skip to main content
10.1145/2463372.2463390acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

Migration study on a pareto-based island model for MOACOs

Published:06 July 2013Publication History

ABSTRACT

Pareto-based island model is a multi-colony distribution scheme recently presented for the resolution, by means of ant colony optimization algorithms, of bi-criteria problems. It yielded very promising results, but the model was implemented considering a unique Pareto-front-shaped unidirectional neighborhood migration topology, and a constant migration rate. In the present work two additional neighborhood topology schemes, and four different migration rates have been tested, considering the algorithm which obtained the best results in average in the model presentation article: MOACS (Multi-Objective Ant Colony System). Several experiments have been conducted, including statistical tests for better support the study. High values for the migration rate and the use of a bidirectional neighborhood migration topology yields the best results.

References

  1. B. Barán and M. Schaerer. A multiobjective ant colony system for vehicle routing problem with time windows. In IASTED International Multi-Conference on Applied Informatics, number 21 in IASTED IMCAI, pages 97--102, 2003.Google ScholarGoogle Scholar
  2. M. Bolondi and M. Bondanza. Parallelizzazione di un Algoritmo per la Risoluzione del Problema del Commesso Viaggiatore. PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, 2003.Google ScholarGoogle Scholar
  3. E. Cantú-Paz. Topologies, migration rates, and multi-population parallel genetic algorithms. In Genetic and Evolutionary Computation Conference, GECCO-99, pages 13--17. ACM, 1999.Google ScholarGoogle Scholar
  4. G. Croes. A method for solving traveling salesman problems. Operations Res., 6:791--812, 1958.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. P. Dickinson and B. Chow. Some properties of the tukey test to duckworth's specification. Technical report, Office of Institutional Research, University of Southwestern Louisiana, Lafayette, USA, 1971.Google ScholarGoogle Scholar
  6. M. Dorigo and T. Stützle. The ant colony optimization metaheuristic: Algorithms, applications, and advances. In G. K. F. Glover, editor, Handbook of Metaheuristics, pages 251--285. Kluwer, 2002.Google ScholarGoogle Scholar
  7. J. Durillo, A. Nebro, and E. Alba. The jmetal framework for multi-objective optimization: Design and architecture. In IEEE Conference on Evolutionary Computation, CEC-2010, pages 4138--4325, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  8. R. Jovanovic, M. Tuba, and D. Simian. Comparison of different topologies for island-based multi-colony ant algorithms for the minimum weight vertex cover problem. Transactions on Computers, 9(1), 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. R. Michel and M. Middendorf. An island model based ant system with lookahead for the shortest supersequence problem. In Fifth International Conference on Parallel Problem Solving from Nature (PPSN-V), 1998. LNCS 1498. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. M. Middendorf, F. Reischle, and H. Schmeck. Information exchange in multi colony ant algorithms. In Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. Middendorf, F. Reischle, and H. Schmeck. Multi colony ant algorithms. Journal of Heuristics, 8:305--320, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. A. Mora, P. García-Sánchez, J. Merelo, and P. Castillo. Pareto-based multi-colony multi-objective ant colony optimization algorithms: An island model proposal. Soft Computing, In Press, 2013. http://link.springer.com/article/10.1007/s00500-013-0993-y.Google ScholarGoogle Scholar
  13. A. Osyczka. Multicriteria optimization for engineering design. In John S. Gero, editor, Design Optimization, pp.193--227. Academic Press, 1985.Google ScholarGoogle ScholarCross RefCross Ref
  14. M. Pedemonte, S. Nesmachnow, and H. Cancela. A survey on parallel ant colony optimization. Appl. Soft Comput., 11(8):5181--5197, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. C. Twomey, T. Stützle, M. Dorigo, M. Manfrin, and M. Birattari. An analysis of communication policies for homogeneous multi-colony aco algorithms. Inf. Sci., 2010(12):2390--2404, 180. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Migration study on a pareto-based island model for MOACOs

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation
          July 2013
          1672 pages
          ISBN:9781450319638
          DOI:10.1145/2463372
          • Editor:
          • Christian Blum,
          • General Chair:
          • Enrique Alba

          Copyright © 2013 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 6 July 2013

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          GECCO '13 Paper Acceptance Rate204of570submissions,36%Overall Acceptance Rate1,669of4,410submissions,38%

          Upcoming Conference

          GECCO '24
          Genetic and Evolutionary Computation Conference
          July 14 - 18, 2024
          Melbourne , VIC , Australia

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader