TY  - JOUR
AV  - none
SP  - 31662
PB  - Institute of Electrical and Electronics Engineers Inc.
EP  - 31677
ID  - scholars15697
TI  - Hybrid binary grey Wolf with Harris hawks optimizer for feature selection
KW  - Ball grid arrays; Benchmarking; Genetic algorithms; Nearest neighbor search; Particle swarm optimization (PSO)
KW  -  Balancing exploration and exploitations; Binary genetic algorithm; Binary particle swarm optimization; Computational time; Feature selection algorithm; K-nearest neighbors; Meta-heuristic methods; Sigmoid transfer function
KW  -  Feature extraction
SN  - 21693536
N2  - Despite Grey Wolf Optimizer's (GWO) superior performance in many areas, stagnation in local optima areas may still be a concern. Several significant GWO factors can be explored to enhance the performance of selection in classification, with two conflicting concepts to be considered in using or modeling a metaheuristic method, exploring a search field, and exploiting optimal solutions. Balancing exploration and exploitation in a good manner will improve the search algorithm's performance. To achieve a good balance, this paper proposes a binary hybrid GWO and Harris Hawks Optimization (HHO) to form a memetic approach called HBGWOHHO. The sigmoid transfer function is used to transfer the continuous search space into a binary one to meet the feature selection nature requirement. A wrapper-based k-Nearest neighbor is used to evaluate the goodness of the selected features. To validate the performance of the proposed method, 18 standard UCI benchmark datasets were used. The performance of the proposed hybrid method was compared with Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), Binary Harris Hawks Optimizer (BHHO), Binary Genetic Algorithm (BGA) and Binary Hybrid BWOPSO. The findings revealed that the proposed method was effective in improving the performance of the BGWO algorithm. The proposed hybrid method outperforms the BGWO algorithm in terms of accuracy, selected feature size, and computational time. Similarly, compared with BPSO and BGA feature selection algorithms, the proposed HBGWOHHO surpassed them yield better accuracy, the smaller size of selected features in much lower computational time. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
VL  - 9
JF  - IEEE Access
N1  - cited By 54
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111949024&doi=10.1109%2fACCESS.2021.3060096&partnerID=40&md5=03a8def0ecbfc204255ada363d797b85
A1  - Al-Wajih, R.
A1  - Abdulkadir, S.J.
A1  - Aziz, N.
A1  - Al-Tashi, Q.
A1  - Talpur, N.
Y1  - 2021///
ER  -