A particle swarm optimization approach to clustering

Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems

“…Common features of these methods with interaction between populations are that they are populationbased and nature-inspired. These algorithms have been adopted by researchers so far and are well suited to solve various complex computational problems such as multi-objective of objective functions (Akbari et al, 2012;Zhou et al, 2011), flow shop scheduling problem (Bank et al, 2012), pattern recognition (Prasartvit et al, 2013;Yu and Duan, 2013), feature selection (Chen et al, 2013;Jung and Zscheischler, 2013;Kabir et al, 2012;Xue et al, 2013) and data clustering Chuang et al, 2011;Cura, 2012;Karaboga and Ozturk, 2011;Liu et al, 2011;Niknam et al, 2011;Yan et al, 2012).…”

An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

“…For instance, (Fathian et al, 2007) propose a honey-regarding bee-mating optimization; (Chen and Ye, 2004) and (Cura, 2012) propose a particle swarm optimization based aprorach; (Hatamlou, 2013) proposes a black hole optimization algorithm; and (Krishnasamy et al, 2014) propose a hybrid approach based on modified cohort intelligence and k-means.…”

A Selection Process for Genetic Algorithm Using Clustering Analysis

“…Some relevant studies that have explored the problem of clustering using various approaches include evolutionary algorithms such as evolutionary programing [9], particle swarm optimization [10][11][12], ant colony algorithms [13,14], artificial bee colony [15], simulated annealing [16,17] and tabu search [18]. Conversely, there have been many attempts to use GAs to solve clustering applications [7,[19][20][21][22][23][24][25][26][27].…”