Abstract
A new approach based on artificial bee colony (ABC) with chaotic theory was proposed to solve the partitional clustering problem. We first investigate the optimization model including both the encoding strategy and the variance ratio criterion (VRC). Second, a chaotic ABC algorithm was developed based on the Rossler attractor. Experiments on three types of artificial data of different degrees of overlap** all demonstrate the CABC is superior to both genetic algorithm (GA) and combinatorial particle swarm optimization (CPSO) in terms of robustness and computation time.
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Zhang, Y., Wu, L., Wang, S., Huo, Y. (2011). Chaotic Artificial Bee Colony Used for Cluster Analysis. In: Chen, R. (eds) Intelligent Computing and Information Science. ICICIS 2011. Communications in Computer and Information Science, vol 134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18129-0_33
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DOI: https://doi.org/10.1007/978-3-642-18129-0_33
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