An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis

By Taher Niknam, Babak Amiri. Applied Soft Computing, 2010

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Improved salp swarm algorithm based on particle swarm optimization for feature selection (2018, methodology): “(4) and (5) represent the method that is used to update the particles positions (Niknam and Amiri 2010; Noman et al. 2009). where x ij is the i -th particle position in the j -th dimension, v ij is the i -th velocity in the j -th dimension, t defines the current iteration, w indicates an inertia…”
Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives (2018, methodology): “Kaveh and Talatahari [105] 2009 DHPSACO Truss structures with discrete variables Kaveh and Talatahari [106] 2009 HPSACO Truss structures Niknam and Amiri [107] 2010 FAPSO-ACO-K Cluster analysis Chen et al.”
Black hole: A new heuristic optimization approach for data clustering (2013, methodology): “For instance, simulated annealing [33], tabu search [58], genetic algorithms [57,62,63,66], ant colony optimization [28,67,89], neural gas algorithm [72–74], honey bee mating optimization [23], differential evolution algorithm [15,16], particle swarm optimization algorithm [1,44,54], artificial bee ”