TY - GEN
T1 - Chaotic artificial bee colony used for cluster analysis
AU - Zhang, Yudong
AU - Wu, Lenan
AU - Wang, Shuihua
AU - Huo, Yuankai
PY - 2011
Y1 - 2011
N2 - 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 overlapping all demonstrate the CABC is superior to both genetic algorithm (GA) and combinatorial particle swarm optimization (CPSO) in terms of robustness and computation time.
AB - 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 overlapping all demonstrate the CABC is superior to both genetic algorithm (GA) and combinatorial particle swarm optimization (CPSO) in terms of robustness and computation time.
KW - Rossler attractor
KW - artificial bee colony
KW - partitional clustering
KW - variance ratio criterion
UR - http://www.scopus.com/inward/record.url?scp=79952793932&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-18129-0_33
DO - 10.1007/978-3-642-18129-0_33
M3 - Conference Proceeding
AN - SCOPUS:79952793932
SN - 9783642181283
T3 - Communications in Computer and Information Science
SP - 205
EP - 211
BT - Intelligent Computing and Information Science - International Conference, ICICIS 2011, Proceedings
A2 - Chen, Ran
T2 - 2011 International Conference on Intelligent Computing and Information Science, ICICIS 2011
Y2 - 8 January 2011 through 9 January 2011
ER -