无线传感器网络布局优化问题采用麻雀搜索算法解决。
麻雀搜索算法的基本原理:下面的博主讲得很好，可以看看。
(2条消息) 智能优化算法：麻雀搜索算法-附代码_智能算法研究社（Jack旭）的博客-CSDN博客_麻雀搜索算法
麻雀搜索算法代码：（SSA基本框架）
function [fMin , bestX,Convergence_curve ] = SSA(pop, M,c,d,dim,fobj ) P_percent = 0.2; % The population size of producers accounts for "P_percent" percent of the total population size %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% pNum = round( pop * P_percent ); % The population size of the producers lb= c.*ones( 1,dim ); % Lower limit/bounds/ a vector ub= d.*ones( 1,dim ); % Upper limit/bounds/ a vector %Initialization for i = 1 : pop x( i, : ) = lb (ub - lb) .* rand( 1, dim ); fit( i ) = fobj( x( i, : ) ) ; end pFit = fit; pX = x; % The individual's best position corresponding to the pFit [ fMin, bestI ] = min( fit ); % fMin denotes the global optimum fitness value bestX = x( bestI, : ); % bestX denotes the global optimum position corresponding to fMin % Start updating the solutions. for t = 1 : M [ ans, sortIndex ] = sort( pFit );% Sort. [fmax,B]=max( pFit ); worse= x(B,:); r2=rand(1); if(r2<0.8) for i = 1 : pNum % Equation (3) r1=rand(1); x( sortIndex( i ), : ) = pX( sortIndex( i ), : )*exp(-(i)/(r1*M)); x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub ); fit( sortIndex( i ) ) = fobj( x( sortIndex( i ), : ) ); end else for i = 1 : pNum x( sortIndex( i ), : ) = pX( sortIndex( i ), : ) randn(1)*ones(1,dim); x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub ); fit( sortIndex( i ) ) = fobj( x( sortIndex( i ), : ) ); end end [ fMMin, bestII ] = min( fit ); bestXX = x( bestII, : ); for i = ( pNum 1 ) : pop % Equation (4) A=floor(rand(1,dim)*2)*2-1; if( i>(pop/2)) x( sortIndex(i ), : )=randn(1)*exp((worse-pX( sortIndex( i ), : ))/(i)^2); else x( sortIndex( i ), : )=bestXX (abs(( pX( sortIndex( i ), : )-bestXX)))*(A'*(A*A')^(-1))*ones(1,dim); end x( sortIndex( i ), : ) = Bounds( x( sortIndex( i ), : ), lb, ub ); fit( sortIndex( i ) ) = fobj( x( sortIndex( i ), : ) ); end c=randperm(numel(sortIndex)); b=sortIndex(c(1:20)); for j = 1 : length(b) % Equation (5) if( pFit( sortIndex( b(j) ) )>(fMin) ) x( sortIndex( b(j) ), : )=bestX (randn(1,dim)).*(abs(( pX( sortIndex( b(j) ), : ) -bestX))); else x( sortIndex( b(j) ), : ) =pX( sortIndex( b(j) ), : ) (2*rand(1)-1)*(abs(pX( sortIndex( b(j) ), : )-worse))/ ( pFit( sortIndex( b(j) ) )-fmax 1e-50); end x( sortIndex(b(j) ), : ) = Bounds( x( sortIndex(b(j) ), : ), lb, ub ); fit( sortIndex( b(j) ) ) = fobj( x( sortIndex( b(j) ), : ) ); end for i = 1 : pop if ( fit( i ) < pFit( i ) ) pFit( i ) = fit( i ); pX( i, : ) = x( i, : ); end if( pFit( i ) < fMin ) fMin= pFit( i ); bestX = pX( i, : ); end end Convergence_curve(t)=fMin; end % Application of simple limits/bounds function s = Bounds( s, Lb, Ub) % Apply the lower bound vector temp = s; I = temp < Lb; temp(I) = Lb(I); % Apply the upper bound vector J = temp > Ub; temp(J) = Ub(J); % Update this new move s = temp; %--------------------------------------------------------------------------------------------------------------------------- 麻雀搜索算法是一种封闭算法，我们如何将其应用到我们需要的场景中？
答案隐藏在第二段代码中，通过编辑Get_Functions_details(F)对于代码段中x的操作，我们可以应用麻雀搜索算法。
麻雀搜索算法是一种寻求最小值的算法。在无线传感器网络布局优化中，我们可以将覆盖率转换为未覆盖率，然后将其纳入麻雀搜索算法，以获得最佳布局。
%_________________________________________________________________________% % 基于麻雀优化WNS覆盖优化算法，3D版 % %_________________________________________________________________________% clear all clc close all rng('default'); %% 设定WNS覆盖参数, %% 默认输入参数为整数。如果您想定义小数，请乘以系数到整数进行转换。 %% 比如范围1*1，R=0.03可以转换为100*100，R=3； %区域范围为AreaX*AreaY*AreaZ AreaX = 100; AreaY = 100; N = 20 ;节点数%覆盖 R = 15；%通信半径 %% 设置麻雀优化参数 % for i=1:N rng('default'); %%设置麻雀优化参数 SearchAgents_no=50; % Number of search agents 种群数量 Max_iteration=150; % Maximum numbef of iterations 设置最大迭代次数 Function_name='F1'; % Name of the test function that can be from F1 to F23 (Table 1,2,3 in the paper) 设置适应性函数 fobj=Get_Functions_details(Function_name); %设定边界以及优化函数 lb=0; ub=100; dim=2*N; % initialization(pop,dim,ub,b);
input=randi([lb,ub],SearchAgents_no,dim);
input1=input(1,:);
input_data=reshape(input1,[],2);
[Best_position,Best_score,SSA_curve]=SSA(SearchAgents_no,Max_iteration,lb,ub,dim,fobj,AreaX,input); %开始优化
% end
%%
%Coordinate代表覆盖的坐标点的存放
%CoordinateNumber 代表覆盖的坐标点的个数
[output1,curve1] = fun(input_data,N,R,AreaX,AreaY); %未优化覆盖图
[output2,curve2] = fun(Best_position,N,R,AreaX,AreaY); %优化后覆盖图
figure
SSA_curve = 1 - SSA_curve;%将未覆盖率转换覆盖率。
plot(SSA_curve,'Color','r')
title('Objective space')
xlabel('迭代次数');
ylabel('覆盖率');
axis tight
grid on
box on
legend('SSA')
% img =gcf; %获取当前画图的句柄
% print(img, '-dpng', '-r600', './img.png') %即可得到对应格式和期望dpi的图像
% display(['最终得到的最优覆盖率 : ', num2str(1 - Best_score)]);
figure
scatter(input_data(1:N),input_data(N+1:2*N),12,'r','filled');%绘制节点
hold on;
scatter(output1(:,1),output1(:,2),4,'b','filled');%绘制覆盖区域
hold off;
axis ( [0 AreaX 0 AreaY]);
title('SSA-WSN未优化前')
grid on;
legend('WSN节点','覆盖区域')
img =gcf; %获取当前画图的句柄
print(img, '-dpng', '-r600', './img1.png') %即可得到对应格式和期望dpi的图像
curve1
curve2
figure
scatter(Best_position(1:N),Best_position(N+1:2*N),12,'r','filled');%绘制节点
hold on;
scatter(output2(:,1),output2(:,2),4,'b','filled');%绘制覆盖区域
hold off;
axis ( [0 AreaX 0 AreaY]);
title('SSA-WSN最终结果')
grid on;
legend('WSN节点','覆盖区域')
img =gcf; %获取当前画图的句柄
print(img, '-dpng', '-r600', './img1.png') %即可得到对应格式和期望dpi的图像
运行结果如下：
完整的matlab代码如下：基于麻雀搜索算法（SSA）的无线传感器网络（WSN）优化问题-算法与数据结构文档类资源-CSDN文库