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10.1145/1569901.1570170acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
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Solving the linear ordering problem using ant models

Published: 08 July 2009 Publication History

Abstract

Ant models are investigated with the purpose of providing a high-quality performing heuristic for solving the linear ordering problem. Extending the Ant Colony System (ACS) model, the proposed Step-Back Sensitive Ant Model (SBSAM) allows agents to take a 'step back' if it reaches a virtual state modulated by various sensitivity levels to the pheromone trails. An effective exploration of the search space is performed particularly by agents having low pheromone sensitivity while the exploitation of intermediary solutions is facilitated by highly-sensitive ants. Both ACS and SB-SAM techniques compete with existing heuristic methods for linear ordering in terms of solution quality.

References

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C. Chira, D. Dumitrescu, C. M. Pintea, "Heterogeneous Sensitive Ant Model for Combinatorial Optimization," ACM Proceedings of Genetic and Evolutionary Computation Conference GECCO'08, 163--164, 2008.
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M. Dorigo, L.M. Gambardella, "Ant Colony System: A cooperative learning approach to the traveling salesman problem," IEEE Trans. on Systems, Man, and Cybernetics, 26, 29--41, 1996.
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C. Garcia, D. P´erez, V. Campos, R. Marti, "Variable Neighborhood Search for the Linear Ordering Problem, Computers and Operations Research, 33, 3549--3565, 2006. http://www.iwr.uni-heidelberg.de/groups/comopt/software/LOLIB/, "LOLIB site, at Heidelberg University, Germany".
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M. Laguna, R. Marti, V. Campos, "Intensification and Diversification with Elite Tabu Search Solutions for the Linear Ordering Problem," Computers and Operations Research, 26, 1217--1230, 1999.
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M.G.C. Resende, C.C. Ribeiro, "Greedy randomized adaptive search procedures: Advances and applications," Handbook of Metaheuristics, 2nd Edition, Springer, 2008.
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Cited By

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  • (2019)Hybrid Heuristics for the Linear Ordering Problem2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790280(1431-1438)Online publication date: Jun-2019
  • (2018)Multi-agents features on Android platformsComplex Adaptive Systems Modeling10.1186/s40294-018-0061-76:1Online publication date: 16-Nov-2018
  • (2017)A New Precedence-Based Ant Colony Optimization for Permutation ProblemsSimulated Evolution and Learning10.1007/978-3-319-68759-9_79(960-971)Online publication date: 14-Oct-2017
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Published In

cover image ACM Conferences
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
July 2009
2036 pages
ISBN:9781605583259
DOI:10.1145/1569901

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2009

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Author Tags

  1. ant colony optimization
  2. linear ordering

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GECCO09
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GECCO09: Genetic and Evolutionary Computation Conference
July 8 - 12, 2009
Québec, Montreal, Canada

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2019)Hybrid Heuristics for the Linear Ordering Problem2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790280(1431-1438)Online publication date: Jun-2019
  • (2018)Multi-agents features on Android platformsComplex Adaptive Systems Modeling10.1186/s40294-018-0061-76:1Online publication date: 16-Nov-2018
  • (2017)A New Precedence-Based Ant Colony Optimization for Permutation ProblemsSimulated Evolution and Learning10.1007/978-3-319-68759-9_79(960-971)Online publication date: 14-Oct-2017
  • (2017)A Hybrid Algorithm Based on Particle Swarm Optimization and Ant Colony Optimization AlgorithmSmart Computing and Communication10.1007/978-3-319-52015-5_3(22-31)Online publication date: 13-Jan-2017
  • (2014)Can deterministic chaos improve differential evolution for the linear ordering problem?2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900589(1443-1448)Online publication date: Jul-2014
  • (2014)Extending distance-based ranking models in estimation of distribution algorithms2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900435(2459-2466)Online publication date: Jul-2014
  • (2013)Implementing Artificial Immune Systems for the Linear Ordering ProblemSoft Computing Models in Industrial and Environmental Applications10.1007/978-3-642-32922-7_6(53-62)Online publication date: 2013
  • (2012)Practical results of artificial immune Systems for combinatorial optimization problems2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC)10.1109/NaBIC.2012.6402261(194-199)Online publication date: Nov-2012
  • (2012)A genetic programming approach for solving the linear ordering problemProceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II10.1007/978-3-642-28931-6_32(331-338)Online publication date: 28-Mar-2012

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