Title of article :
Particle swarm optimization with selective particle regeneration for data clustering
Author/Authors :
Tsai، نويسنده , , Chi-Yang and Kao، نويسنده , , I-Wei، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2011
Abstract :
This paper presents selective regeneration particle swarm optimization (SRPSO), a novel algorithm developed based on particle swarm optimization (PSO). It contains two new features, unbalanced parameter setting and particle regeneration operation. The unbalanced parameter setting enables fast convergence of the algorithm and the particle regeneration operation allows the search to escape from local optima and explore for better solutions. This algorithm is applied to data clustering problems for performance evaluation and a hybrid algorithm (KSRPSO) of K-means clustering method and SRPSO is developed. In the conducted numerical experiments, SRPSO and KSRPSO are compared to the original PSO algorithm, K-means, as well as, other methods proposed by other studies. The results demonstrate that SRPSO and KSRPSO are efficient, accurate, and robust methods for data clustering problems.
Keywords :
data clustering , particle swarm optimization , K-Means algorithm
Journal title :
Expert Systems with Applications
Journal title :
Expert Systems with Applications