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Recent advances of bat-inspired algorithm, its versions and applications.
Alyasseri, Zaid Abdi Alkareem; Alomari, Osama Ahmad; Al-Betar, Mohammed Azmi; Makhadmeh, Sharif Naser; Doush, Iyad Abu; Awadallah, Mohammed A; Abasi, Ammar Kamal; Elnagar, Ashraf.
Afiliación
  • Alyasseri ZAA; ECE Department, Faculty of Engineering, University of Kufa, P.O. Box 21, Najaf, Iraq.
  • Alomari OA; College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq.
  • Al-Betar MA; Information Research and Development Center (ITRDC), University of Kufa, Najaf, Iraq.
  • Makhadmeh SN; MLALP Research Group, University of Sharjah, Sharjah, United Arab Emirates.
  • Doush IA; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates.
  • Awadallah MA; Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan.
  • Abasi AK; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates.
  • Elnagar A; Department of Computing, College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait.
Neural Comput Appl ; 34(19): 16387-16422, 2022.
Article en En | MEDLINE | ID: mdl-35971379
ABSTRACT
Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Neural Comput Appl Año: 2022 Tipo del documento: Article País de afiliación: Irak

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Neural Comput Appl Año: 2022 Tipo del documento: Article País de afiliación: Irak