JACIII Vol.13 No.4 pp. 470-480
doi: 10.20965/jaciii.2009.p0470


Analysis of NSGA-II and NSGA-II with CDAS, and Proposal of an Enhanced CDAS Mechanism

Kyoko Tsuchida*, Hiroyuki Sato*, Hernan Aguirre*,**,
and Kiyoshi Tanaka*

* Faculty of Engineering, Shinshu University,

** Fiber Nanotech Young Researcher Empowerment Program, Shinshu University,
4-17-1 Wakasato, Nagano, 380-8553, Japan

December 1, 2008
March 23, 2009
July 20, 2009
multiobjective evolutionary algorithm, multiobjective optimization, NSGA-II, controlling dominance area of solutions, functionality transition
In this work, we analyze the functionality transition in the evolution process of NSGA-II and an enhanced NSGA-II with the method of controlling dominance area of solutions (CDAS) from the viewpoint of front distribution. We examine the relationship between the population of the first front consisting of non-dominated solutions and the values of two metrics, NORM and ANGLE, which measure convergence and diversity of Pareto-optimal solutions (POS), respectively. We also suggest potentials to further improve the search performance of the enhanced NSGA-II with CDAS by emphasizing the parameter S, which controls the degree of dominance by contracting or expanding the dominance area of solutions, before and after the boundary generation of functionality transition. Furthermore, we analyze the behavior of the evolution of the enhanced NSGA-II with CDAS using the best parameters combination and compare its performance with two other algorithms that enhance selection of NSGA-II.
Cite this article as:
K. Tsuchida, H. Sato, H. Aguirre, and K. Tanaka, “Analysis of NSGA-II and NSGA-II with CDAS, and Proposal of an Enhanced CDAS Mechanism,” J. Adv. Comput. Intell. Intell. Inform., Vol.13 No.4, pp. 470-480, 2009.
Data files:
  1. [1] K. Deb, “Multi-Objective Optimization using Evolutionary Algorithms,” John Wiley & Sons, 2001.
  2. [2] C. A. C. Coello, D. A. Van Veldhuizen, and G. B. Lamont, “Evolutionary Algorithms for Solving Multi-Objective Problems,” Kluwer Academic Publishers, 2002.
  3. [3] K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, “A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II,”KanGAL report 200001, 2000.
  4. [4] H. Sato, H. Aguirre, and K. Tanaka, “Controlling Dominance Area of Solutions and its Impact on the Performance of MOEAs,”Proc. 4th Int. Conf. on Evolutionary Multi-Criterion Optimization (EMO2007), LNCS (Springer), Vol.4403, pp. 5-20, 2007.
  5. [5] M. Sato, H. Aguirre, and K. Tanaka, “Effects of δ-Similar Elimination and Controlled Elitism in the NSGA-II Multiobjective Evolutionary Algorithm,”Proc. IEEE Congress on Evolutionary Computation (CEC2006), pp. 451-458, 2006.
  6. [6] zitzler/testdata.html
  7. [7] E. Zitzler and L. Thiele, “Multiobjective optimization using evolutionary algorithms - a comparative case study,” Proc. 5th Int. Conf. Parallel Problem Solving from Nature (PPSN-V), LNCS (Springer), Vol.1498, pp. 292-301, 1998.
  8. [8] E. Zitzler, “Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications,”Ph.D. thesis, Swiss Federal Institute of Technology, Zurich, 1999.
  9. [9] J. Knowles and D. Corne, “On Metrics for Comparing Non-dominated Sets,” Proc. 2002 IEEE Congress on Evolutionary Computation, pp. 711-716, IEEE Service Center, 2002.
  10. [10] M. Koppen and K. Yoshida, “Substitute Distance Assignments in NSGA-II for Handling Many-Objective Optimization Problems,”Proc. 4th Int. Conf. on Evolutionary Multi-Criterion Optimzation, LNCS (Springer), Vol.4403, pp. 727-741, 2007.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 19, 2024