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⛄ 内容介绍
耙吸挖泥船的耙头产量主要取决于耙头的吸入密度,准确的吸入密度预测对提高耙吸挖泥船疏浚产量具有重要的意义.针对目前对吸入密度预测方法存在精度低,实时效果性差的缺点,提出了一种蝙蝠算法与模糊神经网络相结合的预测方法.通过实测施工数据,构建BA-FNN预测模型.实验表明:BA-FNN预测精度高且稳定性能好,能够为耙头产量预测以及指导施工提供科学有效的参考依据.
⛄ 部分代码
% ======================================================== %
% Files of the Matlab programs included in the book: %
% Xin-She Yang, Nature-Inspired Metaheuristic Algorithms, %
% Second Edition, Luniver Press, (2010). www.luniver.com %
% ======================================================== %
% -------------------------------------------------------- %
% Bat-inspired algorithm for continuous optimization (demo)%
% Programmed by Xin-She Yang @Cambridge University 2010 %
% -------------------------------------------------------- %
% Usage: bat_algorithm([20 1000 0.5 0.5]); %
% -------------------------------------------------------------------
% 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]张容, 阎红, 杜丽萍. 基于模糊神经网络(FNN)的赤潮预警预测研究[J]. 海洋通报:英文版, 2006, 25(001):83-91.
[2]赵建强, 陈必科, 葛考, et al. 基于FOA—FNN算法的边坡稳定性评价研究[C]// 中国系统工程学会第十八届学术年会. 2014.
[3]郝光杰, 俞孟蕻, and 苏贞. "基于蝙蝠算法优化模糊神经网络的耙吸挖泥船耙头吸入密度研究." 计算机与数字工程 002(2022):050.
