Jaya Algorithm and Applications: A Comprehensive Review

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.