# A heuristic optimization method inspired by wolf preying behavior

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## 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.

## References

- 1.Özcan E, Basaran C (2009) A Case Study of Memetic Algorithms for Constraint Optimization. Soft Comput Fusion Found Methodol Appl 13(8–9):871–882Google Scholar
- 2.Yang X-S (2009) Firefly algorithms for multimodal optimization. Stochastic algorithms: foundations and applications, SAGA 2009. Lecture notes in computer sciences, 5792. Springer, Heidelberg, pp 169–178Google Scholar
- 3.Yang X-S, Deb S (2009) Cuckoo search via Levy flights. In: World congress on nature and biologically inspired computing (NaBIC 2009). IEEE Publication, USA. 2009, pp 210–214Google Scholar
- 4.Yang X-S, Deb S, Fong S (2011) Accelerated particle swarm optimization and support vector machine for business optimization and applications, the third international conference on networked digital technologies (NDT 2011), Springer CCIS 136, Macau, 11–13 July 2011, pp 53–66Google Scholar
- 5.Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In: Gonzalez JR et al (eds) Nature inspired cooperative strategies for optimization (NISCO 2010), vol 284., Studies in computational intelligenceSpringer, Berlin, pp 65–74CrossRefGoogle Scholar
- 6.Peng Y (2011) An improved artificial fish swarm algorithm for optimal operation of cascade reservoirs. J Comput 6(4):740–746CrossRefGoogle Scholar
- 7.Törn A, Zilinskas A (1991) Global Optimization. Lect Notes Comput Sci Parallel Comput 17:619–632Google Scholar
- 8.Golfberg D (1975) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, ReadingGoogle Scholar
- 9.Kalender M, Kheiri A, Özcan E, Burke EK (2013) A greedy gradient-simulated annealing selection hyper-heuristic. Soft Comput 17(12):2279–2292. doi: 10.1007/s00500-013-1096-5 CrossRefGoogle Scholar
- 10.Dueck G, Scheuer T (1990) Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing. J Comput Phys 90(1):161–175 ElsevierMathSciNetCrossRefzbMATHGoogle Scholar
- 11.Glover F (1989) Tabu search—part 1. ORSA J Comput 1(2):190–206CrossRefzbMATHGoogle Scholar
- 12.Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B 26(1):29–41CrossRefGoogle Scholar