Enhancing ant colony optimization with genetic algorithm and 3-Opt for multiple drone spraying path planning in precision agriculture
DOI: https://doi.org/10.3846/aviation.2026.25337Abstract
Efficient and environmentally responsible pesticide application is a major challenge in precision agriculture. Excessive pesticide use in conventional farming increases costs, harms the environment, and poses health risks. Recent advancements in unmanned aerial vehicles (UAVs) or drones have enabled targeted spraying, yet optimizing multiple-drone route planning and task allocation remains complex due to dynamic field conditions and limited drone capacity. To address this gap, this study proposes a hybrid optimization approach that integrates Ant Colony Optimization (ACO), Genetic Algorithm (GA), and 3Opt to generate efficient flight routes for multiple sprayer drones based on plant health levels. In this framework, ACO assigns drones to target points, GA automatically tunes key ACO parameters, and 3Opt enhances route efficiency through local optimization. Experimental results show that GA effectively automates the tuning of four key ACO parameters and that drone capacity significantly affects route length. The integration of GA, ACO and 3Opt further reduces total route length, achieving up to 13.6% improvement in efficiency compared to traditional ACO. These findings demonstrate the potential of the proposed method to enhance route efficiency, reduce energy consumption, shorter mission completion time and offers a practical solution for improving the performance and sustainability of multiple-drone spraying operations.
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ant colony optimization, genetic algorithm, 3Opt, agriculture, flight route, multiple drones, optimizationHow to Cite
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Ahuja, A., & Pahwa, A. (2005). Using ant colony optimization for loss minimization in distribution networks. In Proceedings of the 37th Annual North American Power Symposium (pp. 470–474). IEEE. https://doi.org/10.1109/NAPS.2005.1560562
Al-Omeer, M. A., & Ahmed, Z. H. (2019). Comparative study of crossover operators for the MTSP. In 2019 International Conference on Computer and Information Sciences (ICCIS). IEEE. https://doi.org/10.1109/ICCISci.2019.8716483
Asma, A., & Sadok, B. (2019). PSO-based dynamic distributed algorithm for automatic task clustering in a robotic swarm. Procedia Computer Science, 159, 1103–1112. https://doi.org/10.1016/j.procs.2019.09.279
Ayundyahrini, M., Susanto, D. A., Febriansyah, H., Rizanulhaq, F. M., & Aditya, G. H. (2023). Smart farming: Integrated solar water pumping irrigation system in Thailand. Evergreen, 10(1), 553–563. https://doi.org/10.5109/6782161
Bollas, N., Kokinou, E., & Polychronos, V. (2021). Comparison of sentinel-2 and UAV multispectral data for use in precision agriculture: An application from Northern Greece. Drones, 5(2), Article 35. https://doi.org/10.3390/drones5020035
Boukoberine, M. N., Zhou, Z., & Benbouzid, M. (2019). A critical review on unmanned aerial vehicles power supply and energy management: Solutions, strategies, and prospects. Applied Energy, 255, Article 113823. https://doi.org/10.1016/j.apenergy.2019.113823
Cheikhrouhou, O., & Khoufi, I. (2021). A comprehensive survey on the multiple traveling salesman problem: Applications, approaches and taxonomy. Computer Science Review, 40, Article 100369. https://doi.org/10.1016/j.cosrev.2021.100369
Decerle, J., Grunder, O., Hajjam El Hassani, A., & Barakat, O. (2019). A hybrid memetic-ant colony optimization algorithm for the home health care problem with time window, synchronization and working time balancing. Swarm and Evolutionary Computation, 46, 171–183. https://doi.org/10.1016/j.swevo.2019.02.009
Dong, X., Lin, Q., Xu, M., & Cai, Y. (2019). Artificial bee colony algorithm with generating neighbourhood solution for large scale coloured traveling salesman problem. IET Intelligent Transport Systems, 13(10), 1483–1491. https://doi.org/10.1049/iet-its.2018.5359
Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28–39. https://doi.org/10.1109/MCI.2006.329691
Elloumi, W., Baklouti, N., Abraham, A., & Alimi, A. M. (2014). Hybridization of fuzzy PSO and fuzzy ACO applied to TSP. In 13th International Conference on Hybrid Intelligent Systems (HIS 2013) (pp. 105–110). IEEE. https://doi.org/10.1109/HIS.2013.6920464
Farizal, F., Hakim, A. R., Erliza, A., & Setiawan, I. D. (2022). Lubricant products distribution route determination using tabu search algorithm. Evergreen, 9(1), 204–212. https://doi.org/10.5109/4774235
Gite, S., Patil, S., Dharrao, D., Yadav, M., Basak, S., Rajendran, A., & Kotecha, K. (2023). Textual feature extraction using ant colony optimization for hate speech classification. Big Data and Cognitive Computing, 7(1), Article 45. https://doi.org/10.3390/bdcc7010045
Grace, J. L. K., Maneengam, A., Kumar, P. K. V., & Alanya-Beltran, J. (2023). Design and implementation of machine learning modelling through adaptive hybrid swarm optimization techniques for machine management. Evergreen, 10(2), 1120–1126. https://doi.org/10.5109/6793672
Hafeez, A., Husain, M. A., Singh, S. P., Chauhan, A., Khan, M. T., Kumar, N., Chauhan, A., & Soni, S. K. (2022). Implementation of drone technology for farm monitoring & pesticide spraying: A review. Information Processing in Agriculture, 10(2), 192–203. https://doi.org/10.1016/j.inpa.2022.02.002
Hardhienata, M. K. D., Priandana, K., Putra, D. R., Sriatun, M., Wulandari, Buono, A., & Mohamed, R. (2024). Modification of the ant colony optimization algorithm for solving multi-agent task allocation problem in agricultural application. Journal of Advanced Research in Applied Sciences and Engineering Technology, 34(1), 90–105. https://doi.org/10.37934/araset.34.1.90105
Heng, H., & Rahiman, W. (2025). ACO-GA-based optimization to enhance global path planning for autonomous navigation in grid environments. In IEEE Transactions on Evolutionary Computation (pp. 1–1). IEEE. https://doi.org/10.1109/TEVC.2025.3543401
Hiremath, C., Khatri, N., & Jagtap, M. P. (2024). Comparative studies of knapsack, boom, and drone sprayers for weed management in soybean (Glycine max L.). Environmental Research, 240, Article 117480. https://doi.org/10.1016/j.envres.2023.117480
Huang, C., Li, Y., & Yao, X. (2020). A survey of automatic parameter tuning methods for metaheuristics. IEEE Transactions on Evolutionary Computation, 24(2), 201–216. https://doi.org/10.1109/TEVC.2019.2921598
Huerta-Soto, R., Francis, F., Asís-López, M., & Panduro-Ramirez, J. (2023). Implementation of machine learning in supply chain management process for sustainable development by multiple regression analysis approach (MRAA). Evergreen, 10(2), 1113–1119. https://doi.org/10.5109/6793671
Jiang, C., Wan, Z., & Peng, Z. (2020). A new efficient hybrid algorithm for large scale multiple traveling salesman problems. Expert Systems with Applications, 139, Article 112867. https://doi.org/10.1016/j.eswa.2019.112867
Ke, L., Zhang, Q., & Battiti, R. (2013). MOEA/D-ACO: A multiobjective evolutionary algorithm using decomposition and AntColony. IEEE Transactions on Cybernetics, 43(6), 1845–1859. https://doi.org/10.1109/TSMCB.2012.2231860
Lee, M. T., Chen, B. Y., & Lai, Y. C. (2020). A hybrid tabu search and 2-opt path programming for mission route planning of multiple robots under range limitations. Electronics, 9(3), Article 534. https://doi.org/10.3390/electronics9030534
Liu, Y., Mo, R., Tang, F., Fu, Y., & Guo, Y. (2015). Influence of different formulations on chlorpyrifos behavior and risk assessment in bamboo forest of China. Environmental Science and Pollution Research, 22(24), 20245–20254. https://doi.org/10.1007/s11356-015-5272-2
Meesaragandla, S., Jagtap, M. P., Khatri, N., Madan, H., & Vadduri, A. A. (2024). Herbicide spraying and weed identification using drone technology in modern farms: A comprehensive review. Results in Engineering, 21, Article 101870. https://doi.org/10.1016/j.rineng.2024.101870
Mogili, U. R., & Deepak, B. B. V. L. (2018). Review on application of drone systems in precision agriculture. Procedia Computer Science, 133, 502–509. https://doi.org/10.1016/j.procs.2018.07.063
Nayyar, A., Kumar, S., & Nguyen, N. G. (2019). Advances in swarm intelligence and machine learning for optimizing problems in image processing and data analytics (Part 1). Recent Patents on Computer Science, 12(4), 248–249. https://doi.org/10.2174/221327591204190517082230
Neupane, K., & Baysal-Gurel, F. (2021). Automatic identification and monitoring of plant diseases using unmanned aerial vehicles: A review. Remote Sensing, 13(19), Article 3841. https://doi.org/10.3390/rs13193841
Nisrina, N., Kemal, M. I., Akbar, I. A., & Widianti, T. (2022). The effect of genetic algorithm parameters tuning for route optimization in travelling salesman problem through general full factorial design analysis. Evergreen, 9(1), 163–203. https://doi.org/10.5109/4774233
Pandiri, V., & Singh, A. (2018). A swarm intelligence approach for the colored traveling salesman problem. Applied Intelligence, 48(11), 4412–4428. https://doi.org/10.1007/s10489-018-1216-0
Pranaswi, D., Jagtap, M. P., Shinde, G. U., Khatri, N., Shetty, S., & Pare, S. (2024). Analyzing the synergistic impact of UAV-based technology and knapsack sprayer on weed management, yield-contributing traits, and yield in wheat (Triticum aestivum L.) for enhanced agricultural operations. Computers and Electronics in Agriculture, 219, Article 108796. https://doi.org/10.1016/j.compag.2024.108796
Priandana, K., Dewi Hardhienata, M. K., Satya, P. T., Wulandari, & Widhi Surya Atman, M. (2023a). Implementation of modified ant-colony optimization algorithm using crazyflie nano quadcopters. In 2023 International Conference on Informatics Engineering, Science & Technology (INCITEST) (pp. 1–6). IEEE. https://doi.org/10.1109/INCITEST59455.2023.10397008
Priandana, K., Khairi, F., Wulandari, & Dewi Hardhienata, M. K. D. (2023b). Optimizing UAV routes: An implementation and evaluation of ant-colony optimization algorithm on crazyflie quadcopter for solving the traveling salesman problem. In 2025 Frontier in Sustainable Agromaritime and Environmental Development Conference. IPB University. https://conference.ipb.ac.id/index.php/fisaed/article/view/296
Reinecke, M., & Prinsloo, T. (2017). The influence of drone monitoring on crop health and harvest size. In The 2017 1st International Conference on Next Generation Computing Applications (NextComp) (pp. 5–10). IEEE. https://doi.org/10.1109/NEXTCOMP.2017.8016168
Roeva, O., Fidanova, S., & Paprzycki, M. (2015). Population size influence on the genetic and ant algorithms performance in case of cultivation process modeling. In S. Fidanova (Eds), Recent advances in computational optimization. Studies in computational Intelligence (Vol. 580, pp. 107–120). Springer. https://doi.org/10.1007/978-3-319-12631-9_7
Scholtz, M. T., & Bidleman, T. F. (2007). Modelling of the long-term fate of pesticide residues in agricultural soils and their surface exchange with the atmosphere: Part II. Projected long-term fate of pesticide residues. Science of The Total Environment, 377(1), 61–80. https://doi.org/10.1016/j.scitotenv.2007.01.084
Sharma, P., & Dadheech, P. (2023). Modern-age agriculture with artificial intelligence: A review emphasizing crop yield prediction. Evergreen, 10(4), 2570–2582. https://doi.org/10.5109/7160906
Sharma, A., Sharma, Sh., & Gupta, D. (2023). Ant colony optimization based routing strategies for Internet of Things. Evergreen, 10(2), 998–1009. https://doi.org/10.5109/6793654
Shuai, Y., Yunfeng, S., & Kai, Z. (2019). An effective method for solving multiple travelling salesman problem based on NSGA-II. Systems Science & Control Engineering, 7(2), 108–116. https://doi.org/10.1080/21642583.2019.1674220
Tamura, Y., Sakiyama, T., & Arizono, I. (2021). Ant colony optimization using common social information and self-memory. Complexity, 2021. https://doi.org/10.1155/2021/6610670
Townsend, A., Jiya, I. N., Martinson, C., Bessarabov, D., & Gouws, R. (2020). A comprehensive review of energy sources for unmanned aerial vehicles, their shortfalls and opportunities for improvements. Heliyon, 6(11), Article e05285. https://doi.org/10.1016/j.heliyon.2020.e05285
Tudi, M., Ruan, H. D., Wang, L., Lyu, J., Sadler, R., Connell, D., Chu, C., & Phung, D. T. (2021). Agriculture development, pesticide application and its impact on the environment. International Journal of Environmental Research and Public Health, 18(3), Article 1112. https://doi.org/10.3390/ijerph18031112
Vashishtha, J., Kumar, D., & Ratnoo, S. (2013). An evolutionary approach to discover intra- and inter-class exceptions in databases. International Journal of Intelligent Systems Technologies and Applications, 12(3–4), 283–300. https://doi.org/10.1504/IJISTA.2013.056535
Venkatachalam, S., Sundar, K., & Rathinam, S. (2018). A two-stage approach for routing multiple unmanned aerial vehicles with stochastic fuel consumption. Sensors 2018, 18(11), Article 3756. https://doi.org/10.3390/s18113756
Venkatesh, P., & Singh, A. (2015). Two metaheuristic approaches for the multiple traveling salesperson problem. Applied Soft Computing, 26, 74–89. https://doi.org/10.1016/j.asoc.2014.09.029
Wang, Z., Fang, X., Li, H., & Jin, H. (2020). An improved partheno-genetic algorithm with reproduction mechanism for the multiple traveling salesperson problem. IEEE Access, 8, 102607–102615. https://doi.org/10.1109/ACCESS.2020.2998539
Wardana, T. K., Arkeman, Y., Priandana, K., & Kurniawan, F. (2023). Use of plant health level based on random forest algorithm for agricultural drone target points. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(3), 637–645. https://doi.org/10.29207/resti.v7i3.4959
Wei, C., Ji, Z., & Cai, B. (2020). Particle swarm optimization for cooperative multi-robot task allocation: A multi-objective approach. IEEE Robotics and Automation Letters, 5(2), 2530–2537. https://doi.org/10.1109/LRA.2020.2972894
Xu, L., Guo, J., & Zhu, L. (2023). Ant colony optimization algorithm for the hybrid flow shop scheduling problem with integrated consideration of fixture resources. Engineering Proceedings, 45(1), Article 37. https://doi.org/10.3390/engproc2023045037
Younas, I., Kamrani, F., Schulte, C., & Ayani, R. (2011). Optimization of task assignment to collaborating agents. In IEEE Symposium on Computational Intelligence in Scheduling (SCIS) (pp. 17–24). IEEE. https://doi.org/10.1109/SCIS.2011.5976547
Yuan, S., Skinner, B., Huang, S., & Liu, D. (2013). A new crossover approach for solving the multiple travelling salesmen problem using genetic algorithms. European Journal of Operational Research, 228(1), 72–82. https://doi.org/10.1016/j.ejor.2013.01.043
Zhang, Y.-a., Ma, Q., Sakamoto, M., & Furutani, H. (2010). Effects of population size on the performance of genetic algorithms and the role of crossover. Artificial Life and Robotics, 15(2), 239–243. https://doi.org/10.1007/s10015-010-0836-1
Zhang, Y.-a., Sakamoto, M., & Furutani, H. (2008). Effects of population size and mutation rate on results of genetic algorithm. In 2008 4th International Conference on Natural Computation (ICNC) (Vol. 1, pp. 70–75). IEEE. https://doi.org/10.1109/ICNC.2008.345
Zouein, P. P., & Kattan, S. (2022). An improved construction approach using ant colony optimization for solving the dynamic facility layout problem. Journal of the Operational Research Society, 73(7), 1517–1531. https://doi.org/10.1080/01605682.2021.1920345
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