Hybrid Binary Grey Wolf With Harris Hawks Optimizer for Feature Selection

Abstract

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.

Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

“…As a result, it is crucial to develop a binary version of MOGWO. The transfer function in ( 20) is introduced to convert the positions of search candidates for the MOGWO and MOPSO algorithms to a binary search space [51,52]:…”

An improved Wolf pack algorithm for optimization problems: Design and evaluation

“…The presented simulation results showed that PSO-GWO had a better global optimal solution. In addition, Al-Wajih et al [ 27 ] proposed an algorithm called HBGWOHHO, which is based on the combination of GWO algorithm and Harris Hawks Optimization. Moreover, Banaie-Dezfouli et al [ 28 ] proposed a method called R-GWO, which is constructed by a representative based grey wolf optimizer.…”

A novel feature selection algorithm based on damping oscillation theory

“…QASEM et al proposed a binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) [28]. RANYA et al proposed a hybrid feature selection algorithm based on GWO and Harris Hawks Optimization (HHO) for feature selection [29]. Zheng et al combined MSMC (Maximum Spearman Minimum Covariance) and CS (Cuckoo Search) algorithms to form MSMCCS algorithm [30].…”