Multi-Agent Swarm Optimization Method With Contribution-Based Cooperation for Distributed Multi-Target Localization and Data Association

Taiyou Chen; Xiaomin Hu; Qiuzhen Lin; Weineng Chen

doi:10.1109/JAS.2025.125150

Volume 13 Issue 7

Jul. 2026

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 18.3, Top 1 (SCI Q1)

CiteScore: 28.2, Top 1% (Q1)
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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(7): 1673-1688

T. Chen, X. Hu, Q. Lin, and W. Chen, “Multi-agent swarm optimization method with contribution-based cooperation for distributed multi-target localization and data association,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 7, pp. 1673–1688, Jul. 2026. doi: 10.1109/JAS.2025.125150

Citation:

T. Chen, X. Hu, Q. Lin, and W. Chen, “Multi-agent swarm optimization method with contribution-based cooperation for distributed multi-target localization and data association,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 7, pp. 1673–1688, Jul. 2026. doi: 10.1109/JAS.2025.125150

Citation:

T. Chen, X. Hu, Q. Lin, and W. Chen, “Multi-agent swarm optimization method with contribution-based cooperation for distributed multi-target localization and data association,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 7, pp. 1673–1688, Jul. 2026. doi: 10.1109/JAS.2025.125150

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Multi-Agent Swarm Optimization Method With Contribution-Based Cooperation for Distributed Multi-Target Localization and Data Association

doi: 10.1109/JAS.2025.125150

Funds: This work was supported in part by the National Natural Science Foundation of China (62376097) and Guangdong Regional Joint Foundation Key Program (2022B1515120076)

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Author Bio:
Tai-You Chen received the bachelor degree in computer science and technology from the South China University of Technology in 2022, where he is currently pursuing the Ph.D. degree in computer science and technology with the School of Computer Science and Engineering.His current research interests include swarm intelligence, evolutionary computation, consensus-based distributed optimization, multi-agent systems, and their applications in real-world problems

Xiao-Min Hu received the bachelor and Ph.D. degrees from Sun Yat-sen University in 2006 and 2011, respectively. She is currently an Associate Professor with the School of Computer Science and Technology, Guangdong University of Technology. She has published over 30 papers in international journals and conferences.Her current research interests include artificial intelligence, data mining, evolutionary computation, swarm intelligence, and their applications in design and optimization on wireless sensor networks, route optimization, and big data analysis. Dr. Hu was a recipient of the Pearl River S&T Nova Program of Guangzhou in 2018

Qiuzhen Lin (Member, IEEE) received the B.S. degree from Zhaoqing University in 2007, the M.S. degree from Shenzhen University in 2010, and the Ph.D. degree from the Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China in 2014.He is currently an Associate Professor with the College of Computer Science and Software Engineering, Shenzhen University. He has published over sixty research papers since 2008. He is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions on Emerging Topics in Computational Intelligence. His current research interests include artificial immune system, multiobjective optimization, and dynamic system

Wei-Neng Chen (Senior Member, IEEE) received the bachelor and Ph.D. degrees in computer science from Sun Yat-sen University in 2006 and 2012, respectively.Since 2016, he has been a Full Professor with the School of Computer Science and Engineering, South China University of Technology. He has co-authored over 200 international journal and conference papers, including more than 70 papers published in the IEEE Transactions journals. His current research interests include computational intelligence, swarm intelligence, network science, and their applications.Dr. Chen was a recipient of the IEEE Computational Intelligence Society (CIS) Outstanding Dissertation Award in 2016, and the National Science Fund for Excellent Young Scholars in 2016. He was also a Principal Investigator (PI) of the National Science and Technology Innovation 2030 the Next Generation Artificial Intelligence Key Project. He is currently the Vice-Chair of the IEEE Guangzhou Section, and the Chair of IEEE SMC Society Guangzhou Chapter. He is also a Committee Member of the IEEE CIS Emerging Topics Task Force. He serves as an Associate Editor for the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Networks and Learning Systems, and the Complex and Intelligent Systems
Corresponding author: Weineng Chen, e-mail: cschenwn@scut.edu.cn
Received Date: 2024-10-19
Accepted Date: 2024-12-27

Available Online: 2025-03-21

Abstract

Abstract

With the development of communication and computation capabilities on terminal hardware, it is promising to apply distributed optimization methods to wireless sensor networks to improve the autonomous collaboration ability of sensors. In this work, we study distributed multi-target localization problem with measurement-to-measurement association (DM2M), where each sensor only accesses its own measurement data without the association of measurements from other sensors. We first reformulate DM2M into a distributed bilevel optimization problem to reduce the search space of negotiated variables caused by the data association among sensors. Then, we propose a multi-agent swarm optimization method with contribution-based cooperation (MASTER). In MASTER, each sensor maintains a particle swarm to represent candidate solutions of target positions. Sensors evolve their particle swarms through two phases of local optimization and neighbor cooperation to locate the target cooperatively. To address the bilevel local objective function, we combine the Kuhn-Munkres algorithm and the competitive swarm optimization for local optimization. To promote sensors to optimize the global objective, we design a contribution-based cooperation method to guide sensors to learn from their neighbors. Through localization experiments for different target numbers and localization dimensions, the proposed algorithm achieves smaller localization errors and more stable consensus than existing algorithms.

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References

[1]	G. Mao, B. Fidan, and B. D. O. Anderson, “Wireless sensor network localization techniques,” Comput. Netw., vol. 51, no. 10, pp. 2529–2553, Jul. 2007. doi: 10.1016/j.comnet.2006.11.018
[2]	S. Wang, L. Chen, D. Gu, and H. Hu, “Cooperative localization of AUVs using moving horizon estimation,” IEEE/CAA J. Autom. Sinica, vol. 1, no. 1, pp. 68–76, Jan. 2014. doi: 10.1109/jas.2014.7004622
[3]	R. A. Khalil, N. Saeed, M. I. Babar, T. Jan, and S. Din, “Bayesian multidimensional scaling for location awareness in hybrid-internet of underwater things,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 496–509, Mar. 2022. doi: 10.1109/JAS.2021.1004356
[4]	H. Zhao, J. Yan, X. Luo, and X. Gua, “Privacy preserving solution for the asynchronous localization of underwater sensor networks,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 6, pp. 1511–1527, Nov. 2020.
[5]	W. Huang, Z. Pan, and Z. Xu, “Underwater cable localization method based on beetle swarm optimization algorithm,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 9, pp. 1893–1895, Sep. 2023. doi: 10.1109/JAS.2022.106073
[6]	G. Tabella, D. Ciuonzo, N. Paltrinieri, and P. S. Rossi, “Bayesian fault detection and localization through wireless sensor networks in industrial plants,” IEEE Internet Things J., vol. 11, no. 8, pp. 13231–13246, Apr. 2024. doi: 10.1109/JIOT.2024.3359646
[7]	I. Ahmed, S. Din, G. Jeon, F. Piccialli, and G. Fortino, “Towards collaborative robotics in top view surveillance: A framework for multiple object tracking by detection using deep learning,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1253–1270, Jul. 2021. doi: 10.1109/JAS.2020.1003453
[8]	D. Touska, K. Gkountakos, T. Tsikrika, K. Ioannidis, S. Vrochidis, and I. Kompatsiaris, “Graph-based data association in multiple object tracking: A survey,” in Proc. 29th Int. Conf. MultiMedia Modeling, Bergen, Norway, 2023, pp. 386−398.
[9]	J. H. White and R. W. Beard, “An iterative pose estimation algorithm based on epipolar geometry with application to multi-target tracking,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 942–953, Jul. 2020. doi: 10.1109/jas.2020.1003222
[10]	H. Liu, Y. Chen, Y. Huang, X. Cheng, and Q. Xiao, “Study on the localization method of multi-aperture acoustic array based on TDOA,” IEEE Sensors J., vol. 21, no. 12, pp. 13805–13814, Jun. 2021. doi: 10.1109/JSEN.2021.3070811
[11]	L. Rakai, H. Song, S. Sun, W. Zhang, and Y. Yang, “Data association in multiple object tracking: A survey of recent techniques,” Expert Syst. Appl., vol. 192, Art. no. 116300, Apr. 2022. doi: 10.1016/j.eswa.2021.116300
[12]	P. Parmar and M. Zaveri, “Multiple target tracking and data association in wireless sensor network,” in Proc. 4th Int. Conf. Computational Intelligence and Communication Networks, Mathura, India, 2012, pp. 158−163.
[13]	H. Chen, T. Kirubarajan, and Y. Bar-Shalom, “Tracking of spawning targets with multiple finite resolution sensors,” in Proc. 5th Int. Conf. Information Fusion, Annapolis, MD, USA, 2002, pp. 1511−1518.
[14]	A. T. Kamal, J. H. Bappy, J. A. Farrell, and A. K. Roy-Chowdhury, “Distributed multi-target tracking and data association in vision networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 7, pp. 1397–1410, Jul. 2016. doi: 10.1109/TPAMI.2015.2484339
[15]	N. F. Sandell and R. Olfati-Saber, “Distributed data association for multi-target tracking in sensor networks,” in Proc. 47th IEEE Conf. Decision and Control, Cancun, Mexico, 2008, pp. 1085−1090.
[16]	Y. Zhu, A. Vikram, H. Fu, and Y. Guan, “On non-cooperative multiple-target tracking with wireless sensor networks,” IEEE Trans. Wireless Commun., vol. 13, no. 11, pp. 6496–6510, Nov. 2014. doi: 10.1109/TWC.2014.2329306
[17]	B. Camajori Tedeschini, M. Brambilla, L. Barbieri, G. Balducci, and M. Nicoli, “Cooperative Lidar sensing for pedestrian detection: Data association based on message passing neural networks,” IEEE Trans. Signal Process., vol. 71, pp. 3028–3042, Aug. 2023. doi: 10.1109/TSP.2023.3304002
[18]	S. A. R. Kazemi, R. Amiri, and F. Behnia, “Data association for multi-target elliptic localization in distributed MIMO radars,” IEEE Commun. Lett., vol. 25, no. 9, pp. 2904–2907, Sep. 2021. doi: 10.1109/LCOMM.2021.3087009
[19]	T. Ge, R. Tharmarasa, B. Lebel, M. Florea, and T. T. Kirubarajan, “A multidimensional TDOA association algorithm for joint multitarget localization and multisensor synchronization,” IEEE Trans. Aerosp. Electron. Syst., vol. 56, no. 3, pp. 2083–2100, Jun. 2020. doi: 10.1109/TAES.2019.2943786
[20]	Z. Wei, Z. Duan, and M. Mallick, “Measurement-to-measurement association for MDA with a practical coarse gating strategy,” in Proc. 26th Int. Conf. Information Fusion, Charleston, SC, USA, 2023, pp. 1−8.
[21]	X. Dang, Q. Cheng, and H. Zhu, “Indoor multiple sound source localization via multi-dimensional assignment data association,” IEEE/ACM Trans. Audio Speech Lang. Process., vol. 27, no. 12, pp. 1944–1956, Dec. 2019. doi: 10.1109/TASLP.2019.2935837
[22]	A. Ez-Zaidi and S. Rakrak, “A comparative study of target tracking approaches in wireless sensor networks,” J. Sens., vol. 2016, no. 1, Art. no. 3270659, Dec. 2016. doi: 10.1155/2016/3270659
[23]	Y. Zhang, Y. Lou, Y. Hong, and L. Xie, “Distributed projection-based algorithms for source localization in wireless sensor networks,” IEEE Trans. Wireless Commun., vol. 14, no. 6, pp. 3131–3142, Jun. 2015. doi: 10.1109/TWC.2015.2402672
[24]	Y. Lv, H. Zhang, Z. Wang, and H. Yan, “Distributed localization for dynamic multiagent systems with randomly varying trajectory lengths,” IEEE Trans. Ind. Electron., vol. 69, no. 9, pp. 9298–9308, Sep. 2022. doi: 10.1109/TIE.2021.3111571
[25]	Y. Wang, X. Chen, L. Shi, Y. Cheng, and H. Wang, “A trust assessment-based distributed localization algorithm for sensor networks under deception attacks,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1879–1882, Oct. 2022. doi: 10.1109/JAS.2022.105881
[26]	S. Wang, X. Jiang, and H. Wymeersch, “Cooperative localization in wireless sensor networks with AOA measurements,” IEEE Trans. Wireless Commun., vol. 21, no. 8, pp. 6760–6773, Aug. 2022. doi: 10.1109/TWC.2022.3152426
[27]	Y. Zhao, Z. Li, B. Hao, and J. Shi, “Sensor selection for TDOA-based localization in wireless sensor networks with non-line-of-sight condition,” IEEE Trans. Veh. Technol., vol. 68, no. 10, pp. 9935–9950, Oct. 2019. doi: 10.1109/TVT.2019.2936110
[28]	D. Dasgupta, G. Hernandez, D. Garrett, P. K. Vejandla, A. Kaushal, R. Yerneni, and J. Simien, “A comparison of multiobjective evolutionary algorithms with informed initialization and Kuhn-Munkres algorithm for the sailor assignment problem,” in Proc. 10th Ann. Conf. Companion on Genetic and Evolutionary Computation, Atlanta, GA, USA, 2008, pp. 2129−2134.
[29]	R. Cheng and Y. Jin, “A competitive swarm optimizer for large scale optimization,” IEEE Trans. Cybern., vol. 45, no. 2, pp. 191–204, Feb. 2015. doi: 10.1109/TCYB.2014.2322602
[30]	G. Wen, H. Song, F. Gao, Y. Liang, and L. Zhu, “Target localization in asynchronous distributed MIMO radar systems with a cooperative target,” IEEE Trans. Wireless Commun., vol. 21, no. 6, pp. 4098–4113, Jun. 2022. doi: 10.1109/TWC.2021.3126745
[31]	J. A. Costa, N. Patwari, and A. O. Hero, “Distributed weighted-multidimensional scaling for node localization in sensor networks,” ACM Trans. Sen. Netw., vol. 2, no. 1, pp. 39–64, Feb. 2006. doi: 10.1145/1138127.1138129
[32]	K. W. K. Lui, W. K. Ma, H. C. So, and F. K. W. Chan, “Semi-definite programming algorithms for sensor network node localization with uncertainties in anchor positions and/or propagation speed,” IEEE Trans. Signal Process., vol. 57, no. 2, pp. 752–763, Feb. 2009. doi: 10.1109/TSP.2008.2007916
[33]	J. Nie, “Sum of squares method for sensor network localization,” Comput. Optim. Appl., vol. 43, no. 2, pp. 151–179, Jun. 2009. doi: 10.1007/s10589-007-9131-z
[34]	S. Srirangarajan, A. H. Tewfik, and Z. Q. Luo, “Distributed sensor network localization using SOCP relaxation,” IEEE Trans. Wireless Commun., vol. 7, no. 12, pp. 4886–4895, Dec. 2008. doi: 10.1109/T-WC.2008.070241
[35]	A. Nedic and A. Ozdaglar, “Distributed subgradient methods for multi-agent optimization,” IEEE Trans. Autom. Control, vol. 54, no. 1, pp. 48–61, Jan. 2009. doi: 10.1109/TAC.2008.2009515
[36]	D. Yuan and D. W. C. Ho, “Randomized gradient-free method for multiagent optimization over time-varying networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 6, pp. 1342–1347, Jun. 2015. doi: 10.1109/TNNLS.2014.2336806
[37]	M. Chamanbaz, G. Notarstefano, F. Sasso, and R. Bouffanais, “Randomized constraints consensus for distributed robust mixed-integer programming,” IEEE Trans. Control Netw. Syst., vol. 8, no. 1, pp. 295–306, Mar. 2021. doi: 10.1109/TCNS.2020.3024483
[38]	Y. Pang and G. Hu, “Randomized gradient-free distributed optimization methods for a multiagent system with unknown cost function,” IEEE Trans. Autom. Control, vol. 65, no. 1, pp. 333–340, Jan. 2020. doi: 10.1109/TAC.2019.2914025
[39]	W. Ai, W. Chen, and J. Xie, “A general framework for population-based distributed optimization over networks,” Inform. Sci., vol. 418−419, pp. 136–152, Dec. 2017.
[40]	Y. Wakasa and S. Nakaya, “Distributed particle swarm optimization using an average consensus algorithm,” in Proc. 54th IEEE Conf. Decision and Control, Osaka, Japan, 2015, pp. 2661−2666.
[41]	M. K. Jalloul and M. A. Al-Alaoui, “A distributed Particle Swarm Optimization algorithm for block motion estimation using the strategies of diffusion adaptation,” in Proc. Int. Symp. Signals, Circuits and Systems, Iasi, Romania, 2015, pp. 1−4.
[42]	Q. Yang, W. N. Chen, T. Gu, H. Zhang, H. Yuan, S. Kwong, and J. Zhang, “A distributed swarm optimizer with adaptive communication for large-scale optimization,” IEEE Trans. Cybern., vol. 50, no. 7, pp. 3393–3408, Jul. 2020. doi: 10.1109/TCYB.2019.2904543
[43]	Y. H. Jia, W. N. Chen, T. Gu, H. Zhang, H. Q. Yuan, S. Kwong, and J. Zhang, “Distributed cooperative co-evolution with adaptive computing resource allocation for large scale optimization,” IEEE Trans. EComput., vol. 23, no. 2, pp. 188–202, Apr. 2019.
[44]	A. Song, W. N. Chen, Y. J. Gong, X. Luo, and J. Zhang, “A divide-and-conquer evolutionary algorithm for large-scale virtual network embedding,” IEEE Trans. EComput., vol. 24, no. 3, pp. 566–580, Jun. 2020.
[45]	Z. Zhou, Z. Peng, J.-H. Cui, Z. Shi, and A. Bagtzoglou, “Scalable localization with mobility prediction for underwater sensor networks,” IEEE Trans. Mobile Computing, vol. 10, no. 3, pp. 335–348, 2010.
[46]	C. He, Y. Wang, W. Yu, and L. Song, “Underwater target localization and synchronization for a distributed SIMO sonar with an isogradient SSP and uncertainties in receiver locations,” Sensors, vol. 19, no. 9, Art. no. 1976, Apr. 2019. doi: 10.3390/s19091976
[47]	M. Brambilla, M. Nicoli, G. Soatti, and F. Deflorio, “Augmenting vehicle localization by cooperative sensing of the driving environment: Insight on data association in urban traffic scenarios,” IEEE Trans. Intell. Transport. Syst., vol. 21, no. 4, pp. 1646–1663, Apr. 2020. doi: 10.1109/TITS.2019.2941435
[48]	Y. Lei, Y. Wu, and A. J. K. Chowdhury, “Multi-target tracking algorithm in intelligent transportation based on wireless sensor network,” Open Phys., vol. 16, no. 1, pp. 1000–1008, Dec. 2018. doi: 10.1515/phys-2018-0121
[49]	H. Dai, Z. M. Zhu, and X. F. Gu, “Multi-target indoor localization and tracking on video monitoring system in a wireless sensor network,” J. Netw. Comput. Appl., vol. 36, no. 1, pp. 228–234, Jan. 2013. doi: 10.1016/j.jnca.2012.08.013
[50]	S. Xu, K. Doğançay, and H. Hmam, “Distributed pseudolinear estimation and UAV path optimization for 3D AOA target tracking,” Sig. Process., vol. 133, pp. 64–78, Apr. 2017. doi: 10.1016/j.sigpro.2016.10.012
[51]	T. Y. Chen, W. N. Chen, F. F. Wei, X. M. Hu, and J. Zhang, “Multiagent swarm optimization with adaptive internal and external learning for complex consensus-based distributed optimization,” IEEE Trans. EComput., vol. 29, no. 4, pp. 906–920, Aug. 2025. doi: 10.1109/tevc.2024.3380436/mm1
[52]	X. Yi, S. Zhang, T. Yang, and K. H. Johansson, “Zeroth-order algorithms for stochastic distributed nonconvex optimization,” Automatica, vol. 142, Art. no. 110353, Aug. 2022. doi: 10.1016/j.automatica.2022.110353
[53]	V. Krishnamurthy, K. Pattanayak, S. Gogineni, B. Kang, and M. Rangaswamy, “Adversarial radar inference: Inverse tracking, identifying cognition, and designing smart interference,” IEEE Trans. Aerosp. Electron. Syst., vol. 57, no. 4, pp. 2067–2081, Aug. 2021. doi: 10.1109/TAES.2021.3090901
[54]	D. Ciuonzo, P. K. Willett, and Y. Bar-Shalom, “Tracking the tracker from its passive sonar ML-PDA estimates,” IEEE Trans. Aerosp. Electron. Syst., vol. 50, no. 1, pp. 573–590, Jan. 2014. doi: 10.1109/TAES.2013.120407