TY - GEN
T1 - Hybrid Topology-Based Particle Swarm Optimizer for Multi-source Location Problem in Swarm Robots
AU - Zhang, Jun Qi
AU - Lu, Yehao
AU - Zhou, Mengchu
N1 - Funding Information:
Acknowledgements. This work was supported by Innovation Program of Shanghai Municipal Education Commission (202101070007E00098) and Shanghai Industrial Collaborative Science and Technology Innovation Project (2021-cyxt2-kj10). This work was also supported in part by the National Natural Science Foundation of China (51775385, 61703279, 62073244, 61876218) and the Shanghai Innovation Action Plan under grant no. 20511100500.
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Multi-source location problem
KW - Particle Swarm Optimizer
KW - Swarm robots
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U2 - 10.1007/978-3-031-09726-3_2
DO - 10.1007/978-3-031-09726-3_2
M3 - Conference contribution
AN - SCOPUS:85134656828
SN - 9783031097256
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 17
EP - 24
BT - Advances in Swarm Intelligence - 13th International Conference, ICSI 2022, Proceedings, Part II
A2 - Tan, Ying
A2 - Shi, Yuhui
A2 - Niu, Ben
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Swarm Intelligence, ICSI 2022
Y2 - 15 July 2022 through 19 July 2022
ER -