A Reference Vector-Guided and Generative Adversarial Network-Driven Competitive Swarm Optimizer for Large-Scale Multi-Objective Optimization

Chunlei Li; Libao Deng; Liyan Qiao; Lili Zhang

doi:10.1109/JAS.2026.125849

Volume 13 Issue 8

Aug. 2026

IEEE/CAA Journal of Automatica Sinica

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

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(8): 1952-1968

C. Li, L. Deng, L. Qiao, and L. Zhang, “A reference vector-guided and generative adversarial network-driven competitive swarm optimizer for large-scale multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 8, pp. 1952–1968, Aug. 2026. doi: 10.1109/JAS.2026.125849

Citation:

C. Li, L. Deng, L. Qiao, and L. Zhang, “A reference vector-guided and generative adversarial network-driven competitive swarm optimizer for large-scale multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 8, pp. 1952–1968, Aug. 2026. doi: 10.1109/JAS.2026.125849

Citation:

C. Li, L. Deng, L. Qiao, and L. Zhang, “A reference vector-guided and generative adversarial network-driven competitive swarm optimizer for large-scale multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 8, pp. 1952–1968, Aug. 2026. doi: 10.1109/JAS.2026.125849

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A Reference Vector-Guided and Generative Adversarial Network-Driven Competitive Swarm Optimizer for Large-Scale Multi-Objective Optimization

doi: 10.1109/JAS.2026.125849

Funds: This work was supported in part by the National Natural Science Foundation of China (62176075), the National Key Research and Development Program of China (2022YFB3304000), and Shandong Provincial Natural Science Foundation (ZR2021MF063)

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Author Bio:
Chunlei Li received the B.Sc. degree in 2021 from Harbin Institute of Technology (Weihai), where he is currently working toward the Ph.D. degree with the School of Information Science and Engineering. His research interests include multi/many-objective optimization, large-scale optimization, constrained multi-objective optimization, and evolutionary algorithms. He is a Regular Reviewer of IEEE Transactions on Evolutionary Computation, IEEE Transactions on Systems, Man and Cybernetics: Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, and so on

Libao Deng (Member, IEEE) received the B.Sc., M.Sc., and Ph.D. degrees from Harbin Institute of Technology in 2004, 2007, and 2012, respectively. He is currently a Professor with the School of Information Science and Engineering, Harbin Institute of Technology. His research interests include multi/many-objective optimization, large-scale optimization, constrained multi-objective optimization, and optimal scheduling. He is a Regular Reviewer of IEEE Transactions on Cybernetics, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Systems, Man and Cybernetics: Systems, IEEE Transactions on Circuits and Systems, Swarm and Evolutionary Computation, Information Sciences, Complex & Intelligent Systems, and so on

Liyan Qiao (Member, IEEE) received the B.Sc., M.Sc., and Ph.D. degrees from Harbin Institute of Technology in 1996, 1998, and 2005, respectively. He is currently a Full Professor with the School of Electronics and Information Engineering, Harbin Institute of Technology. He has authored or coauthored more than 100 journal and conference papers. His research interests include intelligent computation, data acquisition technology,mass-storage data record technology, and test information processing

Lili Zhang received the B.Sc. and M.Sc. degrees in instrumental science and technology from the Harbin Institute of Technology in 2017 and 2019, respectively, and the Ph.D. degree in computer science from Dublin City University, Dublin, Ireland, in 2022. She is currently a Postdoc with the School of Computing, Dublin City University and a Lecturer with the Department of Computer Science, the National University of Ireland, Dublin, Ireland. Her research interests include intelligent computing and computational psychiatry
Corresponding author: Libao Deng, e-mail: dlb@hit.edu.cn
Received Date: 2025-05-14
Revised Date: 2025-10-14
Accepted Date: 2025-12-24

Available Online: 2026-08-20

Abstract

Abstract

Competitive swarm optimizers (CSOs) have demonstrated promising performance in addressing large-scale optimization challenges. However, they are often criticized for inefficient convergence when solving large-scale multi-objective optimization problems (LSMOPs) due to their competition and learning mechanisms being originally designed for single-objective scenarios. To overcome this limitation, this paper proposes a reference vector-guided and generative adversarial network (GAN)-driven CSO variant for LSMOPs. The algorithm introduces a dual-criteria competition mechanism to replace the conventional random pairwise competition, enabling more effective swarm division into winner and loser groups while preserving diverse search directions. Furthermore, it enhances particle update through two comprehensive learning strategies: a reference vector-guided update strategy that directs loser particles toward promising search directions in a self-supervised manner, coupled with a GAN-driven reproduction strategy where a GAN model is trained to capture the distribution patterns of high-performing winner particles, subsequently generating refined candidate solutions with improved quality and diversity. By synergizing the strong exploration of CSO with the efficient exploitation of GAN-based reproduction, the proposed algorithm achieves promising optimization performance. Experimental results demonstrate its superiority over seven state-of-the-art large-scale multi-objective optimization algorithms on both LSMOP benchmark problems and real-world applications.
- Competitive swarm optimizer (CSO),
- generative adversarial network (GAN),
- large-scale optimization,
- multi-objective optimization

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References(53)

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