Hybrid Topology-Based Particle Swarm Optimizer for Multi-source Location Problem in Swarm Robots

Jun Qi Zhang, Yehao Lu, Mengchu Zhou

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

4 Scopus citations

Abstract

A multi-source location problem aims to locate sources in an unknown environment based on the measurements of the signal strength from them. Vast majority of existing multi-source location methods require such prior environmental information as the signal range of sources and maximum signal strength to set some parameters. However, prior information is difficult to obtain in many practical tasks. To handle this issue, this work proposes a variant of Particle Swarm Optimizers (PSO), named as Hybrid Topology-based PSO (HT-PSO). It combines the advantages of multimodal search capability of a ring topology and rapid convergence of a star topology. HT-PSO does not require any prior knowledge of the environment, thus it has stronger robustness and adaptability. Experimental results show its superior performance over the state-of-the-art multi-source location method.

Original languageEnglish (US)
Title of host publicationAdvances in Swarm Intelligence - 13th International Conference, ICSI 2022, Proceedings, Part II
EditorsYing Tan, Yuhui Shi, Ben Niu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages17-24
Number of pages8
ISBN (Print)9783031097256
DOIs
StatePublished - 2022
Event13th International Conference on Swarm Intelligence, ICSI 2022 - Xi'an, China
Duration: Jul 15 2022Jul 19 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13345 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Swarm Intelligence, ICSI 2022
Country/TerritoryChina
CityXi'an
Period7/15/227/19/22

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

Keywords

  • Multi-source location problem
  • Particle Swarm Optimizer
  • Swarm robots

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