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智能优化算法  神经网络预测 雷达通信  无线传感器

信号处理 图像处理 路径规划 元胞自动机 无人机  电力系统

⛄ 内容介绍

随着科学技术的日益进步,各行各业的发展几乎都要依赖电力的支持,电力系统稳定安全运行已经关系到国家经济的发展.在电力系统运行和控制中,经济调度计算问题研究占有重要地位,在满足可靠供电和电能质量前提下,对电力系统运行的经济性进行优化,使系统获得巨大的经济效益,因此,电力系统经济调度研究具有极大的实际应用价值.

⛄ 部分代码

%

% ======================================================== %    

% -------------------------------------------------------- %

% -------------------------------------------------------------------

% This is a simple demo version only implemented the basic          %

% idea of the bat algorithm without fine-tuning(微调)the parameters,     % 

% Then, though this demo works very well, it is expected that       %

% this demo is much less efficient than the work reported in        % 

% the following papers:                                             %

% (Citation details):                                               %

% 1) Yang X.-S., A new metaheuristic bat-inspired algorithm,        %

%    in: Nature Inspired Cooperative Strategies for Optimization    %

%    (NISCO 2010) (Eds. J. R. Gonzalez et al.), Studies in          %

%    Computational Intelligence, Springer, vol. 284, 65-74 (2010).  %

% 2) Yang X.-S., Nature-Inspired Metaheuristic Algorithms,          %

%    Second Edition, Luniver Presss, Frome, UK. (2010).             %

% 3) Yang X.-S. and Gandomi A. H., Bat algorithm: A novel           %

%    approach for global engineering optimization,                  %

%    Engineering Computations, Vol. 29, No. 5, pp. 464-483 (2012).  %

% -------------------------------------------------------------------

% Main programs starts here

function [best,fmin,N_iter]=bat_algorithm(para)

% Display help

 help bat_algorithm.m

% Default parameters 默认参数

if nargin<1,  para=[20 1000 0.5 0.5];  end

n=para(1);      % Population size, typically10 to 40

N_gen=para(2);  % Number of generations

A=para(3);      % Loudness  (constant or decreasing)

r=para(4);      % Pulse rate (constant or decreasing)

% This frequency range determines the scalings

% You should change these values if necessary

Qmin=0;         % Frequency minimum

Qmax=2;         % Frequency maximum

% Iteration parameters

N_iter=0;       % Total number of function evaluations   %这是什么意思？？？

% Dimension of the search variables

d=10;           % Number of dimensions 

% Lower limit/bounds/ a vector

Lb=-2*ones(1,d);

% Upper limit/bounds/ a vector

Ub=2*ones(1,d);   

% Initializing arrays

Q=zeros(n,1);   % Frequency

v=zeros(n,d);   % Velocities

% Initialize the population/solutions

for i=1:n,

  Sol(i,:)=Lb+(Ub-Lb).*rand(1,d);

  Fitness(i)=Fun(Sol(i,:));

end

% Find the initial best solution

[fmin,I]=min(Fitness);   %返回多个参数的时候用[ ],fmin接受第一个参数，I接受第二个参数

%这里fmin是最小值，I是最小值的索引，也就是第几个

best=Sol(I,:);

% ======================================================  %

% Note: As this is a demo, here we did not implement the  %

% reduction of loudness and increase of emission rates.   %

% Interested readers can do some parametric studies       %

% and also implementation various changes of A and r etc  %

% ======================================================  %

% Start the iterations -- Bat Algorithm (essential part)  %

for t=1:N_gen, 

% Loop over all bats/solutions

        for i=1:n,

          Q(i)=Qmin+(Qmin-Qmax)*rand;%其中rand产生一个0到1的随机数

          v(i,:)=v(i,:)+(Sol(i,:)-best)*Q(i);

          S(i,:)=Sol(i,:)+v(i,:);

          % Apply simple bounds/limits

          Sol(i,:)=simplebounds(Sol(i,:),Lb,Ub);

          % Pulse rate

          if rand>r

          % The factor 0.001 limits the step sizes of random walks 

              S(i,:)=best+0.001*randn(1,d);

          end

     % Evaluate new solutions

           Fnew=Fun(S(i,:));

     % Update if the solution improves, or not too loud

           if (Fnew<=Fitness(i)) & (rand<A) ,

                Sol(i,:)=S(i,:);

                Fitness(i)=Fnew;

           end

          % Update the current best solution

          if Fnew<=fmin,

                best=S(i,:);

                fmin=Fnew;

          end

        end

        N_iter=N_iter+n;

         

end

% Output/display

disp(['Number of evaluations: ',num2str(N_iter)]);

disp(['Best =',num2str(best),' fmin=',num2str(fmin)]);

% Application of simple limits/bounds

function s=simplebounds(s,Lb,Ub)

  % Apply the lower bound vector

  ns_tmp=s;

  I=ns_tmp<Lb;

  ns_tmp(I)=Lb(I);

  

  % Apply the upper bound vector 

  J=ns_tmp>Ub;

  ns_tmp(J)=Ub(J);

  % Update this new move 

  s=ns_tmp;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Objective function: your own objective function can be written here

% Note: When you use your own function, please remember to 

%       change limits/bounds Lb and Ub (see lines 52 to 55) 

%       and the number of dimension d (see line 51). 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function z=Fun(u)

% Sphere function with fmin=0 at (0,0,...,0)

z=sum(u.^2);

%%%%% ============ end ====================================

⛄ 运行结果

⛄ 参考文献

[1]陈相吾. 基于改进蝙蝠算法的多能互补微电网优化调度研究[D]. 西安理工大学, 2019.

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