Abstract
In the Case-Based Reasoning (CBR) System, the retrieval efficiency and system performance are reduced because of the unlimited increasing case base with the incremental learning. This paper proposes the method of ant colony optimization (ACO) in the CBR system. This method combines the increased efficiency of case retrieval, the effective case base indexing, and the validity of maintenances by adding or reducing cases. Through the all processes we have used the clustering and classification algorithm based ACO. The implementation of the ACO algorithm into the CBR system is successful and the experimental results verify its effectiveness.
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Acknowledgment
We would like to thank the support of the National Natural Science Foundation of China under Grant No. 61272153 and No. 61170059.
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Shu, J. (2013). The Application of Ant Colony Optimization in CBR. In: Yin, Z., Pan, L., Fang, X. (eds) Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013. Advances in Intelligent Systems and Computing, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37502-6_143
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DOI: https://doi.org/10.1007/978-3-642-37502-6_143
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