Academy & Industry Research Collaboration Center
K-means Fast Learning Artificial Neural Network (K-FLANN) algorithm begins with the initialization of two parameters vigilance and tolerance which are the key to get optimal clustering outcome. The optimization task is to change these parameters so a desired mapping between inputs and outputs (clusters) of the KFLANN is achieved. This paper presents finding the behavioral parameters of K-FLANN that yield good clustering performance using an optimization method known as differential evolution. DE algorithm is a simple efficient meta-heuristic for global optimization over continuous spaces.