Hybrid Draco Lizard Optimizer with PSO Integration: Enhancing Global Optimization through Neighborhood-Based Steering

Global optimization plays a crucial role in solving complex real world problems characterized by nonlinearity, high dimensionality, and multimodal landscapes. However, many existing metaheuristic algorithms struggle with slow exploration during the early search stages. To address these limitations, this study introduces the Hybrid Draco Lizard Optimizer with PSO Integration (DLO-PSO) a bio-inspired metaheuristic that combines the adaptive exploration behavior of the Draco Lizard Optimizer with the directional learning capability of Particle Swarm Optimization. The hybrid framework enhances information exchange among agents and improves balance between global exploration and local exploitation. The proposed DLO- PSO achieves faster and more stable convergence across diverse optimization problems. Extensive experiments on the CEC۲۰۱۷ benchmark suite demonstrate that DLO-PSO achieves significantly faster convergence, higher solution accuracy, and reduced computational cost compared with recent state-of-the-art algorithms. Statistical and sensitivity analyses further confirm the algorithm's robustness and scalability, highlighting its potential as an efficient framework for complex global optimization tasks.

کلیدواژه ها:

Draco Lizard Optimizer ، Particle Swarm Optimization ، Hybrid Metaheuristic ، algorithms ، Neighborhood