Adaptive cylinder vector particle swarm optimization with differential evolution for UAV path planning. (May 2023)

• Record Type: Journal Article
• Authors: Huang, Chen; Zhou, Xiangbing; Ran, Xiaojuan; Wang, Jiamiao; Chen, Huayue; Deng, Wu
• Is Part Of: Engineering applications of artificial intelligence. Volume 121(2023)
• Publication Date: 2023-05
• Subjects: Particle swarm optimization -- Path planning -- Cylinder vector -- Adaption parameter strategy -- UAV; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2023.105942
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Particle swarm optimization (PSO) algorithm has a potential to solve route planning problem for unmanned aerial vehicle (UAV). However, the traditional PSO algorithm is easy to fall into local optimum under the complicated environments with multiple threats. In order to improve the performance in different complicated environments, a novel and effective PSO algorithm with adaptive adjustment of the parameters, cylinder vector and different evolution operator, named ACVDEPSO, is proposed and demonstrated to be effective for route planning problem for UAV. In the proposed ACVDEPSO, the velocity of the particle is converted to its cylinder vector for the convenience of the path search. It is worth highlighting that the parameters of ACVDEPSO algorithm are automatically chosen by the time and the fitness values of the particles. Furthermore, a challenger based on differential evolution operator is introduced to reduce the probability of falling into local optimum and accelerate the algorithm convergence speed. The simulation experiments have been conducted in real digital elevation model (DEM) maps to test the performance of the ACVDEPSO. The experiment results validate that the optimization performance of the ACVDEPSO outperforms the other comparison methods, which can efficiently generate a higher quality path for UAV under the complicated 3D environments. Highlights: A novel cylinder vector-based PSO algorithm with wave and Sigmoid functions is proposed. An efficient path planning method based on cylinder vector-based PSO is proposed. A new adaptive strategy of the inertia weight based on wave function is developed. A new control scheme of acceleration coefficients based on sigmoid function is designed.