Simultaneous Optimization of Customer Satisfaction and Cost Function by Nature Inspired Computing

  • János Botzheim
  • Péter Földesi
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 15)


When optimizing multi-dimensional non-linear problems the optimal solution in technical terms can be found by heuristic methods. It seems that human thinking does not work properly with the mathematical processing: human decision-makers tend to reject options that represent extreme values in the set of parameters and are not able to handle many system parameters at the same time. The paper investigates the best possible way for modeling the human thinking, comparing bacterial memetic algorithm and particle swarm optimization in fuzzy environment.


Particle Swarm Optimization Customer Satisfaction Memetic Algorithm Human Thinking Simultaneous Optimization 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Balázs, K., Botzheim, J., Kóczy, L.T.: Comparative Investigation of Various Evolutionary and Memetic Algorithms. In: Rudas, I.J., Fodor, J., Kacprzyk, J. (eds.) Computational Intelligence in Engineering. SCI, vol. 313, pp. 129–140. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Botzheim, J., Földesi, P.: Parametric representation of fuzzy power function for decision-making processes. In: Proceedings of the 7th International Symposium on Management Engineering, Kitakyushu, Japan, pp. 248–255 (2010)Google Scholar
  3. 3.
    Botzheim, J., Cabrita, C., Kóczy, L.T., Ruano, A.E.: Fuzzy rule extraction by bacterial memetic algorithms. In: Proceedings of the 11th World Congress of International Fuzzy Systems Association, Beijing, China, pp. 1563–1568 (2005)Google Scholar
  4. 4.
    Coello, C.A.C., Lamont, G.B.: Applications of Multi-Objective Evolutionary Algorithms. World Scientific (2004)Google Scholar
  5. 5.
    Coello, C.A.C., Dhaenens, C., Jourdan, L.: Advances in Multi-Objective Nature Inspired Computing. Springer (2009)Google Scholar
  6. 6.
    Fieldsend, J.E.: Multi-objective particle swarm optimisation methods. Department of Computer Science. University of Exeter (2004)Google Scholar
  7. 7.
    Földesi, P., Botzheim, J.: Interpretation of Loss Aversion in Kano’s Quality Model. In: Watada, J., Phillips-Wren, G., Jain, L.C., Howlett, R.J. (eds.) Intelligent Decision Technologies. SIST, vol. 10, pp. 165–174. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Földesi, P., Kóczy, L.T., Botzheim, J.: Fuzzy solution for non-linear quality models. In: Proceedings of the 12th International Conference on Intelligent Engineering Systems, INES 2008, Miami, Florida, pp. 269–275 (2008)Google Scholar
  9. 9.
    Hernandez-Diaz, A.G., Coello, C.A.C., Perez, F., Caballero, R., Molina, J., Santana-Quintero, L.V.: Seeding the initial population of a multi-objective evolutionary algorithm using gradient-based information. In: Proceedings of the IEEE Congress on Evolutionary Computation, Hong Kong, pp. 1617–1624 (2008)Google Scholar
  10. 10.
    Kano, N., Seraku, N., Takahashi, F., Tsuji, S.: Attractive quality and must-be quality. The Journal of Japanese Society for Quality Control 14(2), 39–48 (1984)Google Scholar
  11. 11.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE Int. Conf. on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)Google Scholar
  12. 12.
    Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm Intelligence. Morgan Kaufmann (2001)Google Scholar
  13. 13.
    Levenberg, K.: A method for the solution of certain non-linear problems in least squares. Quart. Appl. Math. 2(2), 164–168 (1944)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Indust. Appl. Math. 11(2), 431–441 (1963)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Tech. Rep. Caltech, Pasadena, USA (1989)Google Scholar
  16. 16.
    Mostaghim, S., Teich, J.: Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 26–33 (2003)Google Scholar
  17. 17.
    Nawa, N.E., Furuhashi, T.: Fuzzy system parameters discovery by bacterial evolutionary algorithm. IEEE Transactions on Fuzzy Systems 7(5), 608–616 (1999)CrossRefGoogle Scholar
  18. 18.
    Shukla, P.K.: Exploiting Second Order Information in Computational Multi-objective Evolutionary Optimization. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 271–282. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of AutomationSzéchenyi István UniversityGyőrHungary
  2. 2.Department of Logistics and ForwardingSzéchenyi István UniversityGyőrHungary

Personalised recommendations