Adaptive Hybridization of Particle Swarm Optimization and Grey Wolf Optimization

An Adaptive Hybridization of Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) (AHPSOGWO), is presented. This hybridization leverages PSO's exploration ability and GWO's exploitation capacity through adaptive mechanisms to address limitations such as premature convergence and local optima stagnation. Even though the original hybrid approach gives better performance, it is appropriate only for problems with a continuous search space. However, issues such as premature convergence and the tendency to get trapped in local optima arise in some problems. Therefore, an Adaptive Hybridization of PSO and GWO is proposed and implemented. 7 standard benchmark functions were utilized to validate the algorithm’s efficacy, demonstrating superior stability, convergence speed, and potential to find the global optimum compared to standalone PSO and GWO.Pseudo-code, mathematical models, and experimental evaluations are discussed in detail.