Fixed set search applied to the traveling salesman problem

Raka Jovanovic*, Milan Tuba, Stefan Voß

*Corresponding author for this work

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

24 Citations (Scopus)

Abstract

In this paper we present a new population based metaheuristic called the fixed set search (FSS). The proposed approach represents a method of adding a learning mechanism to the greedy randomized adaptive search procedure (GRASP). The basic concept of FSS is to avoid focusing on specific high quality solutions but on parts or elements that such solutions have. This is done through fixing a set of elements that exist in such solutions and dedicating computational effort to finding near optimal solutions for the underlying subproblem. The simplicity of implementing the proposed method is illustrated on the traveling salesman problem. Our computational experiments show that the FSS manages to find significantly better solutions than the GRASP it is based on, the dynamic convexized method and the ant colony optimization combined with a local search.

Original languageEnglish
Title of host publicationHybrid Metaheuristics - 11th International Workshop, HM 2019, Proceedings
EditorsMaria J. Blesa Aguilera, Christian Blum, Pedro Pinacho-Davidson, Julio Godoy del Campo, Haroldo Gambini Santos
PublisherSpringer Verlag
Pages63-77
Number of pages15
ISBN (Print)9783030059828
DOIs
Publication statusPublished - 2019
Event11th International Workshop on Hybrid Metaheuristics, HM 2019 - Concepción, Chile
Duration: 16 Jan 201918 Jan 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11299 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Workshop on Hybrid Metaheuristics, HM 2019
Country/TerritoryChile
CityConcepción
Period16/01/1918/01/19

Keywords

  • GRASP
  • Metaheuristic
  • Traveling salesman problem

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