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.

Enhancing physical layer security through the deployment of intelligent reflecting surfaces (IRS) is an emerging and significant research direction in 6G wireless networks. Existing methods mainly focus on optimizing the transmit beamforming of the access point (AP) and the phase shift of the IRS to maximize the secrecy rate, while neglecting the optimization of the IRS amplitude. We propose a joint op-timization framework that simultaneously optimizes the IRS amplitude, IRS phase shifts, and AP beamforming to enhance the secrecy rate. Due to the problem’s non-convexity, exist-ing alternating optimization methods face high challenges, particularly for large-scale IRS systems and high-power sce-narios. To tackle these issues, we propose a competition mechanism-based multi-strategy swarm optimization algo-rithm, which aims to better balance global exploration and local exploitation capabilities, thereby achieving more opti-mal IRS configurations. Simulations demonstrate that jointly optimizing IRS amplitude, phase, and AP beamforming sig-nificantly improves secrecy rate over phase-only beamforming methods.