A Novel Animal Migration Algorithm for Global Numerical Optimization

Qifang Luo1, Mingzhi Ma2 and Yongquan Zhou1, 2

  1. College of Information Science and Engineering, Guangxi University for Nationalities
    Nanning 530006, China
    yongquanzhou@126.com, l.qf@163.com
  2. Guangxi High School Key Laboratory of Complex System and Computational Intelligence
    Nanning 530006, China
    mamingzhi666@163.com

Abstract

Animal migration optimization (AMO) searches optimization solutions by migration process and updating process. In this paper, a novel migration process has been proposed to improve the exploration and exploitation ability of the animal migration optimization. Twenty-three typical benchmark test functions are applied to verify the effects of these improvements. The results show that the improved algorithm has faster convergence speed and higher convergence precision than the original animal migration optimization and other some intelligent optimization algorithms such as particle swarm optimization (PSO), cuckoo search (CS), firefly algorithm (FA), bat-inspired algorithm (BA) and artificial bee colony (ABC).

Key words

animal migration optimization algorithms, exploration and exploitation, functions optimization

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS141229041L

Publication information

Volume 13, Issue 1 (January 2016)
Year of Publication: 2016
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Luo, Q., Ma, M., Zhou, Y.: A Novel Animal Migration Algorithm for Global Numerical Optimization. Computer Science and Information Systems, Vol. 13, No. 1, 259–285. (2016)