Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems. Issue 1 (11th April 2018)

• Record Type: Journal Article
• Authors: Tawhid, Mohamed A.; Dsouza, Kevin B.
• Is Part Of: Applied computing and informatics. Volume 16:Issue 1/2(2020)
• Page Start: 117
• Page End: 136
• Publication Date: 2018-04-11
• Subjects: Particle Swarm Optimization -- Bat algorithm -- Binary Algorithms -- Hybridization -- Meta-heuristics -- Feature selection problem; Information science -- Periodicals; Information storage and retrieval systems -- Periodicals; 004
• DOI: 10.1016/j.aci.2018.04.001
• Languages: English
• ISSNs: 2210-8327

Abstract

Abstract : In this paper, we present a new hybrid binary version of bat and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed algorithm is called Hybrid Binary Bat Enhanced Particle Swarm Optimization Algorithm (HBBEPSO). In the proposed HBBEPSO algorithm, we combine the bat algorithm with its capacity for echolocation helping explore the feature space and enhanced version of the particle swarm optimization with its ability to converge to the best global solution in the search space. In order to investigate the general performance of the proposed HBBEPSO algorithm, the proposed algorithm is compared with the original optimizers and other optimizers that have been used for feature selection in the past. A set of assessment indicators are used to evaluate and compare the different optimizers over 20 standard data sets obtained from the UCI repository. Results prove the ability of the proposed HBBEPSO algorithm to search the feature space for optimal feature combinations.