1; % elitism parameter: how many of the best individuals to keep from one generation to the next % The 0.5 multiplication factor below seems to be key to getting % good performance from this PBIL program. It may be an artifact of the % particular objective function that we’re optimizing because it tends to keep the % population members in the middle of their allowable ranges. Factor = 1;%0.5; pMutate = 0; % probability vector mutation rate shiftMutate = 0.1; % probability vector mutation shift magnitude epsilon = 1e-6; ProbVec = 0.5 * ones(1, OPTIONS.numVar); % initial probability vector % Begin the evolution loop for GenIndex = 0 : OPTIONS.Maxgen % Generate a population based on the probability vector. for popindex = 1 : OPTIONS.popsize if (GenIndex == 0) || (popindex > Keep) RandVec = Factor * (rand(1,OPTIONS.numVar) - 0.5) + ProbVec; RandVec = max(0, min(1-epsilon, RandVec)); chrom = floor(MinParValue + (MaxParValue - MinParValue + 1) * RandVec); Population(popindex).chrom = chrom; end end % Make sure the population does not have duplicates. Population = ClearDups(Population, MaxParValue, MinParValue); % 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 % Probability vector update from best population members for k = 1 : UpdateFromBest %ProbVec = ProbVec * (1 - LearningRate); Adjustment = (Population(k).chrom - MinParValue) / (MaxParValue - MinParValue); Adjustment = (Adjustment - ProbVec) * LearningRate; ProbVec = ProbVec + Adjustment; end % Probability vector update from worst population members for k = OPTIONS.popsize-UpdateFromWorst+1 : OPTIONS.popsize %ProbVec = ProbVec * (1 - LearningRate); Adjustment = (Population(k).chrom - MinParValue) / (MaxParValue - MinParValue); Adjustment = (ProbVec - Adjustment) * LearningRate; ProbVec = ProbVec + Adjustment; end % Mutation of the probability vector for i = 1 : OPTIONS.numVar if rand < pMutate ProbVec(i) = ProbVec(i) + shiftMutate * (rand < 0.5); end end ProbVec = max(0, min(ProbVec, 1)); end Best = Conclude(DisplayFlag, OPTIONS, Population, nLegal, MinCost); if DisplayFlag disp([’Probability Vector = ’, num2str(ProbVec)]); end return;