Particle Swarm Optimizer Without Communications Among Particles
Document Type
Conference Proceeding
Publication Date
1-1-2023
Abstract
Particle Swarm Optimizer (PSO) is a kind of population-based evolutionary optimizer. Many PSO variants have been proposed and most of them require mutual communications among particles for their fitness values to find the best position, hence leading to their effective collaboration. However, some real scenes using swarm robots to perform PSO cannot provide reliable communications during their distributed search. To handle such issues, this work proposes a novel PSO variant without communications among particles, called Communication-free Particle Swarm Optimizer (CfPSO). It employs particles’ detection ability instead of direct communications among them to accomplish the needed collaboration. Experimental results show that it obtains higher accurate performance than the standard PSO equipped with full communication ability, which is against human intuition.
Identifier
85169001307 (Scopus)
ISBN
[9783031366215]
Publication Title
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
External Full Text Location
https://doi.org/10.1007/978-3-031-36622-2_13
e-ISSN
16113349
ISSN
03029743
First Page
158
Last Page
167
Volume
13968 LNCS
Grant
2021-cyxt2-kj10
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Zhang, Jun Qi; Huang, Xu Rui; Liu, Huan; and Zhou, Meng Chu, "Particle Swarm Optimizer Without Communications Among Particles" (2023). Faculty Publications. 2323.
https://digitalcommons.njit.edu/fac_pubs/2323
