An adaptive particle swarm optimization with information interaction mechanism

Abstract

This paper proposes an adaptive particle swarm optimization with information interaction mechanism (APSOIIM) to enhance the optimization ability of the PSO algorithm. Firstly, a chaotic sequence strategy is employed to generate uniformly distributed particles and to improve their convergence speed at the initialization stage of the algorithm. Then, an interaction information mechanism is introduced to boost the diversity of the population as the search process unfolds, which can effectively interact with the optimal information of neighboring particles to enhance the exploration and exploitation abilities. Therefore, the proposed algorithm may avoid premature and perform a more accurate local search. Besides, the convergence was proven to verify the robustness and efficiency of the proposed APSOIIM algorithm. Finally, the proposed APSOIIM was applied to solve the CEC2014 and CEC2017 benchmark functions as well as famous engineering optimization problems. The experimental results demonstrate that the proposed APSOIIM has significant advantages over the compared algorithms.