An Improved Hybrid Grey Wolf Optimizer for Multi-Agent Trajectory Planning in Complex Environment

Abstract

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.