TY - GEN
T1 - An Improved Hybrid Grey Wolf Optimizer for Multi-Agent Trajectory Planning in Complex Environment
AU - Zhu, Yutong
AU - Zhang, Ye
AU - Wang, Jingyu
AU - Zhang, Ke
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper considers the problem that Grey Wolf Optimizer (GWO) has some defects in solving trajectory optimization problems. To solve this problem, this paper proposes the improved GWO algorithm based on GWO by the idea of linear differential decrement and dynamic exponential weighted average. Compared with other algorithms, this algorithm has more flexibility in position updating and finds the global optimal solution effectively. Finally, simulation results demonstrate the superiority of the improved GWO algorithm in terms of search accuracy and running time.
AB - This paper considers the problem that Grey Wolf Optimizer (GWO) has some defects in solving trajectory optimization problems. To solve this problem, this paper proposes the improved GWO algorithm based on GWO by the idea of linear differential decrement and dynamic exponential weighted average. Compared with other algorithms, this algorithm has more flexibility in position updating and finds the global optimal solution effectively. Finally, simulation results demonstrate the superiority of the improved GWO algorithm in terms of search accuracy and running time.
KW - Grey wolf optimizer
KW - dynamic exponentially weighted average
KW - linear differential decrement
KW - trajectory optimization
UR - http://www.scopus.com/inward/record.url?scp=85189353339&partnerID=8YFLogxK
U2 - 10.1109/CAC59555.2023.10451848
DO - 10.1109/CAC59555.2023.10451848
M3 - 会议稿件
AN - SCOPUS:85189353339
T3 - Proceedings - 2023 China Automation Congress, CAC 2023
SP - 8631
EP - 8636
BT - Proceedings - 2023 China Automation Congress, CAC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 China Automation Congress, CAC 2023
Y2 - 17 November 2023 through 19 November 2023
ER -