Survey of Methodologies for TSP and VRP

Anbuudayasankar, S. P.; Ganesh, K.; Mohapatra, Sanjay

doi:10.1007/978-3-319-05035-5_2

Cited by 21 publications

(10 citation statements)

References 308 publications

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“…The details and benchmark results of leading TSP heuristics such as the Lin–Kernighan (LK) method and the stem-and-cycle (S&C) method can be found in [37]. Metaheuristics have been also widely used to solve TSP and the vehicle routing problem (VRP) [38,39].…”

Section: The Covariance Traveling Salesman Problemmentioning

confidence: 99%

A Revisiting Method Using a Covariance Traveling Salesman Problem Algorithm for Landmark-Based Simultaneous Localization and Mapping

Ryu

2019

Sensors

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This paper presents an efficient revisiting algorithm for landmark-based simultaneous localization and mapping (SLAM). To reduce SLAM uncertainty in terms of a robot’s pose and landmark positions, the method autonomously evaluates valuable landmarks for the data associations in the SLAM algorithm and selects positions to revisit by considering both landmark visibility and sensor measurement uncertainty. The optimal path among the selected positions is obtained by applying the traveling salesman problem (TSP) algorithm. To plan a path that reduces overall uncertainty, the cost matrix associated with the change in covariance between all selected positions of all pairs is applied for the TSP algorithm. From simulations, it is verified that the proposed method efficiently reduces and maintains SLAM uncertainty at the low level compared to the backtracking method.

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Section: The Covariance Traveling Salesman Problemmentioning

confidence: 99%

A Revisiting Method Using a Covariance Traveling Salesman Problem Algorithm for Landmark-Based Simultaneous Localization and Mapping

Ryu

2019

Sensors

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“…Another successful improved version of the artificial bee colony algorithm was proposed for tackling the CVRP with Time dependent Travel times [26]. For further information, the reader may refer to the work of [3].…”

Section: Related Workmentioning

confidence: 99%

A Generalized Island Model Based on Parallel and Cooperating Metaheuristics for Effective Large Capacitated Vehicle Routing Problem Solving

Ammi

Chikhi

2015

CIT

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Capacitated Vehicle Routing Problem (CVRP) is among transportation problems that are of the foremost concerns in logistics. Ensuring an effective product distribution over a large distribution network while reducing the required costs represents the scope of the present work. A synergic and interactive environment of parallel meta-heuristics is developed using a generalized island model to deal with large instances of CVRP. In the proposed model, cooperative meta-heuristics, namely genetic algorithms (GA) and ant colony optimization algorithms (ACO), are organized into archipelagoes. They communicate synchronously, globally and locally by exchanging solutions. In order to handle properly the migration of solutions, either between archipelagoes or between islands within the same archipelago, appropriate selection and replacement policies are adopted. Furthermore, the proposed approach uses other new features including a new binary solution representation and different optimization process (i.e GA, ACO) on each island. To prove the efficiency of the present work, tests over the well-known set of benchmarks, comparative studies and experimental analysis have been conducted.

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“…Some exact approaches [2], such as those based on branch and cut, have been used to solve some large tsp instances. The concorde tool [3] is probably one of the most popular exact tools for the tsp.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, several approximation techniques have also been devised. These include various heuristics and metaheuristics [2]. In the 80s and 90s, a large number of constructive heuristics were defined [7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Diversity-based Evolutionary Algorithm for the Traveling Salesman Problem

Segura

Rionda

Aguirre

et al. 2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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The Traveling Salesman Problem (tsp) is one of the most well-known np-hard combinatorial optimization problems. In order to deal with large tsp instances, several heuristics and metaheuristics have been devised. In this paper, a novel memetic scheme that incorporates a new diversity-based replacement strategy is proposed and applied to the largest instances of the tsplib benchmark. The novelty of our method is that it combines the idea of transforming a single-objective problem into a multi-objective one, by considering diversity as an explicit objective, with the idea of adapting the balance induced between exploration and exploitation to the various optimization stages. In addition, the intensification capabilities of the individual learning method incorporated in the memetic scheme are also adapted by taking into account the stopping criterion. Computational results show the clear superiority of our scheme when compared against state-of-the-art schemes. To our knowledge, our proposal is the first evolutionary scheme that readily solves an instance with more than 30,000 cities to optimality.

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Survey of Methodologies for TSP and VRP

Cited by 21 publications

References 308 publications

A Revisiting Method Using a Covariance Traveling Salesman Problem Algorithm for Landmark-Based Simultaneous Localization and Mapping

A Revisiting Method Using a Covariance Traveling Salesman Problem Algorithm for Landmark-Based Simultaneous Localization and Mapping

A Generalized Island Model Based on Parallel and Cooperating Metaheuristics for Effective Large Capacitated Vehicle Routing Problem Solving

A Novel Diversity-based Evolutionary Algorithm for the Traveling Salesman Problem

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