YEN-CHING CHANG2026-10-162026-10-162013-01-01Pure ID: 15467253Pure UUID: ebb68040-d1e6-4031-886d-956d444e83cd10.1109/IMSNA.2013.6743474https://www.scopus.com/pages/publications/84898473338https://scholars.csmu.edu.tw/handle/123456789/22951Recently, a study of particle swarm optimization (PSO) with considering more local best particles has been proposed to improve the performance of optimization. Better performance of considering some local best particles shows that the proposed two types of variants of PSO have potential advantages over the standard PSO. The basic logic is to exploit all existing resources as fully as possible. Taking the same line, we further study how other local best particles work on bare bones PSO (BBPSO) in this paper. Experimental results show that the adopted idea does effectively raise the overall performance of optimization in most cases.4enjournal article