02131nas a2200229   4500000000100000000000100001008004100002260001200043653002700055653001300082653002000095653002400115653002800139100001300167700001600180245007300196856008000269300001000349490000600359520152200365022001401887       2022                            d  c09/202210aGrey Wolf Optimization10aPlanning10aPest Management10aSimulated Annealing10aUnmanned Aerial Vehicle1 aQun Ding1 aXiaolong Xu00aImproved GWO Algorithm for UAV Path Planning on Crop Pest Monitoring  uhttps://www.ijimai.org/journal/sites/default/files/2022-08/ijimai_7_5_4.pdf  a30-390 v73 aAgricultural information monitoring is the monitoring of the agricultural production process, and its task is to monitor the growth process of major crops systematically. When assessing the pest situation of crops in this process, the traditional satellite monitoring method has the defects of poor real-time and high operating cost, whereas the pest monitoring through Unmanned Aerial Vehicles (UAVs) effectively solves the above problems, so this method is widely used. An important key issue involved in monitoring technology is path planning. In this paper, we proposed an Improved Grey Wolf Optimization algorithm, IGWO, to realize the flight path planning of UAV in crop pest monitoring. A map environment model is simulated, and information traversal is performed, then the search of feasible paths for UAV flight is carried out by the Grey Wolf Optimization algorithm (GWO). However, the algorithm search process has the defect of falling into local optimum which leading to path planning failure. To avoid such a situation, we introduced the probabilistic leap mechanism of the Simulated Annealing algorithm (SA). Besides, the convergence factor is modified with an exponential decay mode for improving the convergence rate of the algorithm. Compared with the GWO algorithm, IGWO has the 8.3%, 16.7%, 28.6% and 39.6% lower total cost of path distance on map models with precision of 15, 20, 25 and 30 respectively, and also has better path planning results in contrast to other swarm intelligence algorithms.  a1989-1660