Abstract
All the population-based optimization algorithms are probabilistic algorithms and require only a few control parameters including number of candidates, number of iterations, elite size, etc. Besides these control parameters, algorithm-specific control parameters are required by different algorithms. Either the computational effort is increased or the local optimal solution is yielded as a result of the improper tuning of algorithm-specific parameters. Thus, the algorithm which works without any algorithm-specific parameters such as Jaya Algorithm (JA) is widespread among the optimization applications and researchers. JA can be easily implemented based on not having to tune any algorithm-specific parameters. In order to prove that JA could be applied to every problem arising in practice, this study presents a comprehensive review of the advances with JA and its variants. On the other hand, the performance of JA was evaluated to solve the Himmelblau function against five well-known optimization algorithms. Therefore, this study is expected to highlight the JA’s capabilities and performances especially for those researchers who are eager to explore the algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.