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Neural Computing and Applications

, Volume 26, Issue 7, pp 1725–1738 | Cite as

A heuristic optimization method inspired by wolf preying behavior

  • Simon FongEmail author
  • Suash Deb
  • Xin-She Yang
Original Article

Abstract

Optimization problems can become intractable when the search space undergoes tremendous growth. Heuristic optimization methods have therefore been created that can search the very large spaces of candidate solutions. These methods, also called metaheuristics, are the general skeletons of algorithms that can be modified and extended to suit a wide range of optimization problems. Various researchers have invented a collection of metaheuristics inspired by the movements of animals and insects (e.g., firefly, cuckoos, bats and accelerated PSO) with the advantages of efficient computation and easy implementation. This paper studies a relatively new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA) that imitates the way wolves search for food and survive by avoiding their enemies. The WSA is tested quantitatively with different values of parameters and compared to other metaheuristic algorithms under a range of popular non-convex functions used as performance test problems for optimization algorithms, with superior results observed in most tests.

Keywords

Metaheuristic Bio-inspired optimization Wolf Search Algorithm 

Notes

Acknowledgments

The authors are thankful for the financial support from the research grant “Adaptive OVFDT with Incremental Pruning and ROC Corrective Learning for Data Stream Mining,” Grant no. MYRG073(Y3-L2)-FST12-FCC, offered by the University of Macau, FST and RDAO.

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Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  1. 1.Department of Computer and Information ScienceUniversity of MacauTaipaMacau SAR
  2. 2.Department of Computer Science and EngineeringCambridge Institute of TechnologyRanchiIndia
  3. 3.School of Design engineering and MathematicsMiddlesex UniversityLondonUK

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