Assessing Simulated Annealing with Variable Neighborhoods

Eduardo Lalla-Ruiz*, Leonard Heilig, Stefan Voß

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

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 languageEnglish
Title of host publicationLearning and Intelligent Optimization
Subtitle of host publication13th International Conference, LION 13, Chania, Crete, Greece, May 27–31, 2019, Revised Selected Papers
EditorsNikolaos F. Matsatsinis, Yannis Marinakis, Panos Pardalos
Place of PublicationCham
PublisherSpringer
Pages298-303
Number of pages6
ISBN (Electronic)Assessing Simulated Annealing with Variable
ISBN (Print)978-3-030-38628-3
DOIs
Publication statusPublished - 1 Jan 2020
Event13th International Conference on Learning and Intelligent Optimization, LION 2019 - Technical University of Crete, Chania, Greece
Duration: 27 May 201931 May 2019
Conference number: 13

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11968
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Learning and Intelligent Optimization, LION 2019
Abbreviated titleLION 2019
CountryGreece
CityChania
Period27/05/1931/05/19

Keywords

  • Metaheuristics
  • Optimization
  • Quadratic assignment problem
  • Heuristics
  • Variable neighbourhood search
  • Artificial Intelligence

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