Heterogenous vehicle routing: comparing parameter tuning using genetic algorithm and bayesian optimization

Ramasamy, Subramanian; Mondal, Md Safwan; Reddinger, Jean-Paul; Dotterweich, James; Humann, James; Childers, Marshal A.; Bhounsule, Pranav A.

doi:10.1109/icuas54217.2022.9836044

Cited by 12 publications

(11 citation statements)

References 23 publications

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“…The authors developed a two-level optimization framework [17] that uses Genetic Algorithm and Bayesian optimization and is described in Sec. 2-A.…”

Section: Methodsmentioning

confidence: 99%

“…The Fig. 2 shows the conventional two-level optimization [17]. The outer level block shown in blue are the heuristics to choose a UGV route.…”

Section: A Conventional Two-level Optimizationmentioning

confidence: 99%

“…The mathematical details are note included here because of space constraint, but can be found in [17].…”

Section: Optimizing Uav Route (Inner-level)mentioning

confidence: 99%

“…Second, they formulated a mixed-integer linear program and solved for the path of both the UAV and UGV. The work in [17] also allows the UGV to move freely only on prescribed paths and is also followed in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…Although such algorithms help us to achieve globally optimal solutions, they come at a cost of significant computational time. This was seen from the previous work by the authors [17] where Genetic Algorithm (GA) and Bayesian Optimization (BO) are implemented for parameter tuning to obtain the UGV route. The results from that work show that particularly in case of GA, higher computational time of about 180 minutes was needed to solve that problem.…”

Section: Introductionmentioning

confidence: 99%

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Solving Vehicle Routing Problem for unmanned heterogeneous vehicle systems using Asynchronous Multi-Agent Architecture (A-teams)

Subramanian¹,

Mondal²,

Bhounsule³

2023

Preprint

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Fast moving but power hungry unmanned aerial vehicles (UAVs) can recharge on slow-moving unmanned ground vehicles (UGVs) to survey large areas in an effective and efficient manner. In order to solve this computationally challenging problem in a reasonable time, we created a two-level optimization heuristics. At the outer level, the UGV route is parameterized by few free parameters and at the inner level, the UAV route is solved by formulating and solving a vehicle routing problem with capacity constraints, time windows, and dropped visits. The UGV free parameters need to be optimized judiciously in order to create high quality solutions. We explore two methods for tuning the free UGV parameters: (1) a genetic algorithm, and (2) Asynchronous Multi-agent architecture (Ateams). The A-teams uses multiple agents to create, improve, and destroy solutions. The parallel asynchronous architecture enables A-teams to quickly optimize the parameters. Our results on test cases show that the A-teams produces similar solutions as genetic algorithm but with a speed up of 2-3 times.

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“…The authors developed a two-level optimization framework [17] that uses Genetic Algorithm and Bayesian optimization and is described in Sec. 2-A.…”

Section: Methodsmentioning

confidence: 99%

“…The Fig. 2 shows the conventional two-level optimization [17]. The outer level block shown in blue are the heuristics to choose a UGV route.…”

Section: A Conventional Two-level Optimizationmentioning

confidence: 99%

“…The mathematical details are note included here because of space constraint, but can be found in [17].…”

Section: Optimizing Uav Route (Inner-level)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%