Vol.:(0123456789) 1 3 Archives of Computational Methods in Engineering https://doi.org/10.1007/s11831-021-09585-8 REVIEW ARTICLE An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications Raed Abu Zitar 1  · Mohammed Azmi Al‑Betar 2,3  · Mohammed A. Awadallah 2,4  · Iyad Abu Doush 5,6  · Khaled Assaleh 2 Received: 14 October 2020 / Accepted: 5 April 2021 © CIMNE, Barcelona, Spain 2021 Abstract In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. The JAYA algorithm combines the survival of the fttest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristics of JAYA algo- rithm are carefully analyzed. Thereafter, the proposed versions of JAYA algorithm have been surveyed such as modifed, binary, hybridized, parallel, chaotic, multi-objective and others. The various applications tackled using relevant versions of JAYA algorithm are also discussed and summarized based on several problem domains. Furthermore, the open sources code of JAYA algorithm are identifed to provide enrich resources for JAYA research communities. The critical analysis of JAYA algorithm reveals its advantages and limitations in dealing with optimization problems. Finally, the paper ends up with conclusion and possible future enhancements suggested to improve the performance of JAYA algorithm. The reader of this overview will determine the best domains and applications used by JAYA algorithm and can justify their JAYA-related contributions. Keywords JAYA Algorithm · Metaheuristics · Optimization · Exploration · Exploitation 1 Introduction In the Artifcial Intelligence, the search for the optimal solution in the search space is one of the common issues in almost all sub-felds such as machine learning, deep learn- ing, knowledge representation, reasoning, and perception. The traditional search techniques depend on the heuristic concepts which are problem-dependent trying to search for approximate solutions often with less focus on the quality [1]. Recently, Metaheuristic-based algorithms attract the attentions of artifcial intelligence research communities due to its impressive capability in manipulating the search space of optimization problems [2]. A Metaheuristic-based algorithm is a general optimization framework that can be adapted for many optimization problems. It has an iterative evolution strategy that utilizes learning mechanisms exploit- ing accumulative knowledge as well as exploring problem search space. It is usually controlled by specifc parameters used to fnd good enough approximation solution for opti- mization problem in hand [3]. Metaheuristic-based algorithms are conventionally cat- egorized in several ways [4]: Nature-inspired vs. non-nature inspired, population-based vs. single point search, dynamic * Mohammed Azmi Al-Betar m.albetar@ajman.ac.ae Raed Abu Zitar raed.zitar@sorbonne.ae Mohammed A. Awadallah ma.awadallah@alaqsa.edu.ps Iyad Abu Doush idoush@auk.edu.kw Khaled Assaleh k.assaleh@ajman.ac.ae 1 Sorbonne University Center of Artifcial Intelligence, Sorbonne University-Abu Dhabi, Abu Dhabi, UAE 2 Artifcial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE 3 Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan 4 Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine 5 Computing Department, American University of Kuwait, Salmiya, Kuwait 6 Computer Science Department, Yarmouk University, Irbid, Jordan