Analysis of Biogeography-Based Optimization
Simon, Mehmet Ergezer, and Dawei Du
Biogeography is the study of the geographical distribution of biological
organisms. The mindset of the engineer is that we can learn from nature. This
motivates the application of biogeography to optimization problems. An
introduction to biogeography-based optimization (BBO) can be found at http://academic.csuohio.edu/simond/bbo.
The references below discusses how Markov analysis can be used to analytically
obtain the probability of each possible population in a BBO problem.
The software that was used to create the results in  can be
downloaded in a zip file. The software is written in m-files that can be run in
the MATLAB environment. The m-files are
contained in bbomarkov.zip. If you
download the zip file to your hard drive and then unzip the file, you can
reproduce the results in . Maybe you can even modify the m-files for
your own research. When you unzip the file on your hard drive, look at readme.txt for more detailed
Simon, M. Ergezer, D. Du, and R. Rarick, Markov
models for biogeography-based optimization, IEEE Transactions on
Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 41, no. 1, pp.
299-306, January 2011 – This paper is probably the most complete
description that we have of BBO Markov modeling
Simon, M. Ergezer, and D. Du, Population
distributions in biogeography-based optimization algorithms with elitism,
IEEE Conference on Systems, Man, and Cybernetics, San Antonio, Texas, pp.
1017-1022, October 2009 – This is a preliminary version of the above
paper, but this paper also shows how to include elitism in the Markov
Simon, R. Rarick, M. Ergezer, and D. Du, Analytical
and numerical comparisons of biogeography-based optimization and genetic
algorithms, Information Sciences, vol. 181, no. 7, pp. 1224-1248,
April 2011 - This paper uses Markov
models to obatin analytical comparisons of BBO and genetic algorithms
Dimension of an Evolutionary Algorithm Transition Matrix, June 2009 - This unpublished note shows
some different ways to calculate the dimension of the Markov transition
matrix. It applies to Markov modeling for any evolutionary algorithm, not
just for BBO.
Professor Simon’s Home Page
Department of Electrical
and Computer Engineering
Cleveland State University
Last Revised: September 14, 2011