InterCriteria Analysis of Relations Between Model Parameters Estimates and ACO Performance

  • Olympia Roeva
  • Stefka FidanovaEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 681)


In this paper we apply the approach InterCriteria Analysis (ICrA) to establish the existing relations and dependencies of defined parameters in non-linear model of an E. coli fed-batch fermentation process. Moreover, based on results of series of Ant Colony Optimization (ACO) identification procedures we observe the mutual relations between model parameters and ACO outcomes (execution time and objective function value). We perform a series of model identification procedures applying ACO. To estimate the model parameters we apply consistently 11 differently tuned ACO algorithms. We use various population sizes—from 5 to 100 ants in the population. In terms of ICrA we define five criteria, namely model parameters (maximum specific growth rate, \(\mu _{max}\); saturation constant, \(k_S\) and yield coefficient, \(Y_{S/X}\)) and ACO outcomes (execution time, T and objective function value, J). Based on ICrA we examine the obtained parameters estimates and discuss the conclusions about existing relations and dependencies between defined criteria. The obtained here results we compare with the ICrA results achieved using Genetic Algorithms (GA) as optimization techniques. Thus, based on the results of ACO and GA (the worst, best and average estimates) we define more precisely in which group (negative consonance, dissonance or positive consonance) fall the given ICrA criteria pairs.


Execution Time Criterion Pair Index Matrix Index Matrix Model Parameter Identification 
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.



Work presented here is partially supported by the Bulgarian National Scientific Fund under Grants DFNI-I02/5 InterCriteria Analysis. A New Approach to Decision Making and DFNI I02/20 Efficient Parallel Algorithms for Large Scale Computational Problems.


  1. 1.
    Angelova, M., Roeva, O., Pencheva, T.: InterCriteria analysis of crossover and mutation rates relations in simple genetic algorithm. Ann. Comput. Sci. Inf. Syst. 5, 419–424 (2015)CrossRefGoogle Scholar
  2. 2.
    Atanassov, K.: On index matrices, Part 1: Standard cases. Adv. Stud. Contemp. Math. 20(2), pp. 291–302 (2010)Google Scholar
  3. 3.
    Atanassov, K.: On index matrices, Part 2: Intuitionistic fuzzy case. In: Proceedings of the Jangjeon Mathematical Society, vol. 13, no. 2, pp. 121–126 (2010)Google Scholar
  4. 4.
    Atanassov, K.: On Intuitionistic Fuzzy Sets Theory. Springer, Berlin (2012)CrossRefzbMATHGoogle Scholar
  5. 5.
    Atanassov, K., Atanassova, V., Gluhchev, G.: InterCriteria analysis: ideas and problems. In: Notes on Intuitionistic Fuzzy Sets, vol. 21, no. 2, pp. 81–88 (2015)Google Scholar
  6. 6.
    Atanassov, K., Mavrov, D., Atanassova, V.: Intercriteria decision making: a new approach for multicriteria decision making, based on index matrices and intuitionistic fuzzy sets. Issues in IFSs and GNs 11, 1–8 (2014)Google Scholar
  7. 7.
    Atanassov, K., Szmidt, E., Kacprzyk, J.: On intuitionistic fuzzy pairs. In: Notes on Intuitionistic Fuzzy Sets, Vol. 19, no. 3, pp. 1–13 (2013)Google Scholar
  8. 8.
    Bastin, G., Dochain, D.: On-line estimation and adaptive control of bioreactors. Els. Sc. Publ. (1991)Google Scholar
  9. 9.
    Boussaid, I., Lepagnot, J., Siarry, P.: A survey on optimization metaheuristics. Inf. Sci. 237, 82–117 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press (2004)Google Scholar
  11. 11.
    Ilkova, T., Petrov, M.: Intercriteria analysis for identification of Escherichia coli fed-batch mathematical model. J. Int. Sci. Publications: Mater. Methods Technol. 9, 598–608 (2015)Google Scholar
  12. 12.
    Pencheva, T., Angelova, M., Atanassova, V., Roeva, O.: InterCriteria analysis of genetic algorithm parameters in parameter identification. In: Notes on Intuitionistic Fuzzy Sets, vol. 21, no. 2, pp. 99–110 (2015)Google Scholar
  13. 13.
    Pencheva, T., Angelova, M., Vassilev, P., Roeva, O.: InterCriteria analysis approach to parameter identification of a fermentation process model. Adv. Intell. Syst. Comput. 401, 385–397 (2016)CrossRefGoogle Scholar
  14. 14.
    Roeva, O., Fidanova, S., Paprzycki, M.: Influence of the population size on the genetic algorithm performance in case of cultivation process modelling. In: Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS), WCO 2013, Poland, pp. 371–376 (2013)Google Scholar
  15. 15.
    Roeva, O., Fidanova, S., Paprzycki, M.: InterCriteria analysis of ACO and GA hybrid algorithms. Stud. Comput. Intell. 610, 107–126 (2016)Google Scholar
  16. 16.
    Roeva, O., Fidanova, S., Vassilev, P., Gepner, P.: InterCriteria analysis of a model parameters identification using genetic algorithm. Ann. Comput. Sci. Inf. Syst. 5, 501–506 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of SciencesSofiaBulgaria
  2. 2.Institute of Information and Communication Technologies, Bulgarian Academy of SciencesSofiaBulgaria

Personalised recommendations