2015 7th International Conference on Recent Advances in Space Technologies (RAST) 2015
DOI: 10.1109/rast.2015.7208428
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Routing unmanned aerial vehicles as adapting to capacitated vehicle routing problem with genetic algorithms

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Cited by 14 publications
(5 citation statements)
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“…This current paper considers the type of task, the UAV load constraints, multiple UAVs, multi-task targets and multi-task types, features that are similar to previous studies in the literature [21,53,54]. Unlike these studies, this paper builds a model from two aspects of the rewards and costs of performing tasks.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…This current paper considers the type of task, the UAV load constraints, multiple UAVs, multi-task targets and multi-task types, features that are similar to previous studies in the literature [21,53,54]. Unlike these studies, this paper builds a model from two aspects of the rewards and costs of performing tasks.…”
Section: Related Workmentioning
confidence: 99%
“…At present, many general mathematical models have been proposed for multi-UAV task allocation at home and abroad. The commonly used task-allocation models include the multiple traveling salesman problem (MTSP) [16] model, mixed-integer linear programming (MILP) model [17], the vehicle routing problem (VRP) [18] model, the network flow optimization (DNFO) [19] model, the cooperative multiple task assignment problem (CMTAP) [20] model, and the capacitated vehicle routing problem (CVRP) [21] model. Based on these general multi-UAV task-allocation models, many scholars have established targeted task-planning models.…”
Section: Introductionmentioning
confidence: 99%
“…Russel and Lamont (2005) solved drone routing by applying genetic vehicle representation and produced competitive or superior results to the benchmark VRP datasets. Zorlu (2015) solved a capacitated VRP (CVRP) for drones. The research randomly generated the route population in the initialization state considering the capacity constraints of two vehicles/drones and 20 locations.…”
Section: Related Researchmentioning
confidence: 99%
“…First, this research considered a specific vehicle assignment case to a location, regarding different drone capabilities. Similar drone-based delivery dealing with genetic algorithm (GA) can be seen in Russel and Lamont (2005), Zorlu (2015); and Wen et al (2016). However, their research considered a single drone profile.…”
Section: Introductionmentioning
confidence: 99%
“…These include the PSO (particle swarm optimization) algorithm [17]. the SA (simulated annealing) algorithm [18], the TS (Tabu search) algorithm [19], the ACO (ant colony optimization) algorithm [20], the GA (genetic algorithm) algorithm [21], etc. The PSO algorithm can make full use of group experience to adjust the running results of the algorithm and has a fast convergence speed, but its local optimization ability is poor.…”
Section: Introductionmentioning
confidence: 99%