title: Impact of Chaos Functions on Modern Swarm Optimizers.
abstract: Exploration and exploitation are two essential components for any optimization algorithm. Much exploration leads to oscillation and premature convergence while too much exploitation slows down the optimization algorithm and the optimizer may be stuck in local minima. Therefore, balancing the rates of exploration and exploitation at the optimization lifetime is a challenge. This study evaluates the impact of using chaos-based control of exploration/exploitation rates against using the systematic native control. Three modern algorithms were used in the study namely grey wolf optimizer (GWO), antlion optimizer (ALO) and moth-flame optimizer (MFO) in the domain of machine learning for feature selection. Results on a set of standard machine learning data using a set of assessment indicators prove advance in optimization algorithm performance when using variational repeated periods of declined exploration rates over using systematically decreased exploration rates.
authors.affiliation: Department of Information Technology, Faculty of Computers and Information, Cairo University, Cairo, Egypt.|Computing Department, Faculty of Computer Studies, Arab Open University, Shrouq, Egypt.
authors.forename: E
authors.initials: E
authors.lastname: Emary
authors.affiliation: Department of Information Technology, Faculty of Computers and Information, Beni-Suef University, Beni-Suef, Egypt.|Department of Computer Science, Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania.
authors.forename: Hossam M
authors.identifier: http://orcid.org/0000-0001-6548-2993
authors.initials: HM
authors.lastname: Zawbaa
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008124:Locomotion; D008954:Models, Biological; D017711:Nonlinear Dynamics; D011897:Random Allocation