Skip to main content
SpringerLink
Log in
Book cover

Variants of Evolutionary Algorithms for Real-World Applications pp 167–203Cite as

A Genetic-Based Solution to the Task-Based Sailor Assignment Problem

  • Chapter

  • 1680 Accesses

Abstract

This chapter presents a study investigating a multi-objective formulation of the United States Navy’s Task-based Sailor Assignment Problem and examines the performance of a widely used multi-objective evolutionary algorithm (MOEA), namely NSGA-II, on large instances of this problem. The performance of the evolutionary algorithm is examined with respect to both solution quality and diversity and has shown to provide inadequate diversity along the Pareto front. Domain-specific local improvement operators were introduced into the MOEA, producing significant performance increases over the evolutionary algorithm alone. Thus, hybrid MOEAs provided greater diversity along the Pareto front. Also a parallel version of the evolutionary algorithm was implemented. Particularly, an island model implementation was investigated. Exhaustive experimentations of the sequential and parallel implementations were carried out. The experimental results show that the genetic-based solution presented here is suitable for these types of problems.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Knowles, J.D., Corne, D.W.: Quantifying the effects of objective space dimension in evolutionary multiobjective optimization. Multi-Criterion Optimization, 757–771 (2007)

    Google Scholar 

  2. Moscato, P.: On evolution, search, optimization, genetic algorithms, and martial arts: Towards memetic algorithms. Technical Report 826, Caltech Concurrent Computation Program, C3P (1989)

    Google Scholar 

  3. Moscato, P., Cotta, C.: A gentle introduction to memetic algorithms. In: Handbook of Metaheuristics, pp. 105–144 (2003)

    Google Scholar 

  4. Dasgupta, D., Hernandez, G., Garrett, D., Vejandla, P., Kaushal, A., Yerneni, R., Simien, J.: A comparison of multiobjective evolutionary algorithms with informed initialization and kuhn-munkres algorithm for the sailor assignment problem. In: Proceedings of the 2008 Genetic and Evolutionary Computation Conference (GECCO 2008). ACM Press, New York (2008)

    Google Scholar 

  5. Garrett, J.D., Vannucci, J., Silva, R., Dasgupta, D., Simien, J.: Genetic algorithms for the sailor assignment problem. In: Proceedings of the 2005 Genetic and Evolutionary Computation Conference, GECCO 2005. ACM Press, New York (2005)

    Google Scholar 

  6. Knowles, J.D., Corne, D.W.: Memetic algorithms for multiobjective optimization: Issues, methods, and prospects. In: Krasnogor, N., Smith, J.E., Hart, W.E. (eds.) Recent Advances in Memetic Algorithms. Springer, Heidelberg (2004)

    Google Scholar 

  7. Jaszkiewicz, A.: On the performance of multiple objective genetic local search on the 0/1 knapsack problem: A comparative experiment. Technical Report RA-002/2000, Institute of Computing Science, Poznan University of Technology (July 2000)

    Google Scholar 

  8. López-Ibáñez, M., Paquete, L., Stützle, T.: Hybrid population-based algorithms for the bi-objective quadratic assignment problem. Technical Report 1, FG Intellektik, FB Informatik, TU Darmstadt, Journal of Mathematical Modelling and Algorithms (2004)

    Google Scholar 

  9. Shmoys, D.B., Tardos, É.: An approximation algorithm for the generalized assignment problem. Mathematical Programming 62(1), 461–474 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  10. Knowles, J., Corne, D.: Towards Landscape Analyses to Inform the Design of Hybrid Local Search for the Multiobjective Quadratic Assignment Problem. In: Abraham, A., Ruiz del Solar, J., Koppen, M. (eds.) Soft Computing Systems: Design, Management and Applications, pp. 271–279. IOS Press, Amsterdam (2002)

    Google Scholar 

  11. Knowles, J.D., Corne, D.W.: M-PAES: A memetic algorithm for multiobjective optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation (CEC 2000), pp. 325–332. IEEE Press, Los Alamitos (2000)

    Chapter  Google Scholar 

  12. Krasnogor, N.: Towards robust memetic algorithms. In: Hart, W.E., Krasnogor, N., Smith, J.E. (eds.) Recent Advances in Memetic Algorithms, pp. 185–208. Springer, Heidelberg (2004)

    Google Scholar 

  13. Garrett, J.D., Vannucci, J., Dasgupta, D., Simien, J.: Applying hybrid multiobjective evolutionary algorithms to the sailor assignment problem. In: Jain, L., Palade, V., Srinivasan, D. (eds.) Advances in Evolutionary Computing for System Design, pp. 269–301. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Gale, D., Shapley, L.S.: College admissions and the stability of marriage. The American Mathematical Monthly 69, 9–15 (1962)

    Article  MathSciNet  MATH  Google Scholar 

  15. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (1991)

    Article  Google Scholar 

  16. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (May 2001)

    Google Scholar 

  17. Munkres, J.: Algorithms for the assignment and transportation problems. Journal of the Society of Industrial and Applied Mathematics 5(1), 32–38 (1957)

    Article  MathSciNet  MATH  Google Scholar 

  18. Kuhn, H.W.: The hungarian method for the assignment problem. Naval Research Logistic Quarterly 2, 83–97 (1955)

    Article  Google Scholar 

  19. Dasgupta, D., Nino, F., Garrett, D., Chaudhuri, K., Medapati, S., Kaushal, A., Simien, J.: A multiobjective evolutionary algorithm for the task based sailor assignment problem. In: Proceedings of the 2009 Genetic and Evolutionary Computation Conference (GECCO 2009), pp. 1475–1482. ACM, New York (2009)

    Google Scholar 

  20. Akgul, M.: The linear assignment problem. Combinatorial Optimization, 85–122 (1992)

    Google Scholar 

  21. Talbi, E.-G., Mostaghim, S., Okabe, T., Ishibuchi, H., Rudolph, G., Coello Coello, C.A.: Parallel approaches for multiobjective optimization. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 349–372. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  22. Sun. Javaspaces, http://Java.sun.com/developer/technicalArticles/tools/JavaSpaces

  23. Freeman, E., Arnold, K., Hupfer, S.: JavaSpaces principles, patterns, and practice. E Addison-Wesley Longman Ltd., Amsterdam (1999)

    Google Scholar 

  24. Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary algorithms for solving multi-objective problems. Springer-Verlag New York Inc. (2007)

    Google Scholar 

  25. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), pp. 825–830. IEEE Press, Los Alamitos (2002)

    Chapter  Google Scholar 

  26. Bosman, P.A.N.: Design and Application of Iterated Density-Estimation Evolutionary Algorithms. PhD thesis, Institute of Information and Computing Sciences, Universiteit Utrecht, Utrecht, The Netherlands (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Memphis, Memphis, TN, USA

    Dipankar Dasgupta & Alex Banceanu

  2. Icelandic Institute for Intelligent Machines/School of Computer Science, Reykjavik University, Reykjavik, iceland

    Deon Garrett

  3. Department of Computer Science, National University of Colombia, Bogota, Colombia

    Fernando Nino & David Becerra

Authors
  1. Dipankar Dasgupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deon Garrett
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fernando Nino
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alex Banceanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David Becerra
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of ICT, Swinburne University of Technology, 3122, Melbourne, VIC, Australia

    Raymond Chiong

  2. Nature Inspired Computation and Applications Laboratory School of Computer Science and Technology, University of Science and Technology of China (USTC), 230027, Hefei, Anhui, China

    Thomas Weise

  3. School of Computer Science, University of Adelaide, 5005, Adelaide, SA, Australia

    Zbigniew Michalewicz

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Dasgupta, D., Garrett, D., Nino, F., Banceanu, A., Becerra, D. (2012). A Genetic-Based Solution to the Task-Based Sailor Assignment Problem. In: Chiong, R., Weise, T., Michalewicz, Z. (eds) Variants of Evolutionary Algorithms for Real-World Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23424-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23424-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23423-1

  • Online ISBN: 978-3-642-23424-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation