Advertisement

Diversity Maintenance Mechanism for Multi-Objective Genetic Algorithms Using Clustering and Network Inversion

  • Tomoyuki Hiroyasu
  • Kenji Kobayashi
  • Masashi Nishioka
  • Mitsunori Miki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

One of the major issues in applying multi-objective genetic algorithms to real-world problems is how to reduce the large number of evaluations. The simplest approach is a search with a small population size. However, the diversity of solutions is often lost with such a search. To overcome this difficulty, this paper proposes a diversity maintenance mechanism using clustering and Network Inversion that is capable of preserving diversity by relocating solutions. In addition, the proposed mechanism adopts clustering of training data sets to improve the accuracy of relocation. The results of numerical experiments on test functions and diesel engine emission and fuel economy problems showed that the proposed mechanism provided solutions with high diversity even when the search was performed with a small number of solutions.

Keywords

Diesel Engine Multiobjective Optimization Inverse Analysis Neighbor Relationship Target Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. KanGAL report 200001, Indian Institute of Technology, Kanpur, India (2000)Google Scholar
  2. 2.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Performance of the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology (ETH) Zurich (2001)Google Scholar
  3. 3.
    Chiba, K., Obayashi, S.: High-Fidelity Multidisciplinary Design Optimization of Aerostructural Wing Shape for Regional Jet. In: 23rd Applied Aerodynamics Conference (2005)Google Scholar
  4. 4.
    Hiroyasu, T., Miki, M., Kamiura, J., Watanabe, S., Hiroyasu, H.: Multi-Objective Optimization of Diesel Engine Emissions and Fuel Economy using Genetic Algorithms and Phenomenological Model. In: SAE, Powertrain and Fluid Systems Conference (2002)Google Scholar
  5. 5.
    Shirai, T., Arakawa, M., Nakayama, H.: Approximate Multi-Objective Optimization Using RBF Network. In: The Computational Mechanics Conference, vol. 18, pp. 759–760 (2005)Google Scholar
  6. 6.
    Peixoto, J.L.: Hierarchical Variable Selection in Polynomial Regression Models. The American Statistician 41(4), 311–313 (1987)Google Scholar
  7. 7.
    Sacks, J., et al.: Design and Analysis of Computer Experiments 4. Statistical Science 4, 409–435 (1989)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Obayashi, S., Sasaki, D., Oyama, A.: Finding Tradeoffs by Using Multiobjective Optimization Algorithms. Transactions of JSASS 47(155), 51–58 (2004)Google Scholar
  9. 9.
    Kobayashi, K., Hiroyasu, T., Miki, M.: Mechanism of Multi-Objective Genetic Algorithm for Maintaining the Solution Diversity Using Neural Network. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 216–226. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Linden, A., Kindermann, J.: Inversion of multilayer nets. In: Proc. Int. Joint conf. on Neural Networks, pp. 425–430 (1989)Google Scholar
  11. 11.
    Ogawa, T., Kaneda, H.: Complex-Valued Network Inversion for Solving Complex-Valued Inverse Problems. Bulletin of science and engineering, Takushoku University 9(4), 83–84 (2006)Google Scholar
  12. 12.
    Cunha, A.G., Vieira, A.: A Hybrid Multi-Objective Evolutionary Algorithm Using an Inverse Neural Network. Hybrid Metaheuristics, 25–30 (2004)Google Scholar
  13. 13.
    Adra, S.F., Hamody, I., Griffin, I., Fleming, P.J.: A Hybrid Multi-Objective Evolutionary Algorithm Using an Inverse Neural Network for Aircraft Control System Design. In: 2005 IEEE Congress on Evolutionary Computation (CEC 2005), vol. 1, pp. 1–8 (2005)Google Scholar
  14. 14.
    Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms, pp. 326–327. John Wiley and Sons, Chichester Google Scholar
  15. 15.
    Deb, K., Meyarivan, T.: Constrained Test Problems for Multi-Objective Evolutionary Optimization. KanGAL report 200005, Indian Institute of Technology, Kanpur, India (2000)Google Scholar
  16. 16.
    Aoyagi, Y.: A Survey of Existing Emission Reduction Technology for Gasoline-powered Engine and Future Prospect  55(9), 10–16 (2001)Google Scholar
  17. 17.
    Hiroyasu, H., Kadota, T., Arai, M.: Development and Use of a Spray Combustion Modeling to Predict Diesel Engine Efficiency and Pollutant Emissions (Part 1 Combustion Modeling). Bulletin of the JSME 26(214), 569–575 (1983)CrossRefGoogle Scholar
  18. 18.
    Hiroyasu, H., Kadota, T., Arai, M.: Development and Use of a Spray Combustion Modeling to Predict Diesel Engine Efficiency and Pollutant Emissions (Part 2 Computational Procedure and Parametric Study). Bulletin of the JSME 26(214), 576–583 (1983)CrossRefGoogle Scholar
  19. 19.
    Itoh, S., Nakamura, K.: Reduction of Diesel Exhaust Gas Emission with Common Rail System 55(9), 46–52 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Tomoyuki Hiroyasu
    • 1
  • Kenji Kobayashi
    • 2
  • Masashi Nishioka
    • 2
  • Mitsunori Miki
    • 3
  1. 1.Faculty of Life and Medical SciencesDoshisha UniversityKyotoJapan
  2. 2.Graduate School of EngineeringDoshisha UniversityKyotoJapan
  3. 3.Faculty of Science and EngineeringDoshisha UniversityKyotoJapan

Personalised recommendations