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Recent Advances in Computational Optimization pp 107–126Cite as

InterCriteria Analysis of ACO and GA Hybrid Algorithms

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 610))

Abstract

In this paper, the recently proposed approach for multicriteria decision making—InterCriteria Analysis (ICA)—is presented. The approach is based on the apparatus of the index matrices and the intuitionistic fuzzy sets. The idea of InterCriteria Analysis is applied to establish the relations and dependencies of considered parameters based on different criteria referred to various metaheuristic algorithms. A hybrid scheme using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) is used for parameter identification of E. coli MC4110 fed-batch cultivation process model. In the hybrid GA-ACO, the GA is used to find feasible solutions to the considered optimization problem. Further ACO exploits the information gathered by GA. This process obtains a solution, which is at least as good as—but usually better than—the best solution devised by GA. Moreover, a comparison with both the conventional GA and ACO identification results is presented. Based on ICA the obtained results are examined and conclusions about existing relations and dependencies between model parameters of the E. coli process and algorithms parameters and outcomes, such as number of individuals, number of generations, value of the objective function and computational time, are discussed.

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References

  1. A. Acan, A GA + ACO hybrid for faster and better search capability, in Ant Algorithms: Proceedings of the Third International Workshop, ANTS 2002. Lecture Notes in Computer Science (2002)

    Google Scholar 

  2. S. AlMuhaideb, M. El, B. Menai, A new hybrid metaheuristic for medical data classification. Int. J. Metaheuristics 3(1), 59–80 (2014)

    Google Scholar 

  3. M. Arndt, B. Hitzmann, Feed forward/feedback control of glucose concentration during cultivation of Escherichia coli, in 8th IFAC International Canada, Conference on Computer Applications in Biotechnology, Canada, 2001, pp. 425–429

    Google Scholar 

  4. K. Atanassov, D. Mavrov, V. Atanassova, InterCriteria decision making: a new approach for multicriteria decision making, based on index matrices and intuitionistic fuzzy sets. Issues IFSs GNs 11, 1–8 (2014)

    Google Scholar 

  5. K. Atanassov, E. Szmidt, J. Kacprzyk, In intuitionistic fuzzy pairs. Notes Intuitionistic Fuzzy Sets 19(3), 1–13 (2013)

    Google Scholar 

  6. K. Atanassov, Generalized index matrices. C. R. Acad. Bulg. Sci. 40(11), 15–18 (1987)

    Google Scholar 

  7. K. Atanassov, On index matrices, part 1: standard cases. Adv. Stud. Contemp. Math. 20(2), 291–302 (2010)

    Google Scholar 

  8. K. Atanassov, On index matrices, part 2: intuitionistic fuzzy case. Proc. Jangjeon Math. Soc. 13(2), 121–126 (2010)

    Google Scholar 

  9. V. Atanassova, D. Mavrov, L. Doukovska, K. Atanassov, Discussion on the threshold values in the InterCriteria decision making approach. Notes IFS 20(2), 94–99 (2014)

    Google Scholar 

  10. V. Atanassova, L. Doukovska, K. Atanassov, D. Mavrov, InterCriteria decision making approach to EU member states competitiveness analysis. BMSD 289–294 (2014)

    Google Scholar 

  11. V. Atanassova, L. Doukovska, D. Karastoyanov, F. Capkovic, InterCriteria decision making approach to EU member states competitiveness analysis: trend analysis. Proceedings of IEEE (2014), pp. 107–115

    Google Scholar 

  12. V. Atanassova, I. Vardeva, Sum- and average-based approach to criteria shortlisting in the InterCriteria analysis. Notes IFS 20(4), 41–46 (2014)

    Google Scholar 

  13. G. Bastin, D. Dochain, On-line Estimation and Adaptive Control of Bioreactors (Elsevier Science Publisher, Amsterdam, 1991)

    Google Scholar 

  14. M. Battarra, A.A. Pessoa, A. Subramanian, E. Uchoa, Exact algorithms for the traveling salesman problem with draft limits. Eur. J. Oper. Res. 235(1), 115–128 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  15. C. Blum, A. Roli, Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)

    Article  Google Scholar 

  16. E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems (Oxford University Press, New York, 1999)

    MATH  Google Scholar 

  17. I. Boussaid, J. Lepagnot, P. Siarry, A survey on optimization metaheuristics. Inf. Sci. 237, 82–117 (2013)

    Article  MathSciNet  Google Scholar 

  18. A. Csebfalv, A hybrid metaheuristic method for continuous engineering optimization. Civ. Eng. 53(2), 93–100 (2009)

    Google Scholar 

  19. M. Dorigo, T. Stutzle, Ant Colony Optimization (MIT Press, Cambridge, 2004)

    Google Scholar 

  20. I. Dumitrescu, T. Sttzle, Combinations of local search and exact algorithms, in Applications of Evolutionary Computation, ed. by G.R. Raidl et al. Lecture Notes in Computer Science, vol. 2611 (Springer 2003), pp. 211–223

    Google Scholar 

  21. Genetic Algorithms, http://www.doc.ic.ac.uk/nd/surprise_96/journal/vol1/hmw/article1.html. Accessed 14 Jan 2015

  22. A. Georgieva, I. Jordanov, Hybrid metaheuristics for global optimization using low-discrepancy sequences of points. Comput. Oper. Res. 37(3), 456–469 (2010)

    Article  MathSciNet  Google Scholar 

  23. F. Glover, G. Kochenberger (eds.), Handbook of Metaheuristics. International Series in Operations Research and Management Science, vol. 57 (Kluwer Academic Publishers, Boston, 2003)

    Google Scholar 

  24. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison Wesley Longman, London, 2006)

    Google Scholar 

  25. H. Guangdong, P. Ling, Q. Wang, A hybrid metaheuristic ACO-GA with an application in sports competition scheduling, in Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, vol. 3, (2007), pp. 611–616

    Google Scholar 

  26. H. Guangdong, Q. Wang, in Chapter 7, A Hybrid ACO-GA on Sports Competition Schedulingby, Ant 451 Colony Optimization—Methods and Applications, ed. by A. Ostfeld, (InTech, 2011), pp. 89–100

    Google Scholar 

  27. N. Harvey, Use of heuristics: insights from forecasting research. Think. Reason. 13(1), 5–24 (2007)

    Article  Google Scholar 

  28. J.H. Holland, Adaptation in Natural and Artificial Systems, 2nd edn. (MIT Press, Cambridge, 1992)

    Google Scholar 

  29. M. Lukasiewycz, M. Gla, F. Reimann, J. Teich, Opt4J—a modular framework for metaheuristic optimization, in Proceedings of the Genetic and Evolutionary Computing Conference (GECCO 2011), Dublin, Ireland, (2011), pp. 1723–1730

    Google Scholar 

  30. S. Masrom, S.Z.Z. Abidin, P.N. Hashimah, A.S. Abd, Rahman, Towards rapid development of user defined metaheuristics hybridisation. Int. J. Softw. Eng. Appl. 5, 1–12 (2011)

    Google Scholar 

  31. O. Roeva, T. Pencheva, B. Hitzmann, St. Tzonkov, A genetic algorithms based approach for identification of escherichia coli fed-batch fermentation. Int. J. Bioautomation 1, 30–41 (2004)

    Google Scholar 

  32. O. Roeva, S. Fidanova, Chapter 13. a comparison of genetic algorithms and ant colony optimization for modeling of E. coli cultivation process, in Real-World Application of Genetic Algorithms, ed. by O. Roeva (InTech, 2012), pp. 261–282

    Google Scholar 

  33. O. Roeva, Improvement of Genetic Algorithm Performance for Identification of Cultivation Process Models. Advanced Topics on Evolutionary Computing, Book Series: Artificial Intelligence Series—WSEAS (WSEAS Press, Bulgaria, 2008), pp. 34–39

    Google Scholar 

  34. O. Roeva, S. Fidanova, V. Atanassova, Hybrid ACO-GA for parameter identification of an E. coli cultivation process model, in Large-Scale Scientific Computing. Lecture Notes in Computer Science, vol. 8353 (Springer, Germany, 2014), pp. 288–295. ISSN 0302–9743

    Google Scholar 

  35. H. Smith, Use of the anchoring and adjustment heuristic by children. Curr. Psychol. J. Divers. Perspect. Divers. Psychol. Issues 18(3), 294–300 (1999)

    Google Scholar 

  36. E.G. Talbi (ed.), Hybrid Metaheuristics. Studies in Computational Intelligence, vol. 434 (Springer, Berlin, 2013) (XXVI, 458 p. 109 illus)

    Google Scholar 

  37. E.G. Talbi, A taxonomy of hybrid metaheuristics. J. Heuristics 8, 541–564 (2002)

    Article  Google Scholar 

  38. J. Toutouh, Metaheuristics for Optimal Transfer of P2P Information in VANETs, MSc Ph.D. thesis, University of Luxembourg, 2010

    Google Scholar 

  39. G.J. Woeginger, Exact Algorithms for NP-Hard Problems: A Survey. Lecture Notes in Computer Science, vol. 2570 (Springer, Berlin, 2003), pp. 185–207

    Google Scholar 

  40. H. Yi, Q. Duan, T. Warren Liao, Three improved hybrid metaheuristic algorithms for engineering design optimization. Appl. Soft Comput. 13(5), 2433–2444 (2013)

    Google Scholar 

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Acknowledgments

Work presented here is a part of the Poland-Bulgarian collaborative Grant “Parallel and distributed computing practices” and the Bulgarian National Scientific Fund under the grants DFNI-I02/20 “Efficient ParallelAlgorithms for Large Scale Computational Problems and DFNI-I02/5 InterCriteria Analysis”. A New Approach to Decision Making.

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Authors and Affiliations

  1. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Science, Sofia, Bulgaria

    Olympia Roeva

  2. Institute of Information and Communication Technology, Bulgarian Academy of Science, Sofia, Bulgaria

    Stefka Fidanova

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw and Management Academy, Warsaw, Poland

    Marcin Paprzycki

Authors
  1. Olympia Roeva
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  2. Stefka Fidanova
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  3. Marcin Paprzycki
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Correspondence to Stefka Fidanova .

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Editors and Affiliations

  1. Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Stefka Fidanova

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Roeva, O., Fidanova, S., Paprzycki, M. (2016). InterCriteria Analysis of ACO and GA Hybrid Algorithms. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-319-21133-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-21133-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21132-9

  • Online ISBN: 978-3-319-21133-6

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