TY - GEN
T1 - Multi-objective Optimization of Electromagnetic Launch System Based on Improved RVEA-IGNG Algorithm
AU - Li, Xiaoyu
AU - Guo, Ke
AU - Chao, Tao
AU - Ma, Ping
AU - Yang, Ming
AU - Wang, Songyan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - For analyzing the influences of discharge sequence of each pulsed power supply and discharge voltage on the performance of the electromagnetic launch system, intelligent optimization algorithms are carried out based on the simulation model of the electromagnetic launch system. Based on the maximum rail current, projectile exit velocity and energy conversion efficiency, the corresponding multi-objective optimization model is established, and the Pareto front (PF) is acquired in RVEA-iGNG algorithm. The algorithm combines an evolutionary optimization algorithm with the reference vector adaptive adjustment by growing neural gas network. It can handle the multi-objective optimization of the electromagnetic launch system with complex irregular PF and achieve fast convergence. In order to provide decision makers with more diversified solutions, this paper proposes an improved RVEA-iGNG algorithm where the archive maintenance strategy is improved. The simulation results not only show that the RVEA-iGNG algorithm can effectively solve the electromagnetic launch system multi-objective optimization problem, but also show that the improved archive maintenance strategy is significantly better than the archive in the original RVEA-iGNG in terms of enhancing the diversity of solutions.
AB - For analyzing the influences of discharge sequence of each pulsed power supply and discharge voltage on the performance of the electromagnetic launch system, intelligent optimization algorithms are carried out based on the simulation model of the electromagnetic launch system. Based on the maximum rail current, projectile exit velocity and energy conversion efficiency, the corresponding multi-objective optimization model is established, and the Pareto front (PF) is acquired in RVEA-iGNG algorithm. The algorithm combines an evolutionary optimization algorithm with the reference vector adaptive adjustment by growing neural gas network. It can handle the multi-objective optimization of the electromagnetic launch system with complex irregular PF and achieve fast convergence. In order to provide decision makers with more diversified solutions, this paper proposes an improved RVEA-iGNG algorithm where the archive maintenance strategy is improved. The simulation results not only show that the RVEA-iGNG algorithm can effectively solve the electromagnetic launch system multi-objective optimization problem, but also show that the improved archive maintenance strategy is significantly better than the archive in the original RVEA-iGNG in terms of enhancing the diversity of solutions.
KW - Decomposition-based multi-objective evolutionary optimization
KW - Electromagnetic launch
KW - Growing neural gas
KW - Irregular pareto front
KW - Reference vector adjustment
UR - https://www.scopus.com/pages/publications/85209595870
U2 - 10.1007/978-981-97-6591-1_27
DO - 10.1007/978-981-97-6591-1_27
M3 - 会议稿件
AN - SCOPUS:85209595870
SN - 9789819765904
T3 - Lecture Notes in Electrical Engineering
SP - 283
EP - 292
BT - Proceedings of the 19th International Conference on Intelligent Unmanned Systems - ICIUS 2023
A2 - Akmeliawati, Rini
A2 - Harvey, David
A2 - Sergiienko, Nataliia
A2 - Yang, Lung-Jieh
A2 - Park, Hoon Cheol
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th International Conference of Intelligent Unmanned Systems, ICIUS 2023
Y2 - 5 July 2023 through 7 July 2023
ER -