WSN通信基于matlab生物地理学优化HWSN节能聚类协议含Matlab源码 1989期

Posted 2022-07-20 海神之光

一、生物地理算法简介

1 基本思路
BBO 算法起源于生物地理学，它通过模拟多物种在不同栖息地的分布、迁移、突变等规律求解寻优问题，在多目标规划领域有广泛应用. 栖息地被认为是独立的区域，不同的栖息地拥有不同的适宜指数HSI（Habitat suitability index）。 HSI较高的栖息地物种丰富度较高，随着种群趋于饱和，其迁出率增高，迁入率减少，而HIS较低的栖息地与之相反，迁入率增高，迁出率减少. 当栖息地遭遇灾害或瘟疫等突发事件时，HIS将随之突变，打破动态平衡，为低HIS的栖息地添加了不可预见性，增大了搜索目标解的几率2.2 迁移和突变操作物种的迁移有其具体的物理模型，最常的有线性模型、二次模型、余弦模型等 . 以图 3线性模型为例，当某栖息地物种数目为 0 时迁入率最高，此刻 λ = I,随着迁入物种数目不断增加，受阳光、水、食物等资源限制，迁入率不断降低，迁出率不断增高 . 当栖息地物种数目为 S0时，恰好达到动态平衡，此时迁出率与迁入率相同 . 而栖息地达到饱和状态时，物种数量达到最大值Smax ,此刻不再有物种迁入，迁出率 μ = E.突变操作基于生物地理学统计公式完成：


式中：ms为栖息地发生突变的概率，mmax为最大突变率，用户可自行设定 . ps为栖息地容纳s种物种的概率， pmax代表容纳最大种群的概率。

二、部分源代码

%%  


clc;

[bestfit, averagefit,CHNum,STATISTICS]=start();
function [Chromosome,Fitness,CHNum] = BBO(ProbFlag,Fitness,PopulationSize,NumberOfNodes,Chromosome,Sensor,Sink,ETX,ERX,Efs,Emp,EDA,do,NumofCH,Totenergy)

if ~exist('ProbFlag', 'var')
    ProbFlag = true;
end
pmodify = 1; % habitat modification probability
pmutate = 0.5; % initial mutation probability

Keep = 2; % elitism parameter: how many of the best habitats to keep from one generation to the next
lambdaLower = 0.0; % lower bound for immigration probabilty per gene
lambdaUpper = 1; % upper bound for immigration probabilty per gene
dt = 1; % step size used for numerical integration of probabilities
I = 1; % max immigration rate for each island
E = 1; % max emigration rate, for each island
P = PopulationSize; %max species count, for each island

for j = 1 : size(Chromosome,1)
    Prob(j) = 1 / size(Chromosome,1);
end

% Begin the optimization loop
for GenIndex = 1 : 20
%     GenIndex
    % Sorting of population
    [Chromosome,indices,Fitness]=PopSort(Chromosome,Fitness);
    MinFitness = [Fitness(1)];
    % Compute the average cost
   [AverageFitness] = ComputeAveCost(Chromosome,Fitness);
    AvgFitness = [AverageFitness];
    % Save the best habitats in a temporary array.
    for j = 1 : Keep
        chromKeep(j,:) = Chromosome(j,:);
        costKeep(j) = Fitness(j);
    end
    
    % Map cost values to species counts.
    
    for i = 1 : size(Chromosome,1)
        if Fitness(i)< inf
            SpeciesCount(i) = P - i;
        else
            SpeciesCount(i) = 0;
        end
    end

    % Compute immigration rate and emigration rate for each species count.
    for i = 1 : size(Chromosome,1)
        lambda(i) = I * (1 - SpeciesCount(i) / P);
        mu(i) = E * SpeciesCount(i) / P;
    end
    
    if ProbFlag
        % Compute the time derivative of Prob(i) for each habitat i.
        for j = 1 : size(Chromosome,1)
            
            lambdaMinus = I * (1 - (SpeciesCount(j) - 1) / P);
            muPlus = E * (SpeciesCount(j) + 1) / P;
            if j < size(Chromosome,1)
                ProbMinus = Prob(j+1);
            else
                ProbMinus = 0;
            end
            if j > 1
                ProbPlus = Prob(j-1);
            else
                ProbPlus = 0;
            end
            ProbDot(j) = -(lambda(j) + mu(j)) * Prob(j) + lambdaMinus * ProbMinus + muPlus * ProbPlus;
        end
        % Compute the new probabilities for each species count.
        Prob = Prob + ProbDot * dt;
        Prob = max(Prob, 0);
        Prob = Prob / sum(Prob);
    end
    % Now use lambda and mu to decide how much information to share between habitats.
    lambdaMin = min(lambda);
    lambdaMax = max(lambda);
    for k = 1 : size(Chromosome,1)
        if rand > pmodify
            continue;
        end
        % Normalize the immigration rate.
        lambdaScale = lambdaLower + (lambdaUpper - lambdaLower) * (lambda(k) - lambdaMin) / (lambdaMax - lambdaMin);
        % Probabilistically input new information into habitat i
        for j = 1 : NumberOfNodes
            if (Chromosome(k,j) ~= -1) && rand < lambdaScale
                % Pick a habitat from which to obtain a feature
                RandomNum = rand * sum(mu);
                Select = mu(1);
                SelectIndex = 1;
                while (RandomNum > Select) && (SelectIndex < PopulationSize)
                    SelectIndex = SelectIndex + 1;
                    Select = Select + mu(SelectIndex);
                end
                Chromosome(k,j) = Chromosome(SelectIndex,j);
            else
                Chromosome(k,j) = Chromosome(k,j);
            end
        end
    end
    if ProbFlag
        % Mutation
        Pmax = max(Prob);
        MutationRate = pmutate * (1 - Prob / Pmax);
        
        % Sorting of population
        [Chromosome,indices,Fitness]=PopSort(Chromosome,Fitness);
        
        % Mutate only the worst half of the solutions
        for k = 1 : size(Chromosome,1)
            for parnum = 1 : NumberOfNodes
                if (Chromosome(k,parnum) ~= -1) && MutationRate(k) > rand
                    if( Chromosome(k,parnum) == 0)
                        Chromosome(k,parnum)= 1;
                    else
                        Chromosome(k,parnum) = 0;
                    end
                end
            end
        end
        

三、运行结果

四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1] 包子阳,余继周,杨杉.智能优化算法及其MATLAB实例（第2版）[M].电子工业出版社，2016.
[2]张岩,吴水根.MATLAB优化算法源代码[M].清华大学出版社，2017.

3 备注
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