A Novel Non-dominated Sorting Algorithm

  • Gaurav Verma
  • Arun Kumar
  • Krishna K. Mishra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)


Many multi-objective evolutionary algorithms (MOEA) require non-dominated sorting of the population. The process of non-dominated sorting is one of the main time consuming parts of MOEA. The performance of MOEA can be improved by designing efficient non-dominated sorting algorithm. The paper proposes Novel Non-dominated Sorting algorithm (NNS). NNS algorithm uses special arrangement of solutions which in turn helps to reduce total number of comparisons among solutions. Experimental analysis and comparison study show that NNS algorithm improves the process of non-dominated sorting for large population size with increasing number of objectives.


Differential Evolution Pareto Front Large Population Size Current Element Strength Pareto Evolutionary Algorithm 
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.
    Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms, pp. 33–43. John Wiley & Sons, Ltd (2000/2001)Google Scholar
  2. 2.
    Deb, K., Pratab, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  3. 3.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. TIK-Report 103. ETH Zentrum, Gloriastrasse 35, CH-8092 Zurich, Switzerland (1999)Google Scholar
  4. 4.
    Shi, C., Li, Y., Kang, L.S.: A New Simple and Highly Efficient multi-objective Optimal Evolutionary Algorithm. In: Proceedings of 2003 IEEE Conference on Evolutionary Computation, Australia (2003)Google Scholar
  5. 5.
    Kung, H., Luccio, F., Preparata, F.: On finding the maxima of a set of vectors. Journal of the Association Computing Machinery 22(4), 469–476 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Freitas, A.A.: A critical review of multi-objective optimization in data mining: a position paper. SIGKDD Explorations 6(2), 77–86 (2004)CrossRefGoogle Scholar
  7. 7.
    Shi, C., Chen, M., Shi, Z.: A Fast Non-dominated Sorting Algorithm. In: International Conference on Neural Networks and Brain, ICNN&B 2005, vol. 2, pp. 1605–1610 (2005)Google Scholar
  8. 8.
    Jensen, M.T.: Reducing the run-time complexity of multi-objective EAs: The NSGA-II and other algorithms. IEEE Transactions on Evolutionary Computation 7, 502–515 (2003)Google Scholar
  9. 9.
    Qu, B.Y., Suganthan, P.N.: Multi-Objective Evolutionary Algorithms based on the Summation of Normalized Objectives and Diversified Selection. Information Sciences 180(17), 3170–3181 (2010)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Qu, B.-Y., Suganthan, P.N.: Multi-Objective Differential Evolution based on the Summation of Normalized Objectives and Improved Selection Method. In: Proc. of Symposium on Differential Evolution, Paris, France, April 2011Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gaurav Verma
    • 1
  • Arun Kumar
    • 2
  • Krishna K. Mishra
    • 3
  1. 1.NetApp India Pvt LtdBangaloreIndia
  2. 2.Citrix Systems Pvt LtdBangaloreIndia
  3. 3.Department of Computer Science and EngineeringMotilal Nehru National Institute of TechnologyAllahabadIndia

Personalised recommendations