A Novel Binary Emperor Penguin Optimizer for Feature Selection Tasks

作     者：Minakshi Kalra Vijay Kumar Manjit Kaur Sahar Ahmed Idris Saban Oturk Hammam Alshazly 

作者机构：Computer Science DepartmentGovernment College BahadurgarhBahadurgarh124507India CSE DepartmentNational Institute of Technology Hamirpur177005India School of Engineering and Applied SciencesBennett UniversityGreater Noida201310India College of Industrial EngineeringKing Khalid UniversityAbhaSaudi Arabia Department of Electrical and Electronics EngineeringAmasya UniversityAmasyaTurkey Faculty of Computers and InformationSouth Valley UniversityQena83523Egypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第3期

页      面：6239-6255页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：This work was supported by the Deanship of Scientific Research at King Khalid University through the Research Groups Program under Grant Number RGP.1/95/42 

主　　题：Metaheuristics optimization algorithms emperor penguin optimizer intensification diversification feature selection 

摘      要：Nowadays,due to the increase in information resources,the number of parameters and complexity of feature vectors *** offermore practical solutions instead of exact solutions for the solution of this *** Emperor PenguinOptimizer(EPO)is one of the highest performing meta-heuristic algorithms of recent times that imposed the gathering behavior of emperor *** shows the superiority of its performance over a wide range of optimization problems thanks to its equal chance to each penguin and its fast convergence *** traditional EPO overcomes the optimization problems in continuous search space,many problems today shift to the binary search ***,in this study,using the power of traditional EPO,binary EPO(BEPO)is presented for the effective solution of binary-nature *** algorithm uses binary search space instead of searching solutions like conventional EPO algorithm in continuous search *** this purpose,the sigmoidal functions are preferred in determining the emperor *** addition,the boundaries of the search space remain constant by choosing binary ***’s performance is evaluated over twenty-nine benchmarking *** evaluations are made to reveal the superiority of the BEPO *** addition,the performance of the BEPO algorithm was evaluated for the binary feature selection *** experimental results reveal that the BEPO algorithm outperforms the existing binary meta-heuristic algorithms in both tasks.