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An improvement to ant colony optimization heuristic

  • Shaoxing University
  • China Jiliang University

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

5 Scopus citations

Abstract

Ant Colony Optimization (ACO) heuristic provides a relatively easy and direct method to handle problem's constraints (through introducing the so called solution construction process), while in the other heuristics, constraint-handling is normally sophisticated. But this makes its solving process slow for the solution construction process occupies most part of its computation time. In this paper, we propose a strategy to hybridize Hopfield discrete neural networks (HDNN) with ACO heuristic for maximum independent set (MIS) problems. Several simulation instances showed that the strategy can greatly improve ACO heuristic performance not only in time cost but also in solution quality.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings
PublisherSpringer Verlag
Pages816-825
Number of pages10
EditionPART 1
ISBN (Print)3540877312, 9783540877318
DOIs
StatePublished - 2008
Event5th International Symposium on Neural Networks, ISNN 2008 - Beijing, China
Duration: 24 Sep 200828 Sep 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5263 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Symposium on Neural Networks, ISNN 2008
Country/TerritoryChina
CityBeijing
Period24/09/0828/09/08

Keywords

  • Ant colony optimization
  • Hopfield discrete neural network
  • Maximum independent set

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