Clustering using firefly algorithm: Performance study

By J. Senthilnath, S. N. Omkar, V. Mani. Swarm and Evolutionary Computation, 2011

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A comprehensive review of firefly algorithms (2013, background): “In paper [132], Senthilnath et al. applied FA for clustering data objects into groups according to the values of their attributes.”
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 ”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, methodology): “Firefly algorithm –”
Firefly Algorithm: Recent Advances and Applications (2013, methodology): “Classifications and clustering are another important area of applications of FA with excellent performance [31, 28].”
Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm (2016, unknown): “Researchers have shown good performance of metaheuristic algorithms in a wide range of complex problems such as scheduling problems [1–6], data clustering [7,8], image and video processing [9–12], tuning of neural networks [13–15] and pattern recognition [16–18], etc.”