【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 rateUpdateFromBest = 1; % number of good population members to use to update the probability vector each generationUpdateFromWorst = 0; % number of bad population members to use to update the probability vector each generationKeep = 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 rateshiftMutate = 0.1; % probability vector mutation shift magnitudeepsilon = 1e-6;ProbVec = 0.5 * ones(1, OPTIONS.numVar); % initial probability vector% Begin the evolution loopfor 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));endBest=Conclude(DisplayFlag, OPTIONS, Population, nLegal, MinCost);if DisplayFlag    disp(['Probability Vector = ', num2str(ProbVec)]);endreturn;