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Local models—an approach to distributed multi-objective optimization

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Abstract

When solving real-world optimization problems, evolutionary algorithms often require a large number of fitness evaluations in order to converge to the global optima. Attempts have been made to find techniques to reduce the number of fitness function evaluations. We propose a novel framework in the context of multi-objective optimization where fitness evaluations are distributed by creating a limited number of adaptive spheres spanning the search space. These spheres move towards the global Pareto front as components of a swarm optimization system. We call this process localization. The contribution of the paper is a general framework for distributed evolutionary multi-objective optimization, in which the individuals in each sphere can be controlled by any existing evolutionary multi-objective optimization algorithm in the literature.

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References

  1. Abbass, H.A., Sarker, R., Newton, C.: PDE: A Pareto frontier differential evolution approach for multiobjective optimization problems. In: Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 971–978. IEEE Service Center, Piscataway (2001)

    Google Scholar 

  2. Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic, Boston (2002)

    MATH  Google Scholar 

  3. Branke, J., Schmeck, H., Deb, K., Maheshwar, R.S.: Parallelizing multiobjective evolutionary algorithms: cone separation. In: Proceedings of the Congress on Evolutionary Computation, pp. 1952–1957. IEEE Press, New York (2004)

    Google Scholar 

  4. Buche, D., Schraudolph, N.N., Koumoutsakos, P.: Accelerating evolutionary algorithms with gaussian process fitness function models. IEEE Trans. Syst. Man Cybern. C: Appl. Rev. 35(2), 183–194 (2005)

    Article  Google Scholar 

  5. Cantu-Paz, E.: A survey of parallel genetic algorithms. Technical Report IlliGal, TR-97003, University of Illinois, Urbana–Champaign, 1997

  6. Cantu-Paz, E.: Migration policies, selection pressure and parallel evolutionary algorithms. Technical Report IlliGal TR-99015, University of Illinois, Urbana–Champaign, 1999

  7. Cantu-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic, Boston (2000)

    MATH  Google Scholar 

  8. Coello, C.A.C.: Evolutionary multi-objective optimization: a historical view of the field. IEEE Comput. Intell. Mag. 1(1), 28–36 (2006)

    Article  Google Scholar 

  9. Coello, C.A.C., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic, New York (2002)

    MATH  Google Scholar 

  10. Deb, K.: Multiobjective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)

    Google Scholar 

  11. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  12. Deb, K., Zope, P., Jain, A.: Distributed computing of Pareto optimal solutions with evolutionary algorithms. In: Evolutionary Multi-Criterion Optimization (2003)

  13. Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the Congress on Evolutionary Computation, pp. 81–86. IEEE Press, New York (2001)

    Google Scholar 

  14. Elmihoub, T., Hopgood, A.A., Nolle, L., Battersby, A.: Performance of hybrid genetic algorithms incorporating local search. In: Proceedings of 18th European Simulation Multiconference, pp. 154–160. Magdeburg, Germany (2004)

  15. Streichert, F., Ulmer, H., Zell, A.: Parallelization of multiobjective evolutionary algorithms using clustering algorithms. In: Evolutionary Multi-Criterion Optimization, pp. 92–107 (2005)

  16. Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic, Boston (2002)

    MATH  Google Scholar 

  17. Hart, W.E.: Adaptive global optimization with local search. Ph.D. thesis, University of California, San Diego, CA, USA (1994)

  18. Hiroyasu, T., Miki, M., Wantanabe, S.: The new model of parallel genetic algorithm in multiobjective optimization problems- divided range multi-objective genetic algorithm. In: Proceedings of the Congress on Evolutionary Computation, pp. 333–340. IEEE Press, New York (2000)

    Google Scholar 

  19. Ishibuchi, H., Yoshida, T., Murata, T.: Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans. Evol. Comput. 7(2), 204–223 (2003)

    Article  Google Scholar 

  20. Jaimes, A.L., Coello, C.A.C.: MRMOGA: parallel evolutionary multiobjective optimization using multiple resolutions. In: Corne, D. (ed.) Proceedings of the Congress on Evolutionary Computation, pp. 2294–2301. IEEE Press, New York (2005)

    Chapter  Google Scholar 

  21. Jaszkiewicz, A.: Genetic local search for multi-objective combinatorial optimization. Eur. J. Oper. Res. 137(1), 50–71 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  22. Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments—a survey. IEEE Trans. Evol. Comput. 9(3), 303–317 (2005)

    Article  Google Scholar 

  23. KanGal. Kangal laboratory website. http://www.iitk.ac.in/kangal/codes.shtml.

  24. Tasoulis, D.K., Parsopoulos, K.E., Vrahatis, M.N.: Multiobjective optimization using parallel vector evaluated particle swarm optimization. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, vol. 2, pp. 823–828. ACTA Press, Innsbruck (2004)

    Google Scholar 

  25. Knowles, J.D., Corne, D.: M-PAES: A memetic algorithm for multi-objective optimization. In: Proceedings of the Congress on Evolutionary Computation, pp. 325–332. IEEE Press, New York (2000)

    Google Scholar 

  26. Lobo, E.G., Goldberg, D.E.: Decision making in a hybrid genetic algorithm. Technical report, Department of General Engineering, University of Illinois at Urbana–Champaign, 1996

  27. Parsopoulos, K.E., Vrahatis, M.N.: Recent approaches to global optimization problems through particle swarm optimization. Nat. Comput. 1(2–3), 235–306 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  28. Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Genetic Algorithms and Their Applications: Proceedings of the First International Conference on Genetic Algorithms, Hillsdale, New Jersey, 1985, pp. 93–100

  29. Schott, J.R.: Fault tolerant design using single and multicriteria genetic algorithm optimization. Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology (1995)

  30. Tan, K.C., Yang, Y.J., Goh, C.K.: A distributed cooperative coevolutionary algorithm for multi-objective optimization. IEEE Trans. Evol. Comput. 10(5), 527–549 (2006)

    Article  Google Scholar 

  31. Tan, K.C., Yang, Y.J., Lee, T.H.: Designing a distributed cooperative coevolutionary algorithm for multiobjective optimization. In: Proceedings of the Congress on Evolutionary Computation, pp. 2513–2520. IEEE Press, New York (2003)

    Chapter  Google Scholar 

  32. Tan, K.C., Lee, T.H., Khor, E.F.: Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization. IEEE Trans. Evol. Comput. 5(6), 565–588 (2001)

    Article  Google Scholar 

  33. Van Veldhuizen, D.A.: Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. Ph.D. thesis, Air Force Institute of Technology, Dayton OH, USA (1999)

  34. Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary computation and convergence to a Pareto front. In: Koza, J.R., et al. (eds.) Genetic Programming Conference 1998, pp. 221–228 (1998)

  35. Van Veldhuizen, D.A., Zydallis, J.B., Lamont, G.B.: Considerations in engineering parallel multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 7(2), 144–173 (2003)

    Article  Google Scholar 

  36. Zhu, Z.-Y., Leung, K.-S.: Asynchronous self-adjustable island genetic algorithm for multi-objective optimization problems. In: Proceedings of the Congress on Evolutionary Computation, pp. 837–842. IEEE Press, New York (2002)

    Google Scholar 

  37. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm. Technical report, Swiss Federal Institute of Technology (2001)

  38. Zitzler, E., Thiele, L.: Multi-objective optimization using evolutionary algorithms—a comparative case study. In: Parallel Problem Solving from Nature, pp. 292–301. Springer, Berllin (1998)

    Chapter  Google Scholar 

  39. Zitzler, E., Thiele, L., Deb, K.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(1), 173–195 (2000)

    Article  Google Scholar 

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Correspondence to Lam T. Bui.

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Bui, L.T., Abbass, H.A. & Essam, D. Local models—an approach to distributed multi-objective optimization. Comput Optim Appl 42, 105–139 (2009). https://doi.org/10.1007/s10589-007-9119-8

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  • DOI: https://doi.org/10.1007/s10589-007-9119-8

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