Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/5314
Title: Hybrid whale optimization algorithm with simulated annealing for feature selection
Authors: Mafarja, Majdi
Mirjalili, Seyedali
Keywords: Mathematical optimization;Operations research;Data mining;Simulated annealing (Mathematics);Artificial intelligence;Algorithms;Database management;Separation of variables;Whale optimization algorithm
Issue Date: 10-May-2017
Publisher: Elsevier
Source: Majdi M. Mafarja, Seyedali Mirjalili, Hybrid Whale Optimization Algorithm with simulated annealing for feature selection, In Neurocomputing, Volume 260, 2017, Pages 302-312, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2017.04.053.
Abstract: Hybrid metaheuristics are of the most interesting recent trends in optimization and memetic algorithms. In this paper, two hybridization models are used to design different feature selection techniques based on Whale Optimization Algorithm (WOA). In the first model, Simulated Annealing (SA) algorithm is embedded in WOA algorithm, while it is used to improve the best solution found after each iteration of WOA algorithm in the second model. The goal of using SA here is to enhance the exploitation by searching the most promising regions located by WOA algorithm. The performance of the proposed approaches is evaluated on 18 standard benchmark datasets from UCI repository and compared with three well-known wrapper feature selection methods in the literature. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which insures the ability of WOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks.
Description: • Four hybrid feature selection methods for classification task are proposed. • Our hybrid method combines Whale Optimization Algorithm with simulated annealing. • Eighteen UCI datasets were used in the experiments. • Our approaches result a higher accuracy by using less number of features.
URI: http://hdl.handle.net/20.500.11889/5314
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