![% ======================================================== %
% 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 0.25 0.5]); %
function [best,fmin,N_iter]=bat_algorithm(para)
% Display help
help bat_algorithm.m
% Default parameters
if nargin<1, para=[10 0.25 0.5]; end
n=para(1); % Population size, typically 10 to 25
A=para(2); % Loudness (constant or decreasing)
r=para(3); % Pulse rate (constant or decreasing)
% This frequency range determines the scalings
Qmin=0; % Frequency minimum
Qmax=2; % Frequency maximum
% Iteration parameters
tol=10^(-5); % Stop tolerance
N_iter=0; % Total number of function evaluations
% Dimension of the search variables
d=3;
% Initial arrays
Q=zeros(n,1); % Frequency
v=zeros(n,d); % Velocities
% Initialize the population/solutions
for i=1:n,
Sol(i,:)=randn(1,d);
Fitness(i)=Fun(Sol(i,:));
end
% Find the current best
[fmin,I]=min(Fitness);
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
while (fmin>tol)
% Loop over all bats/solutions
for i=1:n,
Q(i)=Qmin+(Qmin-Qmax)*rand;
v(i,:)=v(i,:)+(Sol(i,:)-best)*Q(i);
S(i,:)=Sol(i,:)+v(i,:);
% Pulse rate
if rand>r
S(i,:)=best+0.01*randn(1,d);
end
% Evaluate new solutions
Fnew=Fun(S(i,:));
% If the solution improves or not too loudness
if (Fnew<=Fitness(i)) & (rand<A) ,](https://image.slidesharecdn.com/batalgorithm-160331212158/85/Bat-algorithm-demo-1-320.jpg)
![Sol(i,:)=S(i,:);
Fitness(i)=Fnew;
end
% Update the current best
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)]);
% Objective function -- Rosenbrock's 3D function
function z=Fun(u)
z=(1-u(1))^2+100*(u(2)-u(1)^2)^2+100*(u(3)-u(2)^2)^2;
%%%%% ============ end ====================================](https://image.slidesharecdn.com/batalgorithm-160331212158/85/Bat-algorithm-demo-2-320.jpg)
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Bat algorithm (demo)
- 1. % ======================================================== % % 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 0.25 0.5]); % function [best,fmin,N_iter]=bat_algorithm(para) % Display help help bat_algorithm.m % Default parameters if nargin<1, para=[10 0.25 0.5]; end n=para(1); % Population size, typically 10 to 25 A=para(2); % Loudness (constant or decreasing) r=para(3); % Pulse rate (constant or decreasing) % This frequency range determines the scalings Qmin=0; % Frequency minimum Qmax=2; % Frequency maximum % Iteration parameters tol=10^(-5); % Stop tolerance N_iter=0; % Total number of function evaluations % Dimension of the search variables d=3; % Initial arrays Q=zeros(n,1); % Frequency v=zeros(n,d); % Velocities % Initialize the population/solutions for i=1:n, Sol(i,:)=randn(1,d); Fitness(i)=Fun(Sol(i,:)); end % Find the current best [fmin,I]=min(Fitness); 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 while (fmin>tol) % Loop over all bats/solutions for i=1:n, Q(i)=Qmin+(Qmin-Qmax)*rand; v(i,:)=v(i,:)+(Sol(i,:)-best)*Q(i); S(i,:)=Sol(i,:)+v(i,:); % Pulse rate if rand>r S(i,:)=best+0.01*randn(1,d); end % Evaluate new solutions Fnew=Fun(S(i,:)); % If the solution improves or not too loudness if (Fnew<=Fitness(i)) & (rand<A) ,
- 2. Sol(i,:)=S(i,:); Fitness(i)=Fnew; end % Update the current best 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)]); % Objective function -- Rosenbrock's 3D function function z=Fun(u) z=(1-u(1))^2+100*(u(2)-u(1)^2)^2+100*(u(3)-u(2)^2)^2; %%%%% ============ end ====================================