Neighborhood composition A -Parallelization of local search algorithms

Yuichi Handa, Hirotaka Ono, Kunihiko Sadakane, Masafumi Yamashita

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

Abstract

To practically solve NP-hard combinatorial optimization problems, local search algorithms and their parallel implementations on PVM or MPI have been frequently discussed. Since a huge number of neighbors may be examined to discover a locally optimal neighbor in each of local search calls, many of parallelization schemes, excluding socalled the multi-start parallel scheme, try to extract parallelism from a local search by distributing the examinations of neighbors to processors. However, hi straightforward implementations, when the next local search starts, all the processors will be assigned to the neighbors of the latest solution, and the results of all (but one) examinations in the previous local search are thus discarded in vain, despite that they would contain useful information on further search. This paper explores the possibility of extracting information even from unsuccessful neighbor examinations in a systematic way to boost parallel local search algorithms. Our key concept is neighborhood composition. We demonstrate how this idea improves parallel implementations on PVM, by taking as examples well-known local search algorithms for the Traveling Salesman Problem.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsDieter Kranzlmuller, Peter Kacsuk, Jack Dongarra
PublisherSpringer Verlag
Pages155-163
Number of pages9
ISBN (Print)3540231633
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3241
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Handa, Y., Ono, H., Sadakane, K., & Yamashita, M. (2004). Neighborhood composition A -Parallelization of local search algorithms. In D. Kranzlmuller, P. Kacsuk, & J. Dongarra (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 155-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3241). Springer Verlag. https://doi.org/10.1007/978-3-540-30218-6_26