Path planning of aircraft based on adaptive multiobjective estimation of distribution algorithm

Lin, Tao; Zhang, Ke; Cui, Naigang; Tu, Zhenbiao; Zhang, Hu

doi:10.1109/ssci.2016.7850199

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Numerous researchers have dedicated to developing effective EDAs, which have been widely applied to various optimization problems, such as multi-modal optimization problems [17], multi-objective optimization problems [18], and practical problems like multistrategy insurance investment planning [19] and contentdriven interactions generated by multiple sources of heterogeneous users [16]. EDAs also have numerous applications in the field of UAV path planning [20]- [22].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Evolutionary Multi-Objective Estimation of Distribution Algorithm and Its Application to Multi-UAV Path Planning

Yuhang

Zhang

2023

IEEE Access

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This paper concerns the multi-UAV cooperative path planning problem, which is solved by multi-objective optimization and by an adaptive evolutionary multi-objective estimation of distribution algorithm (AEMO-EDA). Since the traditional multi-objective optimization algorithms tend to fall into local optimum solutions when dealing with optimization problems in three dimensions, we suggest an advanced estimation of distribution algorithm. The main idea of this algorithm is to integrate the adaptive deflation of the selection rate, adaptive evolution of the covariance matrix, comprehensive evaluation of individual convergence and diversity, and reference point-based non-dominated ranking. A multi-UAV path planning model involving multi-objective optimization is established, and the designed algorithm is simulated and compared with other three high-dimensional multi-objective optimization algorithms. The results show that the AEMO-EDA proposed in this paper has stronger convergence and wider population distribution diversity in applying to the multi-UAV cooperative path planning model, as well as better global convergence. The algorithm can provide an stable path for each UAV and promote the intelligent operation of the UAV system.

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