Three-Dimensional Multi-Mission Planning of UAV Using Improved Ant Colony Optimization Algorithm Based on the Finite-Time Constraints
, Xin Zheng- DOI
- 10.2991/ijcis.d.201021.001How to use a DOI?
- Keywords
- Improved ant colony optimization; Variable dimension vector coefficient; Three-dimensional missions planning; Time adaptive factor; Finite-time constraints
- Abstract
An improved ant colony optimization (IACO) is proposed to solve three-dimensional multi-task programming under finite-time constraints. The algorithm introduces the artificial preemptive coefficient matrix into the transfer probability formula, which makes results convergence and also reduces the convergence time of the algorithm. Following the principle that there is no pheromone on the path where the ants are just beginning to forage in reality, the pheromone is initially zero, and the ant's self-guided ability is fully utilized, which enhances the random exploration ability of the ant algorithm for the entire solution space. By introducing the variable dimension vector coefficient and the time adaptive factor of transfer probability, the search probability in the inferior solution set is reduced and the convergence speed of the algorithm is increased. Finally, through the simulation on the random map and comparison with the traditional ant colony optimization, particle swarm optimization, and tabu search algorithm, the superiority of the IACO proposed in this paper is demonstrated.
- Copyright
- © 2021 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Weiheng Liu AU - Xin Zheng PY - 2020 DA - 2020/10/29 TI - Three-Dimensional Multi-Mission Planning of UAV Using Improved Ant Colony Optimization Algorithm Based on the Finite-Time Constraints JO - International Journal of Computational Intelligence Systems SP - 79 EP - 87 VL - 14 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.201021.001 DO - 10.2991/ijcis.d.201021.001 ID - Liu2020 ER -