Combinatorial Optimization: Comparison of Heuristic Algorithms in Travelling Salesman Problem

Halim, Abdul; Ismail, Idris

doi:10.1007/s11831-017-9247-y

Cited by 104 publications

(45 citation statements)

References 20 publications

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“…In this paper, a cluster based approach applied on five benchmark problems of the TSP. Different number of cluster (k = 1,2,4,8) was tried and the results which obtained in different cluster number were compared. Iteration number for k-means algorithm was chosen as 100.…”

Section: Resultsmentioning

confidence: 99%

“…The aim of the problem is to find the shortest possible path which is starts and ends with same city, provided that visiting all cities only once. Although, TSP is generally used as a benchmark problem of the optimization to analyze the performance of the algorithms, it is applied on many real world problems [1][2][3]. It can be seen that there are many researches and works about TSP in literature.…”

Section: Introductionmentioning

confidence: 99%

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A New Approach Based on K-means Clustering and Shuffled Frog Leaping Algorithm to Solve Travelling Salesman Problem

Karakoyun¹

2019

acperpro

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The Travelling Salesman Problem (TSP), which is a combinatorial NP-hard problem, aims to find the shortest possible path while visiting all cities (only once) in a given list and returns to the starting point. In this paper, an approach, which is based on k-means clustering and Shuffled Frog Leaping Algorithm (SFLA), is used to solve the TSP. The proposed approach consists of three parts: separate the cities into k clusters, find the shortest path for each cluster and merge the clusters. Experimental results have shown that the algorithm get better results as the number of cluster increase for problems that have a large number of cities.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Approach Based on K-means Clustering and Shuffled Frog Leaping Algorithm to Solve Travelling Salesman Problem

Karakoyun¹

2019

acperpro

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“…In their recent work, Halim et al have presented an extensive analysis over the performance of different heuristic and metaheuristic algorithms on some TSPLib instances [21]. In addition, Hore et al [22] proposed an improved hybrid VNS algorithm for solving TSP instances.…”

Section: Comparison With Recent Similar Workmentioning

confidence: 99%

“…In addition, Hore et al [22] proposed an improved hybrid VNS algorithm for solving TSP instances. In this section, a comparison between our proposed GVNS schemes (GVNS_1 and GVNS_2) and those algorithms presented on papers [21,22] is performed. Table 18 shows the comparison between our GVNS_1 and GVNS_2, within the Best Improvement search strategy and a time limit of 120 s, with all metaheuristic solution approaches presented in the work of Halim et al [21].…”

Section: Comparison With Recent Similar Workmentioning

confidence: 99%

A Performance Study of the Impact of Different Perturbation Methods on the Efficiency of GVNS for Solving TSP

Papalitsas

Karakostas

Andronikos

2019

ASI

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The purpose of this paper is to assess how three shaking procedures affect the performanceof a metaheuristic GVNS algorithm. The first shaking procedure is generally known in the literatureas intensified shaking method. The second is a quantum-inspired perturbation method, and thethird is a shuffle method. The GVNS schemes are evaluated using a search strategy for both Firstand Best improvement and a time limit of one and two minutes. The formed GVNS schemes wereapplied on Traveling Salesman Problem (sTSP, nTSP) benchmark instances from the well-knownTSPLib. To examine the potential advantage of any of the three metaheuristic schemes, extensivestatistical analysis was performed on the reported results. The experimental data shows that for aTSPinstances the first two methods perform roughly equivalently and, in any case, much better thanthe shuffle approach. In addition, the first method performs better than the other two when usingthe First Improvement strategy, while the second method gives results quite similar to the third.However, no significant deviations were observed when different methods of perturbation were usedfor Symmetric TSP instances (sTSP, nTSP).

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“…Meta-heuristic algorithms have been used successfully by manufacturers to reduce transport costs and enhance the business. Algorithms of this type generally find a satisfactory solution to problems, which are inherently nondeterministic polynomial-time hard (NP-hard), in an acceptably short time [1,2]. Meta-heuristic algorithms have also been applied to Traveling Salesman Problem (TSP), a classic NP-hard problem that tries to find the shortest tour that a salesman can take to visit all of his customer sites in a single tour, with starting and end points at the same city.…”

Section: Introductionmentioning

confidence: 99%

Modified Ant Colony Optimization Algorithm for Multiple-vehicle Traveling Salesman Problems

Oonsrikaw¹,

Thammano²

2018

IJNDC

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Combinatorial Optimization: Comparison of Heuristic Algorithms in Travelling Salesman Problem

Cited by 104 publications

References 20 publications

A New Approach Based on K-means Clustering and Shuffled Frog Leaping Algorithm to Solve Travelling Salesman Problem

A New Approach Based on K-means Clustering and Shuffled Frog Leaping Algorithm to Solve Travelling Salesman Problem

A Performance Study of the Impact of Different Perturbation Methods on the Efficiency of GVNS for Solving TSP

Modified Ant Colony Optimization Algorithm for Multiple-vehicle Traveling Salesman Problems

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