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Open AccessArticle

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

1
Department of Informatics, Ionian University, 7 Tsirigoti Square, 49100 Corfu, Greece
2
Department of Applied Informatics, University of Macedonia, GR-546 36 Thessaloniki, Greece
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Syst. Innov. 2019, 2(4), 31; https://doi.org/10.3390/asi2040031
Received: 18 August 2019 / Revised: 12 September 2019 / Accepted: 18 September 2019 / Published: 20 September 2019
(This article belongs to the Special Issue Fuzzy Decision Making and Soft Computing Applications)
The purpose of this paper is to assess how three shaking procedures affect the performance of a metaheuristic GVNS algorithm. The first shaking procedure is generally known in the literature as intensified shaking method. The second is a quantum-inspired perturbation method, and the third is a shuffle method. The GVNS schemes are evaluated using a search strategy for both First and Best improvement and a time limit of one and two minutes. The formed GVNS schemes were applied on Traveling Salesman Problem (sTSP, nTSP) benchmark instances from the well-known TSPLib. To examine the potential advantage of any of the three metaheuristic schemes, extensive statistical analysis was performed on the reported results. The experimental data shows that for aTSP instances the first two methods perform roughly equivalently and, in any case, much better than the shuffle approach. In addition, the first method performs better than the other two when using the 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 used for Symmetric TSP instances (sTSP, nTSP). View Full-Text
Keywords: variable neighborhood search; experimental comparison; statistical analysis; traveling salesman problem; soft computing variable neighborhood search; experimental comparison; statistical analysis; traveling salesman problem; soft computing
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Papalitsas, C.; Karakostas, P.; Andronikos, T. A Performance Study of the Impact of Different Perturbation Methods on the Efficiency of GVNS for Solving TSP. Appl. Syst. Innov. 2019, 2, 31.

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