Assessing Simulated Annealing with Variable Neighborhoods

Eduardo Lalla-Ruiz; Leonard Heilig; Stefan Voß

doi:10.1007/978-3-030-38629-0_24

Assessing Simulated Annealing with Variable Neighborhoods

Eduardo Lalla-Ruiz^*, Leonard Heilig, Stefan Voß

^*Corresponding author for this work

Industrial Engineering & Business Information Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

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Abstract

Simulated annealing (SA) is a well-known metaheuristic commonly used to solve a great variety of -hard problems such as the quadratic assignment problem (QAP). As commonly known, the choice and size of neighborhoods can have a considerable impact on the performance of SA. In this work, we investigate and propose a SA variant that considers variable neighborhood structures driven by the state of the search. In the computational experiments, we assess the contribution of this SA variant in comparison with the state-of-the-art SA for the QAP applied to printed circuit boards and conclude that our approach is able to report better solutions by means of short computational times.

Original language	English
Title of host publication	Learning and Intelligent Optimization
Subtitle of host publication	13th International Conference, LION 13, Chania, Crete, Greece, May 27–31, 2019, Revised Selected Papers
Editors	Nikolaos F. Matsatsinis, Yannis Marinakis, Panos Pardalos
Place of Publication	Cham
Publisher	Springer
Pages	298-303
Number of pages	6
ISBN (Electronic)	Assessing Simulated Annealing with Variable
ISBN (Print)	978-3-030-38628-3
DOIs	https://doi.org/10.1007/978-3-030-38629-0_24
Publication status	Published - 22 Jan 2020
Event	13th International Conference on Learning and Intelligent Optimization, LION 2019 - Technical University of Crete, Chania, Greece Duration: 27 May 2019 → 31 May 2019 Conference number: 13

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11968
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Learning and Intelligent Optimization, LION 2019
Abbreviated title	LION 2019
Country/Territory	Greece
City	Chania
Period	27/05/19 → 31/05/19

Keywords

Metaheuristics
Optimization
Quadratic assignment problem
Heuristics
Variable neighbourhood search
Artificial Intelligence
22/2 OA procedure

Access to Document

10.1007/978-3-030-38629-0_24

Lalla-Ruiz2020_Chapter_AssessingSimulatedAnnealingWitFinal published version, 204 KBLicence: Taverne

Cite this

Lalla-Ruiz, E., Heilig, L., & Voß, S. (2020). Assessing Simulated Annealing with Variable Neighborhoods. In N. F. Matsatsinis, Y. Marinakis, & P. Pardalos (Eds.), Learning and Intelligent Optimization: 13th International Conference, LION 13, Chania, Crete, Greece, May 27–31, 2019, Revised Selected Papers (pp. 298-303). (Lecture Notes in Computer Science; Vol. 11968). Springer. https://doi.org/10.1007/978-3-030-38629-0_24

Lalla-Ruiz, Eduardo ; Heilig, Leonard ; Voß, Stefan. / Assessing Simulated Annealing with Variable Neighborhoods. Learning and Intelligent Optimization: 13th International Conference, LION 13, Chania, Crete, Greece, May 27–31, 2019, Revised Selected Papers. editor / Nikolaos F. Matsatsinis ; Yannis Marinakis ; Panos Pardalos. Cham : Springer, 2020. pp. 298-303 (Lecture Notes in Computer Science).

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title = "Assessing Simulated Annealing with Variable Neighborhoods",

abstract = "Simulated annealing (SA) is a well-known metaheuristic commonly used to solve a great variety of -hard problems such as the quadratic assignment problem (QAP). As commonly known, the choice and size of neighborhoods can have a considerable impact on the performance of SA. In this work, we investigate and propose a SA variant that considers variable neighborhood structures driven by the state of the search. In the computational experiments, we assess the contribution of this SA variant in comparison with the state-of-the-art SA for the QAP applied to printed circuit boards and conclude that our approach is able to report better solutions by means of short computational times.",

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editor = "Matsatsinis, {Nikolaos F.} and Yannis Marinakis and Panos Pardalos",

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Lalla-Ruiz, E, Heilig, L & Voß, S 2020, Assessing Simulated Annealing with Variable Neighborhoods. in NF Matsatsinis, Y Marinakis & P Pardalos (eds), Learning and Intelligent Optimization: 13th International Conference, LION 13, Chania, Crete, Greece, May 27–31, 2019, Revised Selected Papers. Lecture Notes in Computer Science, vol. 11968, Springer, Cham, pp. 298-303, 13th International Conference on Learning and Intelligent Optimization, LION 2019, Chania, Greece, 27/05/19. https://doi.org/10.1007/978-3-030-38629-0_24

Assessing Simulated Annealing with Variable Neighborhoods. / Lalla-Ruiz, Eduardo; Heilig, Leonard; Voß, Stefan.
Learning and Intelligent Optimization: 13th International Conference, LION 13, Chania, Crete, Greece, May 27–31, 2019, Revised Selected Papers. ed. / Nikolaos F. Matsatsinis; Yannis Marinakis; Panos Pardalos. Cham: Springer, 2020. p. 298-303 (Lecture Notes in Computer Science; Vol. 11968).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Assessing Simulated Annealing with Variable Neighborhoods

AU - Lalla-Ruiz, Eduardo

AU - Heilig, Leonard

AU - Voß, Stefan

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N2 - Simulated annealing (SA) is a well-known metaheuristic commonly used to solve a great variety of -hard problems such as the quadratic assignment problem (QAP). As commonly known, the choice and size of neighborhoods can have a considerable impact on the performance of SA. In this work, we investigate and propose a SA variant that considers variable neighborhood structures driven by the state of the search. In the computational experiments, we assess the contribution of this SA variant in comparison with the state-of-the-art SA for the QAP applied to printed circuit boards and conclude that our approach is able to report better solutions by means of short computational times.

AB - Simulated annealing (SA) is a well-known metaheuristic commonly used to solve a great variety of -hard problems such as the quadratic assignment problem (QAP). As commonly known, the choice and size of neighborhoods can have a considerable impact on the performance of SA. In this work, we investigate and propose a SA variant that considers variable neighborhood structures driven by the state of the search. In the computational experiments, we assess the contribution of this SA variant in comparison with the state-of-the-art SA for the QAP applied to printed circuit boards and conclude that our approach is able to report better solutions by means of short computational times.

KW - Metaheuristics

KW - Optimization

KW - Quadratic assignment problem

KW - Heuristics

KW - Variable neighbourhood search

KW - Artificial Intelligence

KW - 22/2 OA procedure

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Lalla-Ruiz E, Heilig L, Voß S. Assessing Simulated Annealing with Variable Neighborhoods. In Matsatsinis NF, Marinakis Y, Pardalos P, editors, Learning and Intelligent Optimization: 13th International Conference, LION 13, Chania, Crete, Greece, May 27–31, 2019, Revised Selected Papers. Cham: Springer. 2020. p. 298-303. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-38629-0_24

Assessing Simulated Annealing with Variable Neighborhoods

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