Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Finding Optimal Discretization Orders for Molecular Distance Geometry by Answer Set Programming Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

InterCriteria Analysis of ACO and GA Hybrid Algorithms

verfasst von : Olympia Roeva, Stefka Fidanova, Marcin Paprzycki

Erschienen in: Recent Advances in Computational Optimization

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel A Review on the Direct and Indirect Methods for Solving Optimal Control Problems with Differential-Algebraic Constraints
Nächstes Kapitel A Two-Stage Look-Ahead Heuristic for Packing Spheres into a Three-Dimensional Bin of Minimum Length
Literatur
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)
2.
S. AlMuhaideb, M. El, B. Menai, A new hybrid metaheuristic for medical data classification. Int. J. Metaheuristics 3(1), 59–80 (2014)
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
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)
5.
K. Atanassov, E. Szmidt, J. Kacprzyk, In intuitionistic fuzzy pairs. Notes Intuitionistic Fuzzy Sets 19(3), 1–13 (2013)
6.
K. Atanassov, Generalized index matrices. C. R. Acad. Bulg. Sci. 40(11), 15–18 (1987)
7.
K. Atanassov, On index matrices, part 1: standard cases. Adv. Stud. Contemp. Math. 20(2), 291–302 (2010)
8.
K. Atanassov, On index matrices, part 2: intuitionistic fuzzy case. Proc. Jangjeon Math. Soc. 13(2), 121–126 (2010)
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)
10.
V. Atanassova, L. Doukovska, K. Atanassov, D. Mavrov, InterCriteria decision making approach to EU member states competitiveness analysis. BMSD 289–294 (2014)
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
12.
V. Atanassova, I. Vardeva, Sum- and average-based approach to criteria shortlisting in the InterCriteria analysis. Notes IFS 20(4), 41–46 (2014)
13.
G. Bastin, D. Dochain, On-line Estimation and Adaptive Control of Bioreactors (Elsevier Science Publisher, Amsterdam, 1991)
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)MathSciNetCrossRefMATH
15.
C. Blum, A. Roli, Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)CrossRef
16.
E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems (Oxford University Press, New York, 1999)MATH
17.
18.
A. Csebfalv, A hybrid metaheuristic method for continuous engineering optimization. Civ. Eng. 53(2), 93–100 (2009)
19.
M. Dorigo, T. Stutzle, Ant Colony Optimization (MIT Press, Cambridge, 2004)
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
21.
22.
A. Georgieva, I. Jordanov, Hybrid metaheuristics for global optimization using low-discrepancy sequences of points. Comput. Oper. Res. 37(3), 456–469 (2010)MathSciNetCrossRef
23.
F. Glover, G. Kochenberger (eds.), Handbook of Metaheuristics. International Series in Operations Research and Management Science, vol. 57 (Kluwer Academic Publishers, Boston, 2003)
24.
D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison Wesley Longman, London, 2006)
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
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
27.
N. Harvey, Use of heuristics: insights from forecasting research. Think. Reason. 13(1), 5–24 (2007)CrossRef
28.
J.H. Holland, Adaptation in Natural and Artificial Systems, 2nd edn. (MIT Press, Cambridge, 1992)
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
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)
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)
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
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
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
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)
36.
E.G. Talbi (ed.), Hybrid Metaheuristics. Studies in Computational Intelligence, vol. 434 (Springer, Berlin, 2013) (XXVI, 458 p. 109 illus)
37.
E.G. Talbi, A taxonomy of hybrid metaheuristics. J. Heuristics 8, 541–564 (2002)CrossRef
38.
J. Toutouh, Metaheuristics for Optimal Transfer of P2P Information in VANETs, MSc Ph.D. thesis, University of Luxembourg, 2010
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
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)
Metadaten
Titel
InterCriteria Analysis of ACO and GA Hybrid Algorithms
verfasst von
Olympia Roeva
Stefka Fidanova
Marcin Paprzycki
Copyright-Jahr
2016
Buch
Recent Advances in Computational Optimization

Print ISBN: 978-3-319-21132-9

Electronic ISBN: 978-3-319-21133-6

Copyright-Jahr: 2016

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

Premium Partner

    Bildnachweise