Recent advances of bat-inspired algorithm, its versions and applications

被引:0
|
作者
Zaid Abdi Alkareem Alyasseri
Osama Ahmad Alomari
Mohammed Azmi Al-Betar
Sharif Naser Makhadmeh
Iyad Abu Doush
Mohammed A. Awadallah
Ammar Kamal Abasi
Ashraf Elnagar
机构
[1] University of Kufa,ECE Department, Faculty of Engineering
[2] University of Warith Al-Anbiyaa,College of Engineering
[3] University of Kufa,Information Research and Development Center (ITRDC)
[4] University of Sharjah,MLALP Research Group
[5] Ajman University,Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology
[6] Al-Balqa Applied University,Department of Information Technology, Al
[7] American University of Kuwait,Huson University College
[8] Yarmouk University,Department of Computing, College of Engineering and Applied Sciences
[9] Al-Aqsa University,Computer Science Department
[10] Ajman University,Department of Computer Science
[11] Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI),Artificial Intelligence Research Center (AIRC)
[12] University of Sharjah,Machine Learning Department
来源
关键词
Bat-inspired algorithm; Metaheuristics; Optimization; Swarm intelligence;
D O I
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学科分类号
摘要
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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页码:16387 / 16422
页数:35
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