A comparison study of genetic codings for the traveling salesman problem

Tamaki, Hisashi; Kita, Hidetoshi; Shimizu, Naofumi; Maekawa, Kazuyoshi; Nishikawa, Yoshikazu

doi:10.1109/icec.1994.350052

Cited by 14 publications

(5 citation statements)

References 2 publications

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“…Many type of GA method for solving TSP has been proposed [8], [9]. In GA method, various techniques like the technique for the tuning of the best parameter [10], [11] etc.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

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Selfish-gene Tolerant Generic Algorithms to solve large-scale constraint TSPs

Sakurai

Onoyama

Kubota

et al. 2006

2006 IEEE International Conference on Information Reuse &Amp;amp; Integration

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Large-scale distribution network simulation applicable to supply-chain management requires to solve hundreds of time-constraint large-scale (max 2000 cities) Traveling Salesman Problems (TSP) within interactive response time, with practicable optimality. To meet this requirement, a selfish-gene tolerant type GA is proposed. Here, each gene of an individual satisfies only its constraints selfishly, disregarding the constraints of other genes in the same individual. Further, to some extent, even individuals that violate constraints can survive over generations and are given the chance of improvement. Our experiment proves that this method provides expert-level solutions for time constraint largescale TSPs within a few seconds.

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“…Many type of GA method for solving TSP has been proposed [8], [9]. In GA method, various techniques like the technique for the tuning of the best parameter [10], [11] etc.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Genetic algorithm (GA) [7] is the other method to solve it. Many types of GA methods for solving TSP have been also proposed [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Selfish-gene Tolerant Generic Algorithms to solve large-scale constraint TSPs

Sakurai

Onoyama

Kubota

et al. 2006

2006 IEEE International Conference on Information Reuse &Amp;amp; Integration

View full text Add to dashboard Cite

show abstract

“…Many genetic algorithms employ a local search heuristic to improve population tours. It has been shown in [34] to be an effective way to improve solution quality. A common local search heuristic for the TSP is the -opt exchange heuristic.…”

Section: Local Search Operatorsmentioning

confidence: 99%

A Comparison of Algorithms for Finding an Efficient Theme Park Tour

Bouzarth

Forrester

Hutson

et al. 2018

Journal of Applied Mathematics

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The problem of efficiently touring a theme park so as to minimize the amount of time spent in queues is an instance of the Traveling Salesman Problem with Time-Dependent Service Times (TSP-TS). In this paper, we present a mixed-integer linear programming formulation of the TSP-TS and describe a branch-and-cut algorithm based on this model. In addition, we develop a lower bound for the TSP-TS and describe two metaheuristic approaches for obtaining good quality solutions: a genetic algorithm and a tabu search algorithm. Using test instances motivated by actual theme park data, we conduct a computational study to compare the effectiveness of our algorithms.

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“…As for the Genetic Algorithms (GA) to efficiently solve TSP, various techniques are proposed. GA applied solving methods using the edges assembly crossover (EAX) (Nagata & Kobayashi, 1999) and the distance-preserving crossover (DPX) (Whiteley & Starkweather, 1989) could get highly optimized solutions in case of very-large-scale TSPs (with 1000-10000 cities) (Tamaki et al, 1994;Baraglia et al, 2001;Tsai et al, 2004;Nguyen et al, 2007). These crossover methods examine characteristics of parent's tour edge to strictly inherit to children.…”

Section: Applicability Of the Proposed Solving Methodsmentioning

confidence: 99%