A particle swarm optimization algorithm based on diversity-driven fusion of opposing phase selection strategies

Abstract Opposition-based learning (OBL) is often embedded in intelligent optimization algorithms to solve practical engineering and mathematical problems, but the combinatorial problems among different OBL variants are rarely studied. To this end, we propose a novel variant based on principle of optical imaging, which combines two types quasi-opposite extended opposite learning, called diversity-driven fused opposition (SQOBL). First, density center neighborhood model proposed. Based rapid convergence centroid, combined advantages centroid construct double mean (DMC) replace original point refraction. Secondly, an method refraction imaging Diversity then exploited drive opposing strategies at stages evolution, thus controlling exploration utilization algorithm. Finally, SQOBL was PSO with eight others representative find most optimal solution for test suite. In addition, 8 first three were selected evaluate performance latest CEC2022 benchmark set realistic constrained problems. Experiments 56 functions 3 real-world constraint show that proposed has good integrative properties CEC2015, CEC2017, CEC2020, suites.