Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013, Article ID 308250, 7 pages
http://dx.doi.org/10.1155/2013/308250
Research Article

Opposition-Based Animal Migration Optimization

School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China

Received 25 July 2013; Accepted 8 September 2013

Academic Editor: William Guo

Copyright © 2013 Yi Cao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

AMO is a simple and efficient optimization algorithm which is inspired by animal migration behavior. However, as most optimization algorithms, it suffers from premature convergence and often falls into local optima. This paper presents an opposition-based AMO algorithm. It employs opposition-based learning for population initialization and evolution to enlarge the search space, accelerate convergence rate, and improve search ability. A set of well-known benchmark functions is employed for experimental verification, and the results show clearly that opposition-based learning can improve the performance of AMO.