Parallel combination of Multi-Verse Optimizer and Particle Swarm Optimization for optimization problems

This study presents a novel optimization approach that combines the Multi-Verse Optimizer (MVO) and Particle Swarm Optimization (PSO) algorithms in a parallel framework. MVO's strength lies in its ability to explore a wide search space, while PSO excels at exploiting local optima. By running these algorithms concurrently and exchanging information periodically, the study aims to enhance both global exploration and local exploitation capabilities. To evaluate the performance of this hybrid approach, experiments on three benchmark functions (Sphere, Rastrigin, and Rosenbrock) were conducted. The results demonstrate that the parallel MVO-PSO consistently outperforms both MVO and PSO when used independently, particularly in problems with multiple local minima. This suggests that the proposed hybrid approach is a promising solution for tackling complex optimization challenges.