Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (4): 969-985.doi: 10.23919/JSEE.2022.000094

• SYSTEMS ENGINEERING • Previous Articles     Next Articles

Multi-objective optimization of operation loop recommendation for kill web

Kewei YANG(), Boyuan XIA(), Gang CHEN*(), Zhiwei YANG(), Minghao LI()   

  1. 1 College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
  • Received:2021-11-13 Online:2022-08-30 Published:2022-08-30
  • Contact: Gang CHEN E-mail:kayyan927@nudt.edu.cn;xiaboyuan@nudt.edu.cn;chengang@nudt.edu.cn;zhwyang88@126.com;liminghao@nudt.edu.cn
  • About author:|YANG Kewei was born in 1977. He received his B.E. and Ph.D. degrees in systems engineering, and management science and engineering from the National University of Defense Technology, Changsha, Hunan, China in 1999 and 2004, respectively. He is currently a professor of management science and engineering and the Director of the Department of Management, in the College of Systems Engineering at the National University of Defense Technology. He was a visiting scholar with the Department of Computer Science at the University of York in the United Kingdom, and with the Science and Technology on Complex Systems Simulation Laboratory, Beijing, China. He has been a member of the Youth Working Committee in the Systems Engineering Society of China since 2009. His research interests focus on intelligent agent simulation, defense acquisition, and system-of-systems requirement modeling. E-mail: kayyan927@nudt.edu.cn||XIA Boyuan was born in 1994. He received his B.E., M.E. and Ph.D. degrees from the National University of Defense Technology in 2015, 2017 and 2021 respectively. His research interests are defense acquisition, complex systems, and system portfolio selection and optimization. E-mail: xiaboyuan@nudt.edu.cn||CHEN Gang was born in 1997. He received his B.E. degree from China University of Mining and Technology in 2019. He is currently a Ph.D. candidate at the National University of Defense Technology. His research interests focus on applying deep reinforcement learning to study game methods and applications of intelligent system-of-systems confrontation, intelligent optimization methods, and complex networks. E-mail: chengang@nudt.edu.cn||YANG Zhiwei was born in 1988. He received his B.E., M.E., and Ph.D. degrees in management science and engineering from National University of Defense Technology, Changsha, Hunan, China in 2010, 2012, and 2016, respectively. He is an associate professor at National University of Defense Technology. His research interests are system optimization, and supply chain optimization. E-mail: zhwyang88@126.com||LI Minghao was born in 1990. He received his M.E. and Ph.D. degrees in management science and engineering from National University of Defense Technology, Changsha, China in 2014 and 2018 respectively. In 2016, he was a visiting scholar with the Leiden University, Leiden, Netherlands. He is currently a lecturer at the College of Systems Engineering, National University of Defense Technology. His current research interests include model based system engineering, defense acquisition, system of systems mo-deling and optimization. E-mail: liminghao@nudt.edu.cn
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (72071206; 71690233), and the Science and Technology Innovation Program of Hunan Province (2020RC4046)

Abstract:

In order to improve our military’s level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental combat form of the future, i.e., “web-based kill,” and the operation loop theory. Firstly, we pioneer the operation loop recommendation problem with operation ring quality as the objective and closed-loop time as the constraint, and construct the corresponding planning model. Secondly, considering the case where there are multiple decision objectives for the combat ring recommendation problem, we propose for the first time a multi-objective optimization algorithm, the multi-objective ant colony evolutionary algorithm based on decomposition (MOACEA/D), which integrates the multi-objective evolutionary algorithm based on decomposition (MOEA/D) with the ant colony algorithm. The MOACEA/D can converge the optimal solutions of multiple single objectives non-dominated solution set for the multi-objective problem. Finally, compared with other classical multi-objective optimization algorithms, the MOACEA/D is superior to other algorithms superior in terms of the hyper volume (HV), which verifies the effectiveness of the method and greatly improves the quality and efficiency of commanders’ decision-making.

Key words: multi-objective, operation loop recommendation, kill web, ant colony, evolutionary algorithm, hyper volume (HV)