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Mathematical Problems in Engineering
Volume 2014, Article ID 465082, 17 pages
Research Article

Wolf Pack Algorithm for Unconstrained Global Optimization

1Materiel Management and Safety Engineering Institute, Air Force Engineering University, Xi’an 710051, China
2Materiel Engineering Institute, Armed Police Force Engineering University, Xi’an 710086, China

Received 28 June 2013; Revised 13 January 2014; Accepted 27 January 2014; Published 9 March 2014

Academic Editor: Orwa Jaber Housheya

Copyright © 2014 Hu-Sheng Wu and Feng-Ming Zhang. 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.


The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstracted three intelligent behaviors, scouting, calling, and besieging, and two intelligent rules, winner-take-all generation rule of lead wolf and stronger-survive renewing rule of wolf pack. Then we proposed a new heuristic swarm intelligent method, named wolf pack algorithm (WPA). Experiments are conducted on a suit of benchmark functions with different characteristics, unimodal/multimodal, separable/nonseparable, and the impact of several distance measurements and parameters on WPA is discussed. What is more, the compared simulation experiments with other five typical intelligent algorithms, genetic algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, and firefly algorithm, show that WPA has better convergence and robustness, especially for high-dimensional functions.