Differential Evolution and Particle Swarm Optimization in Partitional Clustering
published: Feb. 25, 2007, recorded: December 2003, views: 2653
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In recent years, 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. Surprisingly, very few studies considered alternative stochastic search heuristics other than GAs or simulated annealing. Two promising algorithms for numerical optimization, which are hardly known outside the heuristic search field, are particle swarm optimisation (PSO) and differential evolution (DE). In this study, we compared the performance of GAs with PSO and DE for a medoid evolution approach to clustering. Moreover, we compared these results with the nominal classification, k-means and random search (RS) as a lower bound. Our results show that DE is clearly and consistently superior compared to GAs and PSO for hard clustering problems, both in respect to precision as well as robustness (reproducibility) of the results. Only for trivial problems all algorithms can obtain comparable results. Apart from superior performance, DE is very 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 cluster algorithms.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !