A Novel Comprehensive Learning JAYA Algorithm Based on Lévy Flights

To address the issue of the JAYA algorithm becoming stuck in suboptimal solutions, this paper introduces the Lévy flight method and proposes a novel CLJAYA-LF algorithm. This new approach integrates the Lévy flight’s "big step" movement strategy with multi-strategy particle updates, which helps particles escape local extrema and restart in new search spaces, thus significantly enhancing exploration capabilities. Additionally, the algorithm utilizes the Lévy flight’s "small step" random movement strategy to improve local exploitation and address unidirectional particle progress at specific positions. The CLJAYA-LF algorithm’s combination of large and small step movements provides a balanced mechanism for overcoming local optima and refining search efficiency. Experimental results confirm that CLJAYA-LF outperforms existing advanced methods, demonstrating exceptional performance across both low-dimensional and high-dimensional optimization tasks and showing promise for broader applications.