Current-to-Best Crossover for Modified Jaya Algorithm

The Jaya algorithm is a recently designed metaheuristics and has been verified for its good performance in solving various engineering and scientific optimization applications. In this paper, an enhanced variant named ‘MCrJaya’ has been developed by incorporating the DE/current-to-best approach-based crossover operation at the creation phase and the DE/αbest/1 mutation operation at the selection phase. The newly designed crossover operation manages the population diversity, while the second approach helps to amplify the convergence rapidity of the algorithm. The proposed MCrJaya is verified on 54 benchmark problems from different test suits and three real-life applications of IEEE CEC 2011. The performance was evaluated using three metrics: mean, standard deviation, and rank. Additionally, statistical analysis was performed using the Wilcoxon rank-sum test to assess the significance of the results, further validating the robustness of the proposed approach. The experimental results confirm the superiority of the MCrJaya over enhanced variants of Jaya, DE, and other metaheuristics algorithms.