Modification of the ant colony optimization algorithm for solving multi-agent task allocation problem in agricultural application
This paper considers the problem of task allocation where the goal is to find a coalition of UAVs (agents) to complete on-farm agricultural tasks. In this study, Ant Colony Optimization (ACO) algorithm is employed to find the best coalition of agents. The performance of the basic ACO algorithm for s...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Semarak Ilmu Publishing
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/105670/1/ARASETV34_N1_P90_105.pdf http://psasir.upm.edu.my/id/eprint/105670/ https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/3095 |
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| Summary: | This paper considers the problem of task allocation where the goal is to find a coalition of UAVs (agents) to complete on-farm agricultural tasks. In this study, Ant Colony Optimization (ACO) algorithm is employed to find the best coalition of agents. The performance of the basic ACO algorithm for solving task allocation is improved by modifying the efficiency factor. In the proposed algorithm, the efficiency factor is defined as a function that relates not only to the capability of the agents and the distance between the agents, but also to the distance between the agents and the target. To solve the task allocation problem, the capability list of the agents was also adjusted using common UAV capabilities in agricultural application. Simulation results showed that the proposed ACO algorithm with the modified efficiency factor improved the performance of basic ACO algorithm for solving task allocation problem in terms of the average total travel cost for each agent. The optimum number of ants and agents in the proposed algorithm was also analysed for robust performance. Simulation results revealed that the addition of the numbers of agents and ants increases the average efficiency of the algorithm. In this study, we have also added a function to calculate the system capability utilization. By employing such a function, simulation results show that the total resource used by the agents and total communication cost can be optimized. In addition, a simple experiment using five ground robots with a centralized control was also carried out as a proof of concept for the proposed algorithm. © 2024, Semarak Ilmu Publishing. All rights reserved. |
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