Abstract
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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References
Simon, H.: The New science of management decision. Prentice Hall, Englewood Cliffs (1977)
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)
Riesbeck, C.K., Schank, R.C.: Inside Case-Based Reasoning. Lawrence Erlbaum Associates, New Jersey (1989)
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and system approaches. AI Communications 7, 39–59 (1994)
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)
Zadeh, L.A.: Fuzzy Logic = Computing with Words. IEEE Transactions on Fuzzy Systems 4(2), 103–111 (1996)
ShengZhou, Y., Lai, L.Y.: Optimal design for fuzzy controllers by genetic algorithms. IEEE Transactions on Industry Applications 36(1), 93–97 (2000)
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)
Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, California (1993)
Jeng, B.C., Liang, T.P.: Fuzzy indexing and retrieval in case-based systems. Expert Systems with Applications 8(1), 135–142 (1995)
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)
Yisu, J., Knowles, J., Hongmei, L., Yizeng, L., Kell, D.B.: The Landscape Adaptive Particle Swarm Optimizer. Applied Soft Computing 8, 295–304 (2008)
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)
Carlisle, A., Dozier, G.: An Off-The-Shelf PSO. In: Proceedings of the Particle Swarm Optimization Workshop, Indianapolis, Ind., USA, pp. 1–6 (2001)
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Ben Yahia, N., Bellamine, N., Ben Ghezala, H. (2013). Towards an Intelligent Decision Making Support. In: Abraham, A., Thampi, S. (eds) Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32063-7_10
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DOI: https://doi.org/10.1007/978-3-642-32063-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32062-0
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