A New Hybrid Algorithm Based on Grey Wolf Optimization and Crow Search Algorithm for Unconstrained Function Optimization and Feature Selection

Abstract

Grey wolf optimizer (GWO) is a very efficient metaheuristic inspired by the hierarchy of the Canis lupus wolves. It has been extensively employed to a variety of practical applications. Crow search algorithm (CSA) is a recently proposed metaheuristic algorithm, which mimics the intellectual conduct of crows. In this paper, a hybrid GWO with CSA, namely GWOCSA is proposed, which combines the strengths of both the algorithms effectively with the aim to generate promising candidate solutions in order to achieve global optima efficiently. In order to validate the competence of the proposed hybrid GWOCSA, a widely utilized set of 23 benchmark test functions having a wide range of dimensions and varied complexities is used in this paper. The results obtained by the proposed algorithm are compared to 10 other algorithms in this paper for verification. The statistical results demonstrate that the GWOCSA outperforms other algorithms, including the recent variants of GWO called, enhanced grey wolf optimizer (EGWO) and augmented grey wolf optimizer (AGWO) in terms of high local optima avoidance ability and fast convergence speed. Furthermore, in order to demonstrate the applicability of the proposed algorithm at solving complex real-world problems, the GWOCSA is also employed to solve the feature selection problem as well. The GWOCSA as a feature selection approach is tested on 21 widely employed data sets acquired from the University of California at Irvine repository. The experimental results are compared to the stateof-the-art feature selection techniques, including the native GWO, the EGWO, and the AGWO. The results reveal that the GWOCSA has comprehensive superiority in solving the feature selection problem, which proves the capability of the proposed algorithm in solving real-world complex problems. INDEX TERMS Grey wolf optimizer, crow search algorithm, hybrid algorithm, function optimization, feature selection.

Optimization of Low-Power Line-Start PM Motor Using Gray Wolf Metaheuristic Algorithm

“…In the gray wolf method, the value of coefficient a is determined for each iteration. This factor usually decreases linearly [19]. In the proposed algorithm, the value of coefficient a is determined for each iteration according to formula [20].…”

Artificial Intelligence Techniques and External Factors used in Crime Forecasting in Violence and Property: A Review

“…ANN is widely applied in the computer science field because of its capabilities in machine learning and pattern recognition. It is a parallel distributed information processing structure which consists of processing elements that can be considered as neurons (Arora et al, 2019;Cook and Durrance, 2013;Xiao et al, 2018).…”

Diagnosis of cardiac arrhythmia using Swarm-intelligence based Metaheuristic Techniques: A comparative analysis

“…Feature selection is a pre-eminent step of data pre-processing that helps in extracting a subset of features from the native data set. It is a process of uprooting the significant features related to an optimization problem [17]. Feature selection aims to extract the best features and to lessen the duplicate features.…”

“…GWO propounded in 2014 by Sayedali Mirjalili et al imitates the hunting nature and leadership behaviour of grey wolves. Generally, the grey wolves come from the Canidae family[17][55]. Based on the hierarchy grey wolves are categorized as alpha(α), beta(β), delta(δ) and omega(ω).…”