Advanced PSO Algorithms Development with Combined lbest and gbest Neighborhood Topologies

This paper introduces an innovative approach integrating global best (gbest) and local best (lbest) PSO communication topologies. The algorithm initiates with lbest and seamlessly transitions to gbest, with the switching rate controlled by the parameter "a". Rational values of "a"is determined through numerical experiments. A comparative methodology employing two estimation criteria is used to showcase the improved performance of the modified PSO-based algorithms. Furthermore, the efficacy of this approach is demonstrated in addressing two optimal control problems within dynamical systems. Results highlight the modified algorithms' superiority in terms of the total number of successful runs and statistical indicators. Consequently, these advanced algorithms prove effective for applications such as artificial neural network training, controller gains determination, and similar problem domains.