Research

Research Interests

• Evolutionary Computation and Machine Learning

  • Knowledge and data-assisted metaheuristic

  • Multiobjective evolutionary algorithm (MOEA)

  • Deep reinforcement learning for dynamic decision-making

• Operations Research

  • Planning and decision making

  • Vehicle routing problems

  • Air-ground coordination

Publications

1. [CEC] Z. Zhang, L. Wang, and C. Chen*, "A triple network knowledge learning framework for particle swarm optimization," IEEE Congress on Evolutionary Computation (CEC), Yokohama, Japan, 2024, pp. 1-8. [DOI]

2. [CCC] Z. Zhang, L. Wang, and C. Chen*, "A dual-archive memetic algorithm with motion prediction for solving dynamic multiple traveling salesmen problem," 43rd Chinese Control Conference (CCC), Kunming, China, 2024, pp. 2177-2182. [DOI]

3. [CCC] Y. Wang, Z. Zhang (co-first author), and W. Quan, "A multi-strategy enhanced memetic algorithm for airport refueling vehicle scheduling problem," 43rd Chinese Control Conference (CCC), Kunming, China, 2024, pp. 1897-1902. [DOI]

4. [ICUS] W. Quan, L. Jia, Z. Zhang, C. Chen*, and L. Wang, "A multi-dimensional dynamic evaluation method for the intelligence of unmanned aerial vehicle swarm," IEEE International Conference on Unmanned Systems (ICUS), Hefei, China, 2023, pp. 731-737. [DOI]

5. [YAC] L. Jia, L. Wang, W. Quan, E. Qiao, Z. Zhang, and C. Chen*, "A multi-agent based simulation modeling method for UAV swarm collaborative combat," Youth Academic Annual Conference of Chinese Association of Automation (YAC), Dalian, China, 2024, pp. 1061-1066. [DOI]

Preprints

1. [TASE] Z. Zhang, C. Chen*, L. Wang, Y. Ding, and F. Deng, "A Two-phase Planner for Messenger Routing Problem in UAV-UGV Coordination Systems," Submitted to IEEE Transactions on Automation Science and Engineering, 2025. (JCR Q1)

2. [INS] C. Miao, Y. Zhang, C. Chen*, Z. Zhang, and B. Xin, "Knowledge-assisted metaheuristic based on deep graph learning for travelling salesman problem," Submitted to Information Sciences, 2024. (JCR Q1)

Patents

1. Predictive based path planning method for dynamic target interception, C.N. Patent, Application No. 202311504203.X.

2. Path planning method for multi-UAV coverage in irregular regions, C.N. Patent.

Research Experience

• Research on evolutionary learning and learning to optimize, Sep. 2023 - Present

  • Mining and utilizing process data to assist evolutionary algorithms to achieve more efficient evolution.

  • Extracting knowledge of successful solutions to assist metaheuristics in solving the traveling salesman problem.

  • Applying deep reinforcement learning to solve combinatorial optimization problems.

• Messenger path planning in UAV and UGV coordination systems, Oct. 2022 - Sep. 2023

  • Studied a path planning problem of multiple messenger UAVs accessing the communication neighborhood of moving UGVs.

  • Established a dynamic min-max mathematical model with mixed variables.

  • Proposed a two-step planner based on the idea of decoupling and introduced a motion prediction module to solve this problem.

• Optimization and decision making in dynamic deployment for heterogeneous resources, Oct. 2022 - Aug. 2023

  • Established a three-dimensional sensor coverage mathematical model.

  • Established a static deployment model with mixed variables and a driving strategy for dynamic deployment.

  • Designed a variety of heuristic methods to achieve dynamic deployment of resources.

• Intelligence evaluation of unmanned aerial vehicle swarm, Dec. 2021 - May 2023

  • Established an intelligent description structure based on a multi-dimensional evaluation indicator system.

  • Constructed a dynamic evaluation method combining subjective and objective weights based on the data field.

  • Designed a variety of typical scenarios and conducted simulations.