Improved Mixed Discrete Particle Swarms based Multi-Task Assignment for UAVs

Zhenshuai Jia; Bing Xiao; Hanyu Qian

doi:10.1109/DDCLS58216.2023.10166130

Improved Mixed Discrete Particle Swarms based Multi-Task Assignment for UAVs

Zhenshuai Jia, Bing Xiao, Hanyu Qian

School of Automation

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Aiming at the problem of multi-Task distribution for Unmanned Aerial Vehicle (UAV) swarm, a new type of multi-Task distribution model for UAVs is established in this paper, various constraints are considered, such as bomb load and damage loss. To solve the multi-Task allocation problem, an improved mixed discrete particle swarm optimization algorithm (IM-DPSO) is proposed, a two-dimensional particle coding matrix with task priority is designed, the genetic variation rules with particle update strategies are combined, and then the inertia weight and learning factors are optimized to enhance the algorithm's ability of solving the problem. Simulation results show that the improved algorithm can better solve the problem of multi-Task allocation for UAVs under the distribution model.

Original language	English
Title of host publication	Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	442-448
Number of pages	7
ISBN (Electronic)	9798350321050
DOIs	https://doi.org/10.1109/DDCLS58216.2023.10166130
State	Published - 2023
Event	12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023 - Xiangtan, China Duration: 12 May 2023 → 14 May 2023

Publication series

Name	Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023

Conference

Conference	12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023
Country/Territory	China
City	Xiangtan
Period	12/05/23 → 14/05/23

Keywords

Discrete Particle Swarm Optimization (DPSO)
Multi-Task Assignment
Unmanned Aerial Vehicle

Access to Document

10.1109/DDCLS58216.2023.10166130

Cite this

Jia, Z., Xiao, B., & Qian, H. (2023). Improved Mixed Discrete Particle Swarms based Multi-Task Assignment for UAVs. In Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023 (pp. 442-448). (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DDCLS58216.2023.10166130

Jia, Zhenshuai ; Xiao, Bing ; Qian, Hanyu. / Improved Mixed Discrete Particle Swarms based Multi-Task Assignment for UAVs. Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 442-448 (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023).

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abstract = "Aiming at the problem of multi-Task distribution for Unmanned Aerial Vehicle (UAV) swarm, a new type of multi-Task distribution model for UAVs is established in this paper, various constraints are considered, such as bomb load and damage loss. To solve the multi-Task allocation problem, an improved mixed discrete particle swarm optimization algorithm (IM-DPSO) is proposed, a two-dimensional particle coding matrix with task priority is designed, the genetic variation rules with particle update strategies are combined, and then the inertia weight and learning factors are optimized to enhance the algorithm's ability of solving the problem. Simulation results show that the improved algorithm can better solve the problem of multi-Task allocation for UAVs under the distribution model.",

keywords = "Discrete Particle Swarm Optimization (DPSO), Multi-Task Assignment, Unmanned Aerial Vehicle",

author = "Zhenshuai Jia and Bing Xiao and Hanyu Qian",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023 ; Conference date: 12-05-2023 Through 14-05-2023",

year = "2023",

doi = "10.1109/DDCLS58216.2023.10166130",

language = "英语",

series = "Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023",

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Jia, Z, Xiao, B & Qian, H 2023, Improved Mixed Discrete Particle Swarms based Multi-Task Assignment for UAVs. in Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023. Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023, Institute of Electrical and Electronics Engineers Inc., pp. 442-448, 12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023, Xiangtan, China, 12/05/23. https://doi.org/10.1109/DDCLS58216.2023.10166130

Improved Mixed Discrete Particle Swarms based Multi-Task Assignment for UAVs. / Jia, Zhenshuai; Xiao, Bing; Qian, Hanyu.
Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 442-448 (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Aiming at the problem of multi-Task distribution for Unmanned Aerial Vehicle (UAV) swarm, a new type of multi-Task distribution model for UAVs is established in this paper, various constraints are considered, such as bomb load and damage loss. To solve the multi-Task allocation problem, an improved mixed discrete particle swarm optimization algorithm (IM-DPSO) is proposed, a two-dimensional particle coding matrix with task priority is designed, the genetic variation rules with particle update strategies are combined, and then the inertia weight and learning factors are optimized to enhance the algorithm's ability of solving the problem. Simulation results show that the improved algorithm can better solve the problem of multi-Task allocation for UAVs under the distribution model.

AB - Aiming at the problem of multi-Task distribution for Unmanned Aerial Vehicle (UAV) swarm, a new type of multi-Task distribution model for UAVs is established in this paper, various constraints are considered, such as bomb load and damage loss. To solve the multi-Task allocation problem, an improved mixed discrete particle swarm optimization algorithm (IM-DPSO) is proposed, a two-dimensional particle coding matrix with task priority is designed, the genetic variation rules with particle update strategies are combined, and then the inertia weight and learning factors are optimized to enhance the algorithm's ability of solving the problem. Simulation results show that the improved algorithm can better solve the problem of multi-Task allocation for UAVs under the distribution model.

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Jia Z, Xiao B, Qian H. Improved Mixed Discrete Particle Swarms based Multi-Task Assignment for UAVs. In Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 442-448. (Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023). doi: 10.1109/DDCLS58216.2023.10166130

Improved Mixed Discrete Particle Swarms based Multi-Task Assignment for UAVs

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