Differential evolution and particle swarm optimisation in partitional clustering

  • Authors:

  • Sandra Paterlini;Thiemo Krink

  • Affiliations:

  • Dipartimento di Economia Politica, Univ. of Modena and Reggio E., Viale Berengario 51, 41100 Modena, Italy;EVALife group, Dept. of Computer Science, Univ. of Aarhus, Aabogade 34, 8200 Aarhus N, Denmark

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2006

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Abstract

Many partitional clustering algorithms based on genetic algorithms (GA) have been proposed to tackle the problem of finding the optimal partition of a data set. Very few studies considered alternative stochastic search heuristics other than GAs or simulated annealing. Two promising algorithms for numerical optimisation, which are hardly known outside the search heuristics field, are particle swarm optimisation (PSO) and differential evolution (DE). The performance of GAs for a representative point evolution approach to clustering is compared with PSO and DE. The empirical results show that DE is clearly and consistently superior compared to GAs and PSO for hard clustering problems, both with respect to precision as well as robustness (reproducibility) of the results. Only for simple data sets, the GA and PSO can obtain the same quality of results. Apart from superior performance, DE is easy to implement and requires hardly any parameter tuning compared to substantial tuning for GAs and PSOs. Our study shows that DE rather than GAs should receive primary attention in partitional clustering algorithms.