UAV trajectory planning based on an improved sparrow optimization algorithm with multi-strategy integration - Yang, Yu; He, Qing; Yang, Liu (Frontiers in Environmental Science, 2022)
Real-time monitoring of urban high-altitude data is an important issue in the construction and development of smart cities today. However, with the development of modern cities, the monitoring space becomes complicated and narrow because of the different building heights and more no-fly zones, which makes UAV trajectory planning more difficult. In this paper, a Multi-strategy sparrow search algorithm (MSSA) is proposed to solve the UAV trajectory planning problem in a three-dimensional environment. The algorithm aims to minimize the flight distance and maximize the use efficiency of the UAV. Firstly, the improved algorithm uses the reverse learning strategy based on the law of refraction to improve the search range and enhance the optimization performance. Secondly, we introduce the random step size generated by Levy flight into the position update strategy of the participant, the algorithm convergence accuracy and speed improved by the randomness feature. Finally, the algorithm incorporating the Cauchy mutation to improve the scout position, this strategy enhance the ability to jump out of the local optimum of the algorithm. Sixteen benchmark test functions, Wilcoxon rank sum test, and 30 CEC2014 test functions optimization results demonstrate that MSSA has better optimization accuracy, convergence speed and robustness than the other comparison algorithm. In addition, the proposed algorithm is applied to the UAV trajectory planning problem in different complex 3D environments. The results demonstrate that the performance of the MSSA outperforms other algorithm in complex three-dimensional trajectory planning problems.