Hybrid self-organizing migrating algorithm based on estimation of distribution

DOI

10.2991/meic-14.2014.56How to use a DOI?

Keywords

self-organizing migrating algorithm;estimation of distribution algorithm; premature convergence; population diversity; function optimization

Abstract

A new hybrid self-organizing migrating algorithm based on estimation of distribution (HSOMA) is proposed to resolve the defect of premature convergence in the self-organizing migrating algorithm (SOMA) and improve the search ability of SOMA. In order to make full use of the statistical information on population and increase the diversity of migration behavior, HSOMA introduces the thought of estimation of distribution algorithm (EDA) into SOMA and reproduces the genes of new individuals by both SOMA and EDA. The proportion of the use of two algorithms is decided by a control parameter. In this way, HSOMA can increase the population diversity and improve the convergence speed. HSOMA is tested on several complex benchmark functions taken from literature and its efficiency is compared with SOMA, the continuous domain Population-Based Incremental Learning algorithm(PBILc) and hybrid migrating behavior based self-organizing migrating algorithm(HBSOMA). On the basis of comparison it is concluded that HSOMA shows better global search ability and convergence accuracy.

Copyright

© 2014, 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  - Zhiyi Lin
AU  - Li juan Wang
PY  - 2014/11
DA  - 2014/11
TI  - Hybrid self-organizing migrating algorithm based on estimation of distribution
BT  - Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering
PB  - Atlantis Press
SP  - 250
EP  - 254
SN  - 2352-5401
UR  - https://doi.org/10.2991/meic-14.2014.56
DO  - 10.2991/meic-14.2014.56
ID  - Lin2014/11
ER  -