| Metaheuristic optimization algorithms are essential for tackling complex optimization tasks. The Jaya algorithm, a relatively recent method, has been widely used in the optimization of complex multimodal or composite landscapes. However, standard Jaya has low solution diversity and is susceptible to premature convergence. In this work, we propose four novel enhancements to the Jaya optimization algorithm, each addressing different aspects of the exploration-exploitation balance through the use of chaotic explorer solutions. The proposed approaches are evaluated on the IEEE Congress on Evolutionary Computation benchmark suite (CEC 2014), and the performance is compared against the standard Jaya and existing improved variants. Experimental results show the proposed methods consistently outperform the original Jaya across multiple benchmark problems, delivering significantly improved robustness, fine-tuning and solution quality in multimodal optimization. |
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