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Enhanced Multi-Objective Grey Wolf Optimizer with Lévy Flight and Mutation Operators for Feature Selection

Computer Systems Science & Engineering 2023年第11期47卷 1937-1966页

作者： Qasem Al-Tashi Tareq M Shami Said Jadid Abdulkadir Emelia Akashah Patah Akhir Ayed Alwadain Hitham Alhussain Alawi Alqushaibi helmi md rais Amgad Muneer Maliazurina B.Saad Jia Wu Seyedali Mirjalili Department of Imaging Physics The University of Texas MD Anderson Cancer CenterHoustonTXUSA Department of Electronic Engineering University of YorkYorkYO105DDUK Computer and Information Sciences Department Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Computer Science Department Community CollegeKing Saud UniversityRiyadhP.O.Box 145111Saudi Arabia Torrens University Australia BrisbaneQLDAustralia Yonsei Frontier Lab Yonsei UniversitySeoulKorea University Research and Innovation Center Obuda UniversityBudapest1034Hungary

The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be *** it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization *** paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection *** proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation *** Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected *** mutation operator is integrated to add more informative features that can assist in enhancing classification *** feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid *** evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization ***,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.

关键词： Feature selection multi-objective optimization grey wolf optimizer Lévy flight mutation classification

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