Towards an Intelligent Decision Making Support

  • Nesrine Ben Yahia
  • Narjès Bellamine
  • Henda Ben Ghezala
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 182)


This paper presents an intelligent framework that combines case-based reasoning (CBR), fuzzy logic and particle swarm optimization (PSO) to build an intelligent decision support model. CBR is a useful technique to support decision making (DM) by learning from past experiences. It solves a new problem by retrieving, reusing, and adapting past solutions to old problems that are closely similar to the current problem. In this paper, we combine fuzzy logic with case-based reasoning to identify useful cases that can support the DM. At the beginning, a fuzzy CBR based on both problems and actors’ similarities is advanced to measure usefulness of past cases. Then, we rely on a meta-heuristic optimization technique i.e. Particle Swarm Optimization to adjust optimally the parameters of the inputs and outputs fuzzy membership functions.


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  1. 1.
    Simon, H.: The New science of management decision. Prentice Hall, Englewood Cliffs (1977)Google Scholar
  2. 2.
    Zaraté, P.: Des Systèmes Interactifs d’Aide la Décision Aux Systèmes Coopératifs d’Aide la Décision: Contributions conceptuelles et fonctionnelles. HDR dissertation, INP Toulouse (2005)Google Scholar
  3. 3.
    Riesbeck, C.K., Schank, R.C.: Inside Case-Based Reasoning. Lawrence Erlbaum Associates, New Jersey (1989)Google Scholar
  4. 4.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and system approaches. AI Communications 7, 39–59 (1994)Google Scholar
  5. 5.
    Main, J., Dillon, T.S., Khosla, R.: Use of fuzzy feature vectors and neural vectors for case retrieval in case based systems. In: Biennial Conference of the North American Fuzzy Information Processing Society, NAFIPS 1996, pp. 438–443. IEEE, New York (1996)CrossRefGoogle Scholar
  6. 6.
    Zadeh, L.A.: Fuzzy Logic = Computing with Words. IEEE Transactions on Fuzzy Systems 4(2), 103–111 (1996)MathSciNetCrossRefGoogle Scholar
  7. 7.
    ShengZhou, Y., Lai, L.Y.: Optimal design for fuzzy controllers by genetic algorithms. IEEE Transactions on Industry Applications 36(1), 93–97 (2000)CrossRefGoogle Scholar
  8. 8.
    Parsopoulos, K.E., Vrahatis, M.N.: Particle Swarm Optimization and Intelligence: Advances and Applications. Information Science Reference (an imprint of IGI Global), United States of America (2010)CrossRefGoogle Scholar
  9. 9.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, California (1993)Google Scholar
  10. 10.
    Jeng, B.C., Liang, T.P.: Fuzzy indexing and retrieval in case-based systems. Expert Systems with Applications 8(1), 135–142 (1995)CrossRefGoogle Scholar
  11. 11.
    Eberhart, R.C., Kennedy, J.: New optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43 (1995)Google Scholar
  12. 12.
    Yisu, J., Knowles, J., Hongmei, L., Yizeng, L., Kell, D.B.: The Landscape Adaptive Particle Swarm Optimizer. Applied Soft Computing 8, 295–304 (2008)CrossRefGoogle Scholar
  13. 13.
    Clerc, M.: The Swarm and the Queen: Towards A Deterministic and Adaptive Particle Swarm Optimization. In: Proceedings of the Congress of Evolutionary Computation, Washington, DC, pp. 1951–1957 (1999)Google Scholar
  14. 14.
    Carlisle, A., Dozier, G.: An Off-The-Shelf PSO. In: Proceedings of the Particle Swarm Optimization Workshop, Indianapolis, Ind., USA, pp. 1–6 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nesrine Ben Yahia
    • 1
  • Narjès Bellamine
    • 1
  • Henda Ben Ghezala
    • 1
  1. 1.RIADI LaboratoryNational School of Computer Sciences, University Campus ManoubaManoubaTunisia

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