Incorporating Distance Domination in Multiobjective Evolutionary Algorithm

  • Praveen K. Tripathi
  • Sanghamitra Bandyopadhyay
  • Sankar K. Pal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)


In this article we propose a novel distance domination parameter and describe a multiobjective evolutionary concept called distance domination based multiobjective evolutionary algorithm (DBMEA). The distance parameter drives the algorithm faster in approximating the Pareto optimal front. To ensure proper diversity in the solutions of the non-dominating set, a new method for incorporating diversity is explained. The DBMEA has been compared with the NSGA-II algorithm on different test functions using different performance measures.


Test Problem Multiobjective Evolutionary Algorithm Convergence Measure Vector Evaluate Genetic Algorithm Standard Test Function 
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.


  1. 1.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II. Technical Report 200001, Kanpur Genetic Algorithms Laboratory (KanGAL), Indian Institute of Technology Kanpur, India (2000)Google Scholar
  2. 2.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report TIK-103, Computer Engineering and Network Laboratory (TIK), Swiss Fedral Institute of Technology (ETH), Gloriastrasse 35, CH-8092 Zurich, Swidzerland (2001)Google Scholar
  3. 3.
    Schaffer, J.D.: Some Experiments in Machine Learning using Vector Evaluated Genetic Algorithm. PhD thesis, Vanderbilt University, Nashville,TN (1984)Google Scholar
  4. 4.
    Fonseca, C.M., Fleming, P.J.: An Overview of Evolutionary Algorithms in Multi-objective Optimization. Evolutionary Computation Journal 3, 1–16 (1995)CrossRefGoogle Scholar
  5. 5.
    Kursawe, F.: A Variant of Evolutionary Strategies for Vector Optimization. In: Parellel Problem Solving from Nature I (PPSN-I), pp. 193–197 (1990)Google Scholar
  6. 6.
    Poloni, C., Giurgevich, A., Onesti, L., Pediroda, V.: Hybridization of a Multiobjective Genetic Algorithm, a Neural Network and a Classical Optimizer for Complex Design Problem in Fluid Dynamics. Computer Methods in Applied Mechanics and Engineering, 403–420 (2000)Google Scholar
  7. 7.
    Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation Journal 8, 125–148 (2000)CrossRefGoogle Scholar
  8. 8.
    Bandyopadhyay, S., Pal, S.K., Aruna, B.: Multi-Objective GAs, Quantitative Indices, and Pattern Classification. IEEE Transaction on Systems, Man, and Cybernetics-Part B: Cybernetics 34, 2088–2099 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Praveen K. Tripathi
    • 1
  • Sanghamitra Bandyopadhyay
    • 1
  • Sankar K. Pal
    • 1
  1. 1.Machine Intelligence UnitIndian Statistical InstituteKolkata

Personalised recommendations