Differential evolution and particle swarm optimisation in partitional clustering
By Sandra Paterlini, Thiemo Krink. Computational Statistics & Data Analysis, 2006
321 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/f6a5735e9b2c839338016ab3bcab78d019f4d1cb
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Citations in the corpus
- A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, methodology): “Cluster analysis Fun and Chen (2005), Shen et al. (2005), Feng et al. (2006), Hongwen and Rui (2006), Paterlini and Krink (2006), Abraham et al. (2007), Ahmadi et al (2007), Kao et al. (2007a, b), Das et al. (2008), Díaz et al. (2008), Zhang et al. (2008b), Kao and Lee (2009), Herrera et al.…”
- A hybridized approach to data clustering (2008, background): “Recently, many clustering algorithms based on evolutionary computing such as genetic algorithms have been introduced, and only a couple of applications opted for particle swarm optimization (Paterlini and Krink, 2006).”