Discrete Dynamics in Nature and Society
Volume 2012 (2012), Article ID 638275, 24 pages
Research Article

An Algorithm for Global Optimization Inspired by Collective Animal Behavior

CUCEI Departamento de Electrónica, Universidad de Guadalajara, Avenida Revolución 1500, 44100 Guadalajara, JAL, Mexico

Received 21 September 2011; Revised 15 November 2011; Accepted 16 November 2011

Academic Editor: Carlo Piccardi

Copyright © 2012 Erik Cuevas 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.


A metaheuristic algorithm for global optimization called the collective animal behavior (CAB) is introduced. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central locations, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of animals which interact with each other based on the biological laws of collective motion. The proposed method has been compared to other well-known optimization algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.