An Improved Comprehensive Learning Jaya Algorithm with Lévy Flight for Engineering Design Optimization Problems

doi:10.3390/electronics14193776

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An Improved Comprehensive Learning Jaya Algorithm with Lévy Flight for Engineering Design Optimization Problems

2025-10-02

Xintong Shen

Xiaonan Luo *

DOI：10.3390/electronics14193776

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Abstract

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The JAYA algorithm has been widely applied due to its simplicity and efficiency but is prone to entrapment in sub-optimal solutions. This study introduces the Lévy flight mechanism and proposes the CLJAYA-LF algorithm, which integrates large-step and small-step Lévy movements with a multi-strategy particle update mechanism. The large-step strategy enhances global exploration and helps escape local optima, while the small-step strategy improves fine-grained local search accuracy. Extensive experiments on the CEC2017 benchmark suite and real-world engineering optimization problems demonstrate the effectiveness of CLJAYA-LF. In 50-dimensional benchmark problems, it outperforms JAYA, JAYALF, and CLJAYA in 15 of 22 functions with lower mean fitness and competitive variance; in 100-dimensional problems, it achieves smaller variance in 17 of 24 functions. For engineering applications, CLJAYA-LF attains a mean of 16.9 and variance of 0.332 for the Step-cone Pulley, 1.44 × 10−15 and 3.14 × 10−15 for the Gear Train, and 0.535 and 0.0498 for the Planetary Gear Train, surpassing most JAYA variants. These results indicate that CLJAYA-LF delivers superior optimization performance while maintaining robust stability across dimensions and problem types, demonstrating significant potential for complex and high-dimensional optimization scenarios.

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