@inproceedings{c49f0581ac19429b9a97b3419e14cfd5,
title = "Hybrid Topology-Based Particle Swarm Optimizer for Multi-source Location Problem in Swarm Robots",
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.",
keywords = "Multi-source location problem, Particle Swarm Optimizer, Swarm robots",
author = "Zhang, {Jun Qi} and Yehao Lu and Mengchu Zhou",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 13th International Conference on Swarm Intelligence, ICSI 2022 ; Conference date: 15-07-2022 Through 19-07-2022",
year = "2022",
doi = "10.1007/978-3-031-09726-3_2",
language = "English (US)",
isbn = "9783031097256",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "17--24",
editor = "Ying Tan and Yuhui Shi and Ben Niu",
booktitle = "Advances in Swarm Intelligence - 13th International Conference, ICSI 2022, Proceedings, Part II",
address = "Germany",
}