The application of ant colony optimization in CBR

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. © Springer-Verlag Berlin Heidelberg 2013.