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

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.