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AuthorMashwani, W.K.
AuthorHamdi, A.
AuthorAsif Jan, M.
AuthorGoktas, A.
AuthorKhan, F.
Available date2023-09-24T07:55:32Z
Publication Date2020
Publication NameJournal of Intelligent and Fuzzy Systems
ResourceScopus
URIhttp://dx.doi.org/10.3233/JIFS-192162
URIhttp://hdl.handle.net/10576/47876
AbstractThere are numerous large-scale global optimization problems encountered in real-world applications including engineering, manufacturing, economics, networking fields. Over the last two decades different varieties of swarm intelligence and nature inspired based evolutionary algorithms (EAs) were developed and still. Among them, particles swarm optimization, Firefly algorithm, Ant colony optimization, Bat algorithm are the most popular and recently developed leading swarm intelligence based approaches. They are mainly inspired by the social and cooperative behaviors of swarm likewise herds of animals, flocking of birds, schooling of fish, ant colonies, herds of bisons and packs of wolves working together for their common benefit. Due to easy implementation and high capability in achieving of absolute optimum, swarm intelligence based algorithms have attained a great deal attention in both academic and industrial applications. This paper proposes a hybrid swarm intelligence (HSI) algorithm that employs the Bat Algorithm (BA) and the Practical Swarm Optimization (PSO) as constituents to perform their search process for dealing with recently designed benchmark functions in the special session of the 2017 IEEE congress of evolutionary computation (CEC'17) [3]. The approximate solutions for most of the CEC'17 benchmark functions obtained by the suggested algorithm in its twenty five independent runs of trails are much promising as compared to its competitors. 2020-IOS Press and the authors. All rights reserved.
Languageen
PublisherIOS Press
Subjectevolutionary algorithms (EAs)
evolutionary computing (EC)
Global optimization
optimization problems
soft computing
swarm intelligence based approaches and hybrid swarm intelligence algorithm
TitleLarge-scale global optimization based on hybrid swarm intelligence algorithm
TypeArticle
Pagination1257-1275
Issue Number1
Volume Number39
dc.accessType Abstract Only


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