Improved Multi-Objective Diversity Control Oriented Genetic Algorithm

  • Theera Piroonratana
  • Nachol Chaiyaratana
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


This paper presents an improved multi-objective diversity control oriented genetic algorithm (MODCGA-II). The improvement includes the introduction of an objective-domain diversity control operator, which is chromosome representation independent, and a solution archive. The performance comparison between the MODCGA-II, a non-dominated sorting genetic algorithm II (NSGA-II) and an improved strength Pareto evolutionary algorithm (SPEA-II) is carried out where different two-objective benchmark problems with specific multi-objective characteristics are utilised. The results indicate that the MODCGA-II solutions are better than the solutions generated by the NSGA-II and SPEA-II in terms of the closeness to the true Pareto optimal solutions and the uniformity of solution distribution along the Pareto front.


Genetic Algorithm Pareto Front Multiobjective Optimization Objective Space True Pareto Front 


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  1. 1.
    Mauldin, M.L.: Maintaining diversity in genetic search. In: Proceedings of the National Conference on Artificial Intelligence, Austin, TX, pp. 247–250 (1984)Google Scholar
  2. 2.
    Mori, N., Yoshida, J., Tamaki, H., Kita, H., Nishikawa, Y.: A thermodynamical selection rule for the genetic algorithm. In: Proceedings of the Second IEEE International Conference on Evolutionary Computation, Perth, WA, pp. 188–192 (1995)Google Scholar
  3. 3.
    Whitley, D.: The GENITOR algorithm and selection pressure: Why rank-based allocation of reproduction trials is best. In: Proceedings of the Third International Conference on Genetic Algorithms, Fairfax, VA, pp. 116–121 (1989)Google Scholar
  4. 4.
    Eshelman, L.J.: The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: Rawlins, G.J.E. (ed.) Foundations of Genetic Algorithms, vol. 1, pp. 265–283. Morgan Kaufmann, San Mateo (1991)Google Scholar
  5. 5.
    Shimodaira, H.: A new genetic algorithm using large mutation rates and population-elitist selection (GALME). In: Proceedings of the Eighth IEEE International Conference on Tools with Artificial Intelligence, Toulouse, France, pp. 25–32 (1996)Google Scholar
  6. 6.
    Shimodaira, H.: DCGA: A diversity control oriented genetic algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Glasgow, UK, pp. 444–449 (1997)Google Scholar
  7. 7.
    Shimodaira, H.: A diversity-control-oriented genetic algorithm (DCGA): Performance in function optimization. In: Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, Korea, pp. 44–51 (2001)Google Scholar
  8. 8.
    Fonseca, C.M., Fleming, P.J.: Multiobjective optimization and multiple constraint handling with evolutionary algorithms–Part 1: A unified formulation. IEEE Transactions on Systems, Man, and Cybernetics–Part A: Systems and Humans 28(1), 26–37 (1998)CrossRefGoogle Scholar
  9. 9.
    Sangkawelert, N., Chaiyaratana, N.: Diversity control in a multi-objective genetic algorithm. In: Proceedings of the 2003 Congress on Evolutionary Computation, Canberra, Australia, pp. 2704–2711 (2003)Google Scholar
  10. 10.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  11. 11.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K., Tsahalis, D., Periaux, J., Papailiou, K., Fogarty, T. (eds.) Evolutionary Methods for Design, Optimisation and Control, Barcelona, Spain, pp. 95–100 (2002)Google Scholar
  12. 12.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173–195 (2000)CrossRefGoogle Scholar
  13. 13.
    Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. In: Abraham, A., Jain, L.C., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization: Theoretical Advances and Applications, pp. 105–145. Springer, London (2005)CrossRefGoogle Scholar
  14. 14.
    Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Theera Piroonratana
    • 1
  • Nachol Chaiyaratana
    • 2
  1. 1.Department of Production EngineeringKing Mongkut’s Institute of Technology North BangkokBangsue, BangkokThailand
  2. 2.Research and Development Center for Intelligent SystemsKing Mongkut’s Institute of Technology North BangkokBangsue, BangkokThailand

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