SMMH - A parallel heuristic for combinatorial optimization problems

Guilherme Domingues, Yoshiyuki Morie, Feng Long Gu, Takeshi Nanri, Kazuaki Murakami

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

Abstract

The process of finding one or more optimal solutions for answering combinatorial optimization problems bases itself on the use of algorithms instances. Those instances usually have to explore a very large search spaces. Heuristics search focusing on the use of High-Order Hopfield neural networks is a largely deployed technique for very large search space. It can be established a very powerful analogy towards the dynamics evolution of a physics spin-glass system while minimizing its own energy and the energy function of the network. This paper presents a new approach for solving combinatorial optimization problems through parallel simulations, based on a High-Order Hopfield neural network using MPI specification.

Original languageEnglish
Title of host publicationComputation in Modern Science and Engineering - Proceedings of the International Conference on Computational Methods in Science and Engineering 2007 (ICCMSE 2007)
Pages1195-1198
Number of pages4
Edition2
DOIs
StatePublished - 2007
Externally publishedYes
EventInternational Conference on Computational Methods in Science and Engineering 2007, ICCMSE 2007 - Corfu, Greece
Duration: 25 Sep 200730 Sep 2007

Publication series

NameAIP Conference Proceedings
Number2
Volume963
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

ConferenceInternational Conference on Computational Methods in Science and Engineering 2007, ICCMSE 2007
Country/TerritoryGreece
CityCorfu
Period25/09/0730/09/07

Keywords

  • Combinatorial optimization
  • High-order hopfield network
  • MPI

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