A Modified Bat Algorithm for Solving Large-Scale Bound Constrained Global Optimization Problems

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dc.authoridMashwani, Prof.Dr. Wali Khan/0000-0002-5081-741X
dc.authoridAbu Bakar, Maharani/0000-0001-9512-7191
dc.contributor.authorMashwani, Wali Khan
dc.contributor.authorMehmood, Ihsan
dc.contributor.authorAbu Bakar, Maharani
dc.contributor.authorKoccak, Ismail
dc.date.accessioned2025-01-21T16:35:00Z
dc.date.available2025-01-21T16:35:00Z
dc.date.issued2021
dc.departmentKırıkkale Üniversitesi
dc.description.abstractIn the last two decades, the field of global optimization has become very active, and, in this regard, many deterministic and stochastic algorithms were developed for solving various optimization problems. Among them, swarm intelligence (SI) is a stochastic algorithm that is more flexible and robust and has had the ability to find an optimum solution for high-dimensional optimization and search problems. SI-based algorithms are mainly inspired by the social behavior of fish schooling or bird flocking. Among the SI-based algorithms, Bat algorithm (BA) is one of the recently developed evolutionary algorithms. It employs an echolocation behavior of microbats by varying pulse rates of emission and loudness to perform their search process. In this paper, a modified Bat algorithm (MBA) is developed. The main focus of the MBA is to further enhance the exploration and exploitation search abilities of the original Bat algorithm. The performance of the modified Bat algorithm (MBA) is examined over the benchmark functions designed for evolutionary algorithms competition in the special session of 2005 IEEE Congress on Evolutionary Computation. The used benchmark functions include the unimodal, multimodal, and hybrid benchmark functions with high dimensionality. Furthermore, the impact analysis with respect to different values of temperatures is conducted by executing the proposed algorithm twenty-five times independently by using each benchmark function with different random seeds.
dc.description.sponsorshipResearch Intensified Grant Scheme (RIGS), Universiti Malaysia Terengganu [Fasa 1/2019, 55192/7]
dc.description.sponsorshipThe authors thank and acknowledge the support of the Research Intensified Grant Scheme (RIGS) Fasa 1/2019, Vot Number 55192/7, Universiti Malaysia Terengganu.
dc.identifier.doi10.1155/2021/6636918
dc.identifier.issn1024-123X
dc.identifier.issn1563-5147
dc.identifier.scopus2-s2.0-85104487878
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1155/2021/6636918
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24058
dc.identifier.volume2021
dc.identifier.wosWOS:000637403700001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherHindawi Ltd
dc.relation.ispartofMathematical Problems In Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.titleA Modified Bat Algorithm for Solving Large-Scale Bound Constrained Global Optimization Problems
dc.typeArticle

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