1 简介
无线传感器网络（Wireless Sensor Network，WSN）是由部署在目标区域内一定数量的传感器节点组成的无线通信网络，可实现对目标区域中物理信号的采集、监测、传输等功能。与传统有线网络相比，无线传感器网络具有自组织、灵活、容错和快速部署等特点，广泛应用于环境监测、天气预报、家居生活、国防军事、医疗护理等领域。作为无线传感器网络的核心支撑技术，定位技术是当前研究热点，没有位置属性的消息是毫无意义的，同时定位技术也是研究其他技术的基础。在基于测距的无线传感器网络节点定位中，最小二乘法由于定位误差的累积，定位精度不高。针对该问题，提出了一种基于入侵杂草优化算法和花授粉混合算法的定位方法。该算法以定位误差为适应度函数，将定位问题转换为求解非线性方程组最优化问题。在求解的过程中，利用未知节点到锚节点的距离和锚节点可信度对适应度函数进行修正，以实现更高精度的定位。仿真实验表明：改进的定位算法，在不同测距误差、不同通信半径、不同锚节点数和不同节点数下，都能得到更高的定位精度。
2 部分代码
clc;
clear;
close all;
%% Problem Definition
CostFunction = @(x) Sphere(x); % Objective Function
nVar = 5; % Number of Decision Variables
VarSize = [1 nVar]; % Decision Variables Matrix Size
VarMin = -10; % Lower Bound of Decision Variables
VarMax = 10; % Upper Bound of Decision Variables
%% IWO Parameters
MaxIt = 200; % Maximum Number of Iterations
nPop0 = 10; % Initial Population Size
nPop = 25; % Maximum Population Size
Smin = 0; % Minimum Number of Seeds
Smax = 5; % Maximum Number of Seeds
Exponent = 2; % Variance Reduction Exponent
sigma_initial = 0.5; % Initial Value of Standard Deviation
sigma_final = 0.001;% Final Value of Standard Deviation
%% Initialization
% Empty Plant Structure
empty_plant.Position = [];
empty_plant.Cost = [];
pop = repmat(empty_plant, nPop0, 1); % Initial Population Array
for i = 1:numel(pop)
% Initialize Position
pop(i).Position = unifrnd(VarMin, VarMax, VarSize);
% Evaluation
pop(i).Cost = CostFunction(pop(i).Position);
end
% Initialize Best Cost History
BestCosts = zeros(MaxIt, 1);
%% IWO Main Loop
for it = 1:MaxIt
% Update Standard Deviation
sigma = ((MaxIt - it)/(MaxIt - 1))^Exponent * (sigma_initial - sigma_final) + sigma_final;
% Get Best and Worst Cost Values
Costs = [pop.Cost];
BestCost = min(Costs);
WorstCost = max(Costs);
% Initialize Offsprings Population
newpop = [];
% Reproduction
for i = 1:numel(pop)
ratio = (pop(i).Cost - WorstCost)/(BestCost - WorstCost);
S = floor(Smin + (Smax - Smin)*ratio);
for j = 1:S
% Initialize Offspring
newsol = empty_plant;
% Generate Random Location
newsol.Position = pop(i).Position + sigma * randn(VarSize);
% Apply Lower/Upper Bounds
newsol.Position = max(newsol.Position, VarMin);
newsol.Position = min(newsol.Position, VarMax);
% Evaluate Offsring
newsol.Cost = CostFunction(newsol.Position);
% Add Offpsring to the Population
newpop = [newpop
newsol]; %#ok
end
end
% Merge Populations
pop = [pop
newpop];
% Sort Population
[~, SortOrder]=sort([pop.Cost]);
pop = pop(SortOrder);
% Competitive Exclusion (Delete Extra Members)
if numel(pop)>nPop
pop = pop(1:nPop);
end
% Store Best Solution Ever Found
BestSol = pop(1);
% Store Best Cost History
BestCosts(it) = BestSol.Cost;
% Display Iteration Information
disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCosts(it))]);
end
%% Results
figure;
% plot(BestCosts,'LineWidth',2);
semilogy(BestCosts,'LineWidth',2);
xlabel('Iteration');
ylabel('Best Cost');
grid on;
img =gcf; %获取当前画图的句柄
print(img, '-dpng', '-r600', './运行结果.png') %即可得到对应格式和期望dpi的图像
3 仿真结果
4 参考文献
[1]陈志泊, 张蕾蕾, 李巨虎,等. 基于入侵杂草优化算法的无线传感网节点定位[J]. 计算机工程与应用, 2014, 50(9):77-82.
部分理论引用网络文献，若有侵权联系博主删除。
