Bio-Inspired Algorithms and Preferences for Multi-objective Problems

  • Daniel CinalliEmail author
  • Luis Martí
  • Nayat Sanchez-Pi
  • Ana Cristina Bicharra Garcia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9648)


Multi-objective optimization evolutionary algorithms have been applied to solve many real-life decision problems. Most of them require the management of trade-offs between multiple objectives. Reference point approaches highlight a preferred set of solutions in relevant areas of Pareto frontier and support the decision makers to take more confidence evaluation. This paper extends some well-known algorithms to work with collective preferences and interactive techniques. In order to analyse the results driven by the online reference points, two new performance indicators are introduced and tested against some synthetic problem.


Collective intelligence Preferences Reference points Evolutionary multi-objective optimization algorithms 



This work was partially funded by CNPq BJT Project 407851/2012-7, FAPERJ APQ1 Project 211.500/2015, FAPERJ APQ1 Project 211.451/2015.


  1. 1.
    Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181(3), 1653–1669 (2007)CrossRefzbMATHGoogle Scholar
  2. 2.
    Coello, C.C., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, New York (2007)zbMATHGoogle Scholar
  3. 3.
    Conover, W.: Practical Nonparametric Statistics. Wiley, New York (1999)Google Scholar
  4. 4.
    Das, I., Dennis, J.E.: Normal-boundary intersection: a new method for generating the pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optim. 8(3), 631–657 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Deb, K., Kumar, A.: Light beam search based multi-objective optimization using evolutionary algorithms. In: IEEE Congress on Evolutionary Computation, 2007, CEC 2007, pp. 2125–2132. IEEE (2007)Google Scholar
  6. 6.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  7. 7.
    Deb, K., Sundar, J., Udaya Bhaskara Rao, N., Chaudhuri, S.: Reference point based multi-objective optimization using evolutionary algorithms. Int. J. Comput. Intell. Res. 2(3), 273–286 (2006)MathSciNetGoogle Scholar
  8. 8.
    Grinstead, C.M., Snell, J.L.: Introduction to Probability. American Mathematical Soc., Providence (2012)zbMATHGoogle Scholar
  9. 9.
    Malone, T.W., Laubacher, R., Dellarocas, C.: Harnessing crowds: mapping the genome of collective intelligence (2009)Google Scholar
  10. 10.
    Martınez, S.Z., Coello, C.A.C.: An archiving strategy based on the convex hull of individual minima for MOEAs (2010)Google Scholar
  11. 11.
    Shan-Fan, J., Xiong, S.W., Zhuo-Wang, J.: The multi-objective differential evolution algorithm based on quick convex hull algorithms. In: Fifth International Conference on Natural Computation, 2009, ICNC 2009, vol. 4, pp. 469–473. IEEE (2009)Google Scholar
  12. 12.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Daniel Cinalli
    • 1
    Email author
  • Luis Martí
    • 1
  • Nayat Sanchez-Pi
    • 2
  • Ana Cristina Bicharra Garcia
    • 1
  1. 1.Universidade Federal FluminenseRio de JaneiroBrazil
  2. 2.Universidade do Estado do Rio de JaneiroRio de JaneiroBrazil

Personalised recommendations