Clustering with Differential Evolution Particle Swarm Optimization
The applications of recently developed meta-heuristics in cluster analysis, such as particle swarm optimization (PSO) and differential evolution (DE), have increasingly attracted attention and popularity in a wide variety of communities owing to their effectiveness in solving complicated combinatorial optimization problems. Here, we propose to use a hybrid of PSO and DE, known as differential evolution particle swarm optimization (DEPSO), in order to further improve search capability and achieve higher flexibility in exploring the natural while hidden data structures of data of interest. Empirical results show that the DEPSO-based clustering algorithm achieves better performance in terms of the number of epochs required to reach a pre-specified cutoff value of the fitness function than either of the other approaches used. Further experimental studies on both synthetic and real data sets demonstrate the effectiveness of the proposed method in finding meaningful clustering solutions.
R. Xu et al., "Clustering with Differential Evolution Particle Swarm Optimization," Proceedings of the 2010 IEEE World Congress on Computational Intelligence / 2010 IEEE Congress on Evolutionary Computation (2010, Barcelona, Spain), Institute of Electrical and Electronics Engineers (IEEE), Jul 2010.
The definitive version is available at https://doi.org/10.1109/CEC.2010.5586257
6th IEEE World Congress on Computational Intelligence, WCCI 2010 / 2010 IEEE Congress on Evolutionary Computation, CEC 2010 (2010: Jul. 18-23, Barcelona, Spain)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2010 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
23 Jul 2010