A brief introduction to biogeographic algorithms
1 Basic IdeasBBO algorithm originated from biogeography. It can solve optimization problems by simulating the distribution, migration and mutation of multiple species in different habitats, and has been widely used in the field of multi-objective planning. A Habitat is considered as an independent area, and different habitats have different HSI (Habitat suitability Index). Habitats with higher HSI have higher species richness, and as the population tends to saturate, the migration rate increases and the migration rate decreases, while habitats with lower HIS, on the contrary, increase and decrease the migration rate. When the habitat of a disaster or pestilence sudden emergencies, such as HIS will then mutation, break the dynamic balance, is the habitat of the low ihs added unpredictability, increases the search target solution chance 2.2 migration and mutation operation migration of species has its specific physical model, most often have a linear model, the quadratic model, cosine model, etc. Taking the linear model in FIG. 3 as an example, when the number of species in a certain habitat is 0, the immigration rate is the highest. At this moment λ = I, with the increasing number of species, the immigration rate decreases and the emigration rate increases due to the limitations of sunlight, water, food and other resources. When the number of habitat species is S0, the dynamic equilibrium is reached, and the emigration rate is the same as the emigration rate. When the habitat reaches saturation state, the number of species reaches the maximum Smax. At this moment, no species will move in, and the migration rate μ = E. Mutation operation is completed based on biogeography statistical formula:
Two, some source code
% Biogeography-Based Optimization (BBO) trainer for MLP %
% source codes version 1 %
% %
clc
clear all
% For modifying initial parameters please have a look at init.m file
display('... ')
display('BBO is training MLP ... ')
display('... ')
[cg_curve1,Hamming,best] = BBO(@MLP_Iris, 1.1.300); % BBO trainer
Best_W_B(1, :)=best;
display('... ')
display('PSO is training MLP ... ')
display('... ')
[cg_curve2,best]= PSO(@MLP_Iris, 1); % PSO trainer
Best_W_B(2, :)=best;
display('... ')
display('GA is training MLP ... ')
display('... ')
[cg_curve3,best]= GA(@MLP_Iris, 1); % GA trainer
Best_W_B(3, :)=best;
display('... ')
display('ACO is training MLP ... ')
display('... ')
[cg_curve4,best]= ACO(@MLP_Iris, 1); % ACO trainer
Best_W_B(4, :)=best;
display('... ')
display('ES is training MLP ... ')
display('... ')
[cg_curve5,best]= ES(@MLP_Iris, 1); % ES trainer
Best_W_B(5, :)=best;
display('... ')
display('PBIL is training MLP ... ')
display('... ')
[cg_curve6,best]=PBIL(@MLP_Iris, 1); % PBIL trainer
Best_W_B(6, :)=best;
% Calculating classification rates
load iris.txt
x=sortrows(iris,2);
H2=x(1:150.1);
H3=x(1:150.2);
H4=x(1:150.3);
H5=x(1:150.4);
T=x(1:150.5);
H2=H2';
[xf,PS] = mapminmax(H2); % Normalzation of input
I2(:,1)=xf;
H3=H3';
[xf,PS2] = mapminmax(H3); % Normalzation of input
I2(:,2)=xf;
H4=H4';
[xf,PS3] = mapminmax(H4); % Normalzation of input
I2(:,3)=xf;
H5=H5';
[xf,PS4] = mapminmax(H5); % Normalzation of input
I2(:,4)=xf;
Thelp=T;
T=T';
[yf,PS5]= mapminmax(T); % Normalzation of output
T=yf;
T=T';
for i=1:6
Rrate=0;
W=Best_W_B(i,1:63);
B=Best_W_B(i,64:75);
for pp=1:150
actualvalue=my_MLP(4.9.3,W,B,I2(pp,1),I2(pp,2), I2(pp,3),I2(pp,4));
if(T(pp)==- 1)
if (actualvalue(1) > =0.95 && actualvalue(2) <0.05 && actualvalue(3) <0.05)
Rrate=Rrate+1;
end
end
if(T(pp)==0)
if (actualvalue(1) <0.05 && actualvalue(2) > =0.95 && actualvalue(3) <0.05)
Rrate=Rrate+1;
end
end
if(T(pp)==1)
if (actualvalue(1) <0.05 && actualvalue(2) <0.05 && actualvalue(3) > =0.95)
Rrate=Rrate+1;
end
end
end
Final_Classification_Rates(1,i)=(Rrate/150) *100;
end
display('-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --')
display('Classification rate')
display(' BBO PSO GA ACO ES PBIL')
display(Final_Classification_Rates(1:6))
display('-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --')
figure('Position'[500 500 660 290])
%Draw convergence curves
subplot(1.2.1);
hold on
title('Convergence Curves')
semilogy(cg_curve1,'Color'.'r')
semilogy(cg_curve2,'Color'.'k')
semilogy(cg_curve3,'Color'.'b')
semilogy(cg_curve4,'Color'.'r')
semilogy(cg_curve5,'Color'.'g')
semilogy(cg_curve6,'Color'.'c')
xlabel('Generation');
ylabel('MSE');
axis tight
grid on
box on
legend('BBO'.'PSO'.'GA'.'ACO'.'ES'.'PBIL')
%Draw classification rates
subplot(1.2.2);
hold on
title('Classification Accuracies')
bar(Final_Classification_Rates)
xlabel('Algorithm');
ylabel('Classification rate (%)');
grid on
box on
set(gca,'XTickLabel', {'BBO'.'PSO'.'GA'.'ACO'.'ES'.'PBIL'});
Copy the code3. Operation results
Matlab version and references
1 matlab version 2014A
[1] Yang Baoyang, YU Jizhou, Yang Shan. Intelligent Optimization Algorithm and Its MATLAB Example (2nd Edition) [M]. Publishing House of Electronics Industry, 2016. [2] ZHANG Yan, WU Shuigen. MATLAB Optimization Algorithm source code [M]. Tsinghua University Press, 2017.