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受蝙蝠启发算法的最新进展、其版本及应用

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

作者信息

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

机构信息

ECE Department, Faculty of Engineering, University of Kufa, P.O. Box 21, Najaf, Iraq.

College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq.

出版信息

Neural Comput Appl. 2022;34(19):16387-16422. doi: 10.1007/s00521-022-07662-y. Epub 2022 Aug 11.

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.

摘要

蝙蝠启发算法(BA)是一种强大的群体智能算法,在许多问题领域都取得了成功。蝙蝠动物的生态系统启发了BA的主要思想。这篇综述论文扫描并分析了2017年至2021年使用BA进行的前沿研究。BA具有非常令人印象深刻的特点,如易于使用、概念简单、灵活且适应性强、一致、合理且完整。它具有强大的算子,通过在局部最优解吸引的强化步骤中遵循适者生存规则来纳入自然选择原则。首先,根据每年发表的基于BA的期刊文章数量、引用次数、顶级作者、与BA相关的工作、顶级机构和顶级国家,总结了Scopus索引文章中近期扎实研究的增长情况。之后,强调了不同版本的BA,以适应诸如二进制、改进、杂交和多目标BA等优化问题的复杂性。综述并总结了BA的成功应用,如电力系统、无线和网络系统、环境与材料工程、分类与聚类、结构与机械工程、特征选择、图像与信号处理、机器人技术、医疗与保健、调度领域等。还提到了对BA局限性和缺点的批判性分析。给出了BA代码的开源代码,以构建丰富的BA综述。最后,对BA综述进行了总结,并提出了未来可能的发展方向,例如利用BA服务于动态、强大、多目标、大规模优化,以及通过利用结构化种群、调整参数、混合策略和选择机制来提高BA性能。这篇综述的读者将确定BA使用的最佳领域和应用,并能证明他们与BA相关的贡献。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1139/9366842/142172668d87/521_2022_7662_Fig1_HTML.jpg

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