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 15 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%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Swarm Intelligence in Action: Particle Swarm Optimization and Rendezvous Algorithms for Swarm Robotics

Ganduri,

Pathri

2024

Journal of Field Robotics

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Swarm technology is evolving quickly in the new era in the domains of autonomous underwater vehicles (AUVs), unmanned aerial vehicles (UAVs), and unmanned ground vehicles (UGVs). Swarm technology takes its cues from nature, particularly from the behaviors of ants, bees, schools of fish, and birds. The rapid growth of swarm technologies can be attributed to this organic inspiration. It is utilized in a variety of applications, such as pick‐and‐place robotics and drone swarms for laser shows. To mimic the behavior of swarm robots or microbots, this research work explores the use of swarm algorithms, namely particle swarm optimization (PSO) and rendezvous algorithms (RA). The emphasis will be on applying these algorithms to situations that require swarm robots to navigate around obstacles while planning their paths and forming patterns. This study shows how successful these algorithms are by using MATLAB as a useful tool. The outcomes demonstrate that the algorithms are capable of producing intricate patterns and facilitating effective path planning for swarms of robots. The robots' ability to avoid obstacles and travel around them at a high speed is noteworthy. This study covers a wide range of industries, such as space exploration, military/defense applications, storage, surveillance, and search and rescue. The application of swarm technology demonstrates how it may transform practical situations and provides information about how flexible and efficient swarm algorithms are in a variety of settings. By offering useful insights for researchers, engineers, and practitioners in utilizing the combined potential of swarm intelligence (SI) for a variety of applications, this investigation advances the field of swarm robotics.

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Hybrid Genetic Algorithm Based on Machine Learning and Fitness Function Estimation Proposal for Ground Vehicle and Drone Cooperative Delivery Problem

Özer

2024

Intelligent Systems Reference Library

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

Cited by 15 publications

References 23 publications

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

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

Swarm Intelligence in Action: Particle Swarm Optimization and Rendezvous Algorithms for Swarm Robotics

Hybrid Genetic Algorithm Based on Machine Learning and Fitness Function Estimation Proposal for Ground Vehicle and Drone Cooperative Delivery Problem

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