Collaborative Artificial Bee Colony Optimization Clustering Using SPNN

A Review on Artificial Bee Colony Algorithms and Their Applications to Data Clustering

“…The two-stage expert system produced better and efficient solutions. S h a n t h i and A m a l r a j [180] proposed a hybrid approach with combination of ABC and Harmonic Search algorithms. The modified C-ABC algorithm is able to find the local optimal solutions and has been used to develop a new clustering method for neural networks.…”

A Multiple Pheromone Ant Clustering Algorithm

“…BCO is applied to clustering problems [Shanthi and Amalraj, 2012], [Nesamalar and Chandran, 2012], [Oleynik et al, 2010]. Notwithstanding, the lack of stigmergy and the more intricate bee-to-bee communication mechanisms than exist in ants, both BCO and ACO algorithms are composed of multiple homogeneous individuals which interact locally on a set of simple rules.…”

Effect of classification by competitive neural network on reconstruction of reflectance spectra using principal component analysis

“…Such networks can detect the correlations in their input and organize groups of similar input vectors. The classification of inputs by a competitive neural networks is dependent only on the distance between input vectors and there is no strict mechanism in a competitive layer design …”

“…The classification of inputs by a competitive neural networks is dependent only on the distance between input vectors and there is no strict mechanism in a competitive layer design. 31,32 In this research, a new method is proposed for reconstruction of reflectance spectra from CIEXYZ tristimulus values. First, the reflectance spectra of training data were divided according to CIELAB colorimetric coordinate into 3, 6, 9, and 12 subgroups by a competitive neural network.…”