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Journal of Computer Science and Technology

, Volume 23, Issue 1, pp 44–63 | Cite as

Interleaving Guidance in Evolutionary Multi-Objective Optimization

  • Lam Thu BuiEmail author
  • Kalyanmoy Deb
  • Hussein A. Abbass
  • Daryl Essam
Regular Paper

Abstract

In this paper, we propose a framework that uses localization for multi-objective optimization to simultaneously guide an evolutionary algorithm in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres (local models) in the decision space. These spheres are usually guided, using some direction information, in the decision space towards the areas with non-dominated solutions. We use a second mechanism to adjust the spheres to specialize on different parts of the Pareto front by using a guided dominance technique in the objective space. Through this interleaved guidance in both spaces, the spheres will be guided towards different parts of the Pareto front while also exploring the decision space efficiently. The experimental results showed good performance for the local models using this dual guidance, in comparison with their original version.

Keywords

evolutionary multi-objective optimization guided dominance local models 

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References

  1. [1]
    Deb K. Multiobjective Optimization Using Evolutionary Algorithms. John Wiley and Son Ltd., New York, 2001.Google Scholar
  2. [2]
    Tan K C, Lee T H, Khor E F. Evolutionary algorithms for multi-objective optimization: Performance assessments and comparisons. Artificial Intelligence Review, 2002, 17(4): 251–290.zbMATHCrossRefGoogle Scholar
  3. [3]
    Tan K C, Khor E F, Lee T H. Multiobjective Evolutionary Algorithms and Applications. Springer-Verlag, 2005.Google Scholar
  4. [4]
    Coello C A C. Evolutionary multi-objective optimization: A historical view of the field. IEEE Computational Intelligence Magazine, 2006, 1(1): 28–36.CrossRefGoogle Scholar
  5. [5]
    Schaffer J D. Multiple objective optimization with vector evaluated genetic algorithms. In Proc. the First International Conference on Genetic Algorithms, Hillsdale, New Jersey, 1985, pp.93–100.Google Scholar
  6. [6]
    Zitzler E, Laumanns M, Thiele M. SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In Proc. EUROGEN 2001– Evolutionary Methods for Design, Optimisation and Control with Applications to Industrial Problems, Athens, Greece, 2001, pp.95–100.Google Scholar
  7. [7]
    Abbass H A, Sarker R, Newton C. PDE: A Pareto frontier differential evolution approach for multiobjective optimization problems. In Proc. CEC–2001, Seoul, Korea, vol 2, IEEE Press, 2001, pp.971–978.Google Scholar
  8. [8]
    Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA–II. IEEE Trans. Evolutionary Computation, 2002, 6(2): 182–197.CrossRefGoogle Scholar
  9. [9]
    Bui L T, Abbass H A, Essam D. Local models: An approach to distributed multi-objective optimization. Journal of Computational Optimization and Applications, Springer. [In Press, DOI: 10.1007/s10589-007-9119-8], 2007.
  10. [10]
    Deb K, Zope P, Jain A. Distributed computing of Pareto optimal solutions using multi-objective evolutionary algorithms. Technical Report, No. 2002008, KANGAL, IITK, India, 2002.Google Scholar
  11. [11]
    Zitzler E, Thiele L, Deb K. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 2000, 8(1): 173–195.CrossRefGoogle Scholar
  12. [12]
    Fonseca C M, Fleming P J. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In Proc. the Fifth International Conference on Genetic Algorithms, San Mateo, California, Morgan Kauffman Publishers, 1993, pp.416–423.Google Scholar
  13. [13]
    Horn J, Nafpliotis N, Goldberg D E. A niched Pareto genetic algorithm for multiobjective optimization. In Proc. The First IEEE Conference on Evolutionary Computation, Vol.1, IEEE World Congress on Computational Intelligence, Piscataway, New Jersey, 1994, pp.82–87.CrossRefGoogle Scholar
  14. [14]
    Srinivas N, Deb K. Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 1994, 2(3): 221–248.Google Scholar
  15. [15]
    Branke J, Kaufler T, Schmeck H. Guiding multi-objective evolutionary algorithms towards interesting regions. Technical Report No. 399. Technical Report, Institute AIFB, University of Karlsruhe, Germany, 2000.Google Scholar
  16. [16]
    Deb K, Zope P, Jain A. Distributed computing of Pareto optimal solutions with evolutionary algorithms. In Proc. Evolutionary Multi-Criterion Optimization, LNCS 2632, 2003, pp.535–549.Google Scholar
  17. [17]
    Branke J, Schmeck H, Deb K, Maheshwar R S. Parallelizing multiobjective evolutionary algorithms: Cone separation. In Proc. the Congress on Evolutionary Computation, Portland, Oregon, USA, IEEE Press, 2004, pp.1952–1957.CrossRefGoogle Scholar
  18. [18]
    Deb K, Sundar J. Reference point based multi-objective optimization using evolutionary algorithms. In Proc. The 8th Annual Conference on Genetic and Evolutionary Computation, GECCO’06, New York, NY, USA, 2006, ACM Press, pp.635–642.CrossRefGoogle Scholar
  19. [19]
    Eberhart R C, Shi Y. Particle swarm optimization: Developments, applications and resources. In Proc. the Congress on Evolutionary Computation, Piscataway, NJ, USA, IEEE Press, 2001, pp.81–86.CrossRefGoogle Scholar
  20. [20]
    KanGal. Kangal laboratory website. http://www.iitk.ac.in/kangal/codes.shtml, 2006.
  21. [21]
    Veldhuizen D A V. Multiobjective evolutionary algorithms: Classifications, analyses, and new innovation [Dissertation]. Department of Electrical Engineering and Computer Engineering, Airforce Institute of Technology, Ohio, 1999.Google Scholar
  22. [22]
    Tan K C, Lee T H, Khor E F. Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization. IEEE Transactions on Evolutionary Computation, 2001, 5(6): 565–588.CrossRefGoogle Scholar

Copyright information

© Science Press, Beijing, China and Springer Science + Business Media, LLC, USA 2008

Authors and Affiliations

  • Lam Thu Bui
    • 1
    Email author
  • Kalyanmoy Deb
    • 2
  • Hussein A. Abbass
    • 1
  • Daryl Essam
    • 1
  1. 1.The Artificial Life and Adaptive Robotics Laboratory, School of ITEE, ADFAUniversity of New South WalesCanberraAustralia
  2. 2.Mechanical Engineering DepartmentIndian Institute of TechnologyKanpurIndia

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