caa a22 4500 60615938X CHVBK 20210128100624.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00521-015-1836-9 doi (NATIONALLICENCE)springer-10.1007/s00521-015-1836-9 A heuristic optimization method inspired by wolf preying behavior [Elektronische Daten] [Simon Fong, Suash Deb, Xin-She Yang] 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. The Natural Computing Applications Forum, 2015 Metaheuristic nationallicence Bio-inspired optimization nationallicence Wolf Search Algorithm nationallicence Fong Simon Department of Computer and Information Science, University of Macau, Taipa, Macau SAR aut Deb Suash Department of Computer Science and Engineering, Cambridge Institute of Technology, Ranchi, India aut Yang Xin-She School of Design engineering and Mathematics, Middlesex University, London, UK aut Neural Computing and Applications Springer London 26/7(2015-10-01), 1725-1738 0941-0643 26:7<1725 2015 26 521 https://doi.org/10.1007/s00521-015-1836-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1836-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fong Simon Department of Computer and Information Science, University of Macau, Taipa, Macau SAR aut NATIONALLICENCE 700 1- Deb Suash Department of Computer Science and Engineering, Cambridge Institute of Technology, Ranchi, India aut NATIONALLICENCE 700 1- Yang Xin-She School of Design engineering and Mathematics, Middlesex University, London, UK aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/7(2015-10-01), 1725-1738 0941-0643 26:7<1725 2015 26 521