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The Parallel Single Front Genetic Algorithm (PSFGA) in Dynamic Multi-objective Optimization

  • Mario Cámara
  • Julio Ortega
  • Francisco de Toro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)

Abstract

This paper analyzes the use of the, previously proposed, Parallel Single Front Genetic Algorithm (PSFGA) in applications in which the objective functions, the restrictions, and hence also solutions can change over the time. These dynamic optimization problems appear in quite different real applications with relevant socio-economic impacts. PSFGA uses a master process that distributes the population among the processors in the system (that evolve their corresponding solutions according to an island model), and collects and adjusts the set of local Pareto fronts found by each processor (this way, the master also allows an implicit communication among islands). The procedure exclusively uses non-dominated individuals for the selection and variation, and maintains the diversity of the approximation to the Pareto front by using a strategy based on a crowding distance.

Keywords

dynamic optimization problems parallel evolutionary computation single front multi-objective optimization parallel processing 

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References

  1. 1.
    Branke, J., Mattfeld, D.C.: Anticipation and flexibility in dynamic scheduling. International Journal of Production Research 43(15), 3103–3129 (2005)CrossRefGoogle Scholar
  2. 2.
    Farina, M., Deb, K., Amato, P.: Dynamic Multi-objective Optimization Problems: Test cases, Approximations, and Applications. IEEE Trans. on Evolutionary Computation 8(5), 342–425 (2004)CrossRefGoogle Scholar
  3. 3.
    Coello, C.A.: An Updated Survey of GA-Based Multi-objective Optimization Techniques. Technical Report Lania-RD-98-08, Laboratorio Nacional de Informática Avanzada (LANIA), México (1998)Google Scholar
  4. 4.
    Jin, Y., Branke, J.: Evolutionary Optimization in Uncertain Environments – A Survey. IEEE Trans. on Evolutionary Computation 9(3), 303–317 (2005)CrossRefGoogle Scholar
  5. 5.
    Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Dordrecht (2002)zbMATHGoogle Scholar
  6. 6.
    Bibliography about Evolutionary Algorithms for Multi-objective Optimization: http://www.lania.mx/~ccoello/EMOO
  7. 7.
    EvoDOP (Evolutionary Algorithms for Dynamic Optimization Problems): http://www.aifb.uni-karlsruhe.de/~jbr/EvoDOP
  8. 8.
    Weicker, K.: Performance Measures for Dynamic Environments. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 64–73. Springer, Heidelberg (2002)Google Scholar
  9. 9.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of Multi-objective Evolutionary Algorithms: Empirical Results. Tech. Report 70, ETH Zurich (December 1999)Google Scholar
  10. 10.
    Van Veldhuizen, D.A., Zydallis, J.B., Lamont, G.B.: Considerations in Engineering Parallel Multi-objective Evolutionary Algorithms. IEEE Trans. Evolutionary Computation 7(2), 144–173 (2003)CrossRefGoogle Scholar
  11. 11.
    Toro, F., Ortega, J., Ros, E., Mota, B., Paechter, B., Martín, J.M.: PSFGA: Parallel processing and evolutionary computation for multi-objective optimization. Parallel Computing 30, 721–739 (2004)CrossRefGoogle Scholar
  12. 12.
    Toro, F., Ros, E., Mota, S., Ortega, J.: Evolutionary Algorithms for Multi-objective and Multimodal Optimization of Diagnostic Schemes. IEEE Trans. on Biomedical Engineering 53(2), 178–189 (2006)CrossRefGoogle Scholar
  13. 13.
    Alba, E.: Parallel evolutionary algorithms can achieve super-linear performance. Information Processing Letters 82, 7–13 (2002)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Mario Cámara
    • 1
  • Julio Ortega
    • 1
  • Francisco de Toro
    • 2
  1. 1.Dep. of Computer Architecture and Technology 
  2. 2.Dep. of Signals, Telematics, and Communications, E.T.S.I.I.T. University of GranadaSpain

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