Optimization of the Unmanned Aerial Vehicle Route Based on Chimpanzee Leader Election Optimization
Ferry Wahyu Wibowo, Wihayati · 2024
Pest and disease control often requires large amounts of pesticides, which can damage the environment and human health. In addition, unplanned control can result in inefficient use of chemicals. Unmanned aerial vehicles (UAVs) with spraying systems can highly spray pesticides or nutrients. UAV or drone technology makes it possible to target areas affected by pests or diseases precisely, reducing the amount of pesticides needed and minimizing negative environmental impacts. Farmers can increase productivity, reduce environmental impact, and better manage resources by leveraging drone technology. This paper aims to optimize the drone spray route in rice fields using the Chimpanzee Leader Election Optimization (CLEO) algorithm. The method to improve performance efficiency was to simulate the drone flight route by placing several obstacles the drone should not pass through. The optimization results have used several flight reference points, namely 2,3, and 4. This experiment was carried out 30 times for each reference point number to obtain statistical results as a performance analysis of the modeling. The best results have shown that the placement of two reference points for the drone flight route has a superior value, with a mean of 9.08 and a standard deviation of 0.91.