Optimization of UAV Cooperative Path Planning Mathematical Model Based on Personalized Multigroup Sparrow Search Algorithm in Complex Environment

Abstract

Sparrow search algorithm has the problem of redundancy of convergence speed due to its fast convergence speed, and it is easy to fall into local optimum in multimodal environment. To solve the above problem, this paper presents a personalized multipopulation sparrow search algorithm (MPSSA). By introducing multiple population mechanisms to reduce the probability of falling into the local optimum due to single-population search, by using a personalized subpopulation strategy to improve the personalized differences of subpopulations and balance the exploratory ability of algorithm development, then by using weighted center-of-gravity communication strategy to improve the quality of communication between populations, and finally by using dimension by dimension dynamic reverse learning to improve the accuracy of search. The superiority of MPSSA is validated by comparing the benchmark function and CEC2017. Finally, the algorithm solves the problem of poor quality due to the dimension increase of the UAV cooperative track. MPSSA helps the UAV to quickly plan a better and stable track group to ensure the UAV to complete the cooperative task safely and stably.