An advanced ACO algorithm for feature subset selection

By Shima Kashef, Hossein Nezamabadi-pour. Neurocomputing, 2015

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An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (2018, methodology): “P T In addition, the results of the BSSA_S3_CP approach is also compared to the results of the bGWO1, bGWO2, GA, and PSO techniques reported in [46].”
Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection (2019, methodology): “In [40] and [41], two hybrids were proposed using ACO and GA to perform feature selection.”
Swarm Intelligence Algorithms for Feature Selection: A Review (2018, methodology): “ABC [38,41] DA [46] ABC [54] 11 BBMO [44,63] GSO [50] FA [42] ACO [35] ACO [62] WSO [73] Bacteria BFO and BCO [64] 1 Bird PSO [29,31] PSO [66,74,75] PSO [28,30,76] 10 [77] [78] Mammal GWO [39] GWO [34,40] 6 CaTS [65] BA [55] WOA [67] Fish AFSA [51,52] 3 FSA [79] Frog SFLA [45] 1”
A hybrid approach of differential evolution and artificial bee colony for feature selection (2016, methodology): “D Moreover, ABC-DE is compared with the advanced ACO method proposed by Kashef and Nezamabadi-pour (2015) by using two common datasets.”
A Survey on Evolutionary Computation Approaches to Feature Selection (2016, background): “Kashef and Nezamabadi-Pour [191], [192] also proposed a new representation in which each feature had two nodes; one for selecting that feature and the other for removing it.”