Theoretical Analysis of Biogeography Based Optimization

DOI

10.2991/mmebc-16.2016.404How to use a DOI?

Keywords

meta-heuristic; global optimization; NP hard problem

Abstract

Since the computation ability of computer improves dramatically, a lot of new meta-heuristic methods arise. All those algorithms are originated from some mechanisms in nature, and are similar in structure and widely used to solve global optimization problems. However, evolutionary algorithm, such as BBO, is lack of strict theory foundation and hard to be analyzed in theory, because it comes from heuristic idea and has complicated random behavior. Therefore, In this paper, we propose a Markov chain model of BBO to analyze the relationship between individual vector and , and prove that a Markov population series in BBO is an absorbing Markov chain. Convergence analysis of BBO is obtained, which is the Markov population series in BBO converge to objective subspace with probability one.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Aijun Zhu
AU  - Cong Hu
AU  - Chuanpei Xu
AU  - Zhi Li
PY  - 2016/06
DA  - 2016/06
TI  - Theoretical Analysis of Biogeography Based Optimization
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 2011
EP  - 2016
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.404
DO  - 10.2991/mmebc-16.2016.404
ID  - Zhu2016/06
ER  -