Particle Swarm Optimizer Without Communications Among Particles

Jun Qi Zhang, Xu Rui Huang, Huan Liu, Meng Chu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationAdvances in Swarm Intelligence - 14th International Conference, ICSI 2023, Proceedings
EditorsYing Tan, Yuhui Shi, Wenjian Luo
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783031366215
StatePublished - 2023
Event14th International Conference on Advances in Swarm Intelligence, ICSI 2023 - Shenzhen, China
Duration: Jul 14 2023Jul 18 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13968 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th International Conference on Advances in Swarm Intelligence, ICSI 2023

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Communication
  • Communication-free
  • Particle Swarm Optimizer


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