CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a w...

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Published in:	Scientific reports Vol. 14; no. 1; pp. 701 - 18
Main Authors:	Abdelrazek, Mohammed, Abd Elaziz, Mohamed, El-Baz, A. H.
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 06.01.2024 Nature Publishing Group Nature Portfolio
Subjects:	639/705/117 639/705/794 Algorithms Benchmarks Feature selection Foraging behavior Gene mapping Humanities and Social Sciences multidisciplinary Optimization algorithms Science Science (multidisciplinary)
ISSN:	2045-2322, 2045-2322
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Abstract	In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) and F-Score (90–100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC’2022 benchmarks functions.
AbstractList	In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9-100%), sensitivity (77.6-100%), precision (91.8-96.08%), specificity (91.6-100%) and F-Score (90-100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC'2022 benchmarks functions.In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9-100%), sensitivity (77.6-100%), precision (91.8-96.08%), specificity (91.6-100%) and F-Score (90-100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC'2022 benchmarks functions. In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) and F-Score (90–100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC’2022 benchmarks functions. Abstract In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) and F-Score (90–100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC’2022 benchmarks functions.
ArticleNumber	701
Author	Abdelrazek, Mohammed Abd Elaziz, Mohamed El-Baz, A. H.
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Cites_doi	10.1016/j.neucom.2016.03.101 10.1016/j.engappai.2018.04.012 10.1016/j.neucom.2022.04.083 10.1142/S0218488523500241 10.4018/IJAMC.292517 10.1016/j.knosys.2021.107283 10.1007/s12559-019-09668-6 10.1016/j.matpr.2017.07.055 10.1007/s10044-018-0695-2 10.1016/j.chaos.2006.04.057 10.1007/s11042-023-15467-x 10.1109/ACCESS.2021.3133286 10.1007/s10489-018-1261-8 10.4249/scholarpedia.1883 10.1109/TSMCB.2012.2227469 10.1007/s12652-018-1031-9 10.1109/ACCESS.2020.2999093 10.1002/cem.1180080107 10.1016/j.cie.2021.107408 10.1016/j.eswa.2022.117217 10.1080/25742558.2018.1483565 10.1016/j.swevo.2011.02.002 10.1016/j.future.2019.02.028 10.1016/j.eswa.2022.119041 10.1016/j.jocs.2022.101651 10.1016/j.compbiolchem.2007.10.001 10.1109/MCI.2006.329691 10.1007/s00357-018-9261-2 10.1016/j.cma.2021.114194 10.1038/s41598-022-18993-0 10.1016/j.knosys.2022.108771 10.1016/j.engappai.2017.01.006 10.1007/s00521-017-2988-6 10.1016/j.engappai.2022.105082 10.1007/s00521-022-07925-8 10.1007/s11721-007-0002-0 10.1109/TKDE.2008.239 10.1016/j.compstruc.2016.03.001 10.1016/j.cma.2022.114570 10.1016/j.asoc.2009.11.014 10.1109/EURCON.2005.1629899 10.1007/978-1-4419-1665-5_1 10.1109/KCIC.2017.8228458 10.1109/ICMLC.2007.4370294 10.1109/SOCPAR.2015.7492775 10.1007/978-3-540-73190-0_7
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References	Kyaw, Limsiroratana, Sattayaraksa (CR1) 2022; 13 Emary, Zawbaa (CR3) 2019; 22 Karaboga, Akay (CR20) 2009; 214 Agushaka, Ezugwu, Abualigah (CR27) 2022; 391 Sayed, Darwish, Hassanien (CR30) 2018; 35 Emary, Zawbaa, Hassanien (CR17) 2016; 213 Mafarja, Qasem, Heidari, Aljarah, Faris, Mirjalili (CR41) 2020; 12 CR33 Neggaz, Neggaz, Fizazi (CR50) 2023; 35 Derrac, García, Molina, Herrera (CR35) 2011; 1 Yang, Wang, Zhao, Yu, Huang, Heidari, Cai, Bourouis, Algarni, Chen (CR49) 2023; 213 Ahmed, Ghosh, Singh, Geem, Sarkar (CR40) 2020; 8 CR2 CR7 CR9 Long, Xu, Jiao, Wu (CR4) 2022; 201 Zhao, Wang, Mirjalili (CR44) 2022; 388 Wang, Cao, Zhang, Mirjalili, Zhao (CR37) 2022; 114 Etminaniesfahani, Ghanbarzadeh, Marashi (CR22) 2018; 74 Shen, Shi, Kong (CR13) 2008; 32 CR19 Joshi, Kulkarni, Kakandikar, Nandedkar (CR11) 2017; 4 Sayed, Hassanien, Azar (CR32) 2019; 31 Dehghani, Hubálovsky (CR47) 2021; 9 Eluri, Devarakonda (CR24) 2022; 247 Eluri, Devarakonda (CR25) 2023; 31 Xue, Zhang, Browne (CR16) 2012; 43 Gandomi, Yang, Talatahari, Alavi (CR8) 2013; 1 CR12 Peterson (CR34) 2009; 4 CR10 Moosavi, Bardsiri (CR48) 2017; 60 Dokeroglu, Deniz, Kiziloz (CR15) 2022 Eluri, Devarakonda (CR26) 2023; 82 Abdollahzadeh, Gharehchopogh, Mirjalili (CR45) 2021; 158 Akinola, Ezugwu, Oyelade, Agushaka (CR23) 2022; 12 Dorigo, Birattari, Stutzle (CR6) 2006; 1 Nasiri, Khiyabani (CR14) 2018; 5 Aalaei, Shahraki, Rowhanimanesh, Eslami (CR18) 2016; 19 Sayed, Tharwat, Hassanien (CR31) 2019; 49 Chuang, Yang, Li (CR29) 2011; 11 Etminaniesfahani, Gu, Salehipour (CR21) 2022; 61 Poli, Kennedy, Blackwell (CR5) 2007; 1 Leardi (CR43) 1994; 8 He, Garcia (CR36) 2009; 21 Askarzadeh (CR46) 2016; 169 Ahmed, Ghosh, Mirjalili, Sarkar (CR39) 2021; 228 Yang, Li, Cheng (CR28) 2007; 34 Heidari, Mirjalili, Faris, Aljarah, Mafarja, Chen (CR38) 2019; 97 Ibrahim, Ewees, Oliva, Abd Elaziz, Lu (CR42) 2019; 10 D Karaboga (50959_CR20) 2009; 214 M Mafarja (50959_CR41) 2020; 12 J Nasiri (50959_CR14) 2018; 5 SHS Moosavi (50959_CR48) 2017; 60 J Derrac (50959_CR35) 2011; 1 I Neggaz (50959_CR50) 2023; 35 A Etminaniesfahani (50959_CR22) 2018; 74 AS Joshi (50959_CR11) 2017; 4 RK Eluri (50959_CR25) 2023; 31 A Askarzadeh (50959_CR46) 2016; 169 50959_CR19 H He (50959_CR36) 2009; 21 KS Kyaw (50959_CR1) 2022; 13 W Long (50959_CR4) 2022; 201 Q Shen (50959_CR13) 2008; 32 A Etminaniesfahani (50959_CR21) 2022; 61 50959_CR12 D Yang (50959_CR28) 2007; 34 E Emary (50959_CR17) 2016; 213 RK Eluri (50959_CR24) 2022; 247 50959_CR10 T Dokeroglu (50959_CR15) 2022 L Wang (50959_CR37) 2022; 114 R Leardi (50959_CR43) 1994; 8 M Dehghani (50959_CR47) 2021; 9 W Zhao (50959_CR44) 2022; 388 S Ahmed (50959_CR39) 2021; 228 GI Sayed (50959_CR30) 2018; 35 S Aalaei (50959_CR18) 2016; 19 M Dorigo (50959_CR6) 2006; 1 JO Agushaka (50959_CR27) 2022; 391 E Emary (50959_CR3) 2019; 22 GI Sayed (50959_CR31) 2019; 49 GI Sayed (50959_CR32) 2019; 31 B Xue (50959_CR16) 2012; 43 50959_CR2 AH Gandomi (50959_CR8) 2013; 1 AA Heidari (50959_CR38) 2019; 97 B Abdollahzadeh (50959_CR45) 2021; 158 RK Eluri (50959_CR26) 2023; 82 50959_CR9 50959_CR7 R Poli (50959_CR5) 2007; 1 X Yang (50959_CR49) 2023; 213 LY Chuang (50959_CR29) 2011; 11 LE Peterson (50959_CR34) 2009; 4 50959_CR33 S Ahmed (50959_CR40) 2020; 8 RA Ibrahim (50959_CR42) 2019; 10 OA Akinola (50959_CR23) 2022; 12
References_xml	– volume: 213 start-page: 54 year: 2016 end-page: 65 ident: CR17 article-title: Binary ant lion approaches for feature selection publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.03.101 – volume: 74 start-page: 1 year: 2018 end-page: 9 ident: CR22 article-title: Fibonacci indicator algorithm: A novel tool for complex optimization problems publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2018.04.012 – year: 2022 ident: CR15 article-title: A comprehensive survey on recent metaheuristics for feature selection publication-title: Neurocomputing doi: 10.1016/j.neucom.2022.04.083 – volume: 31 start-page: 497 issue: 03 year: 2023 end-page: 530 ident: CR25 article-title: Chaotic binary pelican optimization algorithm for feature selection publication-title: Int. J. Uncert. Fuzziness Knowl. Based Syst. doi: 10.1142/S0218488523500241 – volume: 13 start-page: 1 issue: 1 year: 2022 end-page: 34 ident: CR1 article-title: A comparative study of meta-heuristic and conventional search in optimization of multi-dimensional feature selection publication-title: Int. J. Appl. Metaheuristic Comput. (IJAMC) doi: 10.4018/IJAMC.292517 – volume: 228 year: 2021 ident: CR39 article-title: AIEOU: Automata-based improved equilibrium optimizer with U-shaped transfer function for feature selection publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2021.107283 – volume: 12 start-page: 150 year: 2020 end-page: 175 ident: CR41 article-title: Efficient hybrid nature-inspired binary optimizers for feature selection publication-title: Cognit Comput. doi: 10.1007/s12559-019-09668-6 – ident: CR12 – volume: 4 start-page: 7262 issue: 8 year: 2017 end-page: 7269 ident: CR11 article-title: Cuckoo search optimization-a review publication-title: Mater. Today Proc. doi: 10.1016/j.matpr.2017.07.055 – volume: 19 start-page: 476 issue: 5 year: 2016 ident: CR18 article-title: Feature selection using genetic algorithm for breast cancer diagnosis: Experiment on three different datasets publication-title: Iran. J. Basic Med. Sci. – volume: 22 start-page: 857 year: 2019 end-page: 876 ident: CR3 article-title: Feature selection via Lèvy Antlion optimization publication-title: Pattern Anal. Appl. doi: 10.1007/s10044-018-0695-2 – volume: 34 start-page: 1366 issue: 4 year: 2007 end-page: 1375 ident: CR28 article-title: On the efficiency of chaos optimization algorithms for global optimization publication-title: Chaos Solit. Fractals doi: 10.1016/j.chaos.2006.04.057 – volume: 82 start-page: 26679 issue: 17 year: 2023 end-page: 26730 ident: CR26 article-title: Feature selection with a binary flamingo search algorithm and a genetic algorithm publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-023-15467-x – volume: 9 start-page: 162059 year: 2021 end-page: 162080 ident: CR47 article-title: Northern goshawk optimization: A new swarm-based algorithm for solving optimization problems publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3133286 – volume: 49 start-page: 188 year: 2019 end-page: 205 ident: CR31 article-title: Chaotic dragonfly algorithm: An improved metaheuristic algorithm for feature selection publication-title: Appl. Intell. doi: 10.1007/s10489-018-1261-8 – ident: CR19 – volume: 4 start-page: 1883 issue: 2 year: 2009 ident: CR34 article-title: K-nearest neighbor publication-title: Scholarpedia. doi: 10.4249/scholarpedia.1883 – volume: 43 start-page: 1656 issue: 6 year: 2012 end-page: 1671 ident: CR16 article-title: Particle swarm optimization for feature selection in classification: A multi-objective approach publication-title: IEEE Trans. Cybern. doi: 10.1109/TSMCB.2012.2227469 – volume: 10 start-page: 3155 year: 2019 end-page: 3169 ident: CR42 article-title: Improved salp swarm algorithm based on particle swarm optimization for feature selection publication-title: J. Ambient Intell. Hum. Comput. doi: 10.1007/s12652-018-1031-9 – volume: 8 start-page: 102629 year: 2020 end-page: 102645 ident: CR40 article-title: Hybrid of harmony search algorithm and ring theory-based evolutionary algorithm for feature selection publication-title: IEEE Access. doi: 10.1109/ACCESS.2020.2999093 – ident: CR9 – volume: 8 start-page: 65 issue: 1 year: 1994 end-page: 79 ident: CR43 article-title: Application of a genetic algorithm to feature selection under full validation conditions and to outlier detection publication-title: J. Chemometr. doi: 10.1002/cem.1180080107 – volume: 158 year: 2021 ident: CR45 article-title: African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2021.107408 – volume: 201 year: 2022 ident: CR4 article-title: A velocity-based butterfly optimization algorithm for high-dimensional optimization and feature selection publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.117217 – volume: 5 start-page: 1483565 issue: 1 year: 2018 ident: CR14 article-title: A whale optimization algorithm (WOA) approach for clustering publication-title: Cogent Math. Stat. doi: 10.1080/25742558.2018.1483565 – volume: 1 start-page: 3 issue: 1 year: 2011 end-page: 18 ident: CR35 article-title: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2011.02.002 – volume: 97 start-page: 849 year: 2019 end-page: 872 ident: CR38 article-title: Harris hawks optimization: Algorithm and applications publication-title: Future Gen. Comput. Syst. doi: 10.1016/j.future.2019.02.028 – volume: 213 year: 2023 ident: CR49 article-title: An adaptive quadratic interpolation and rounding mechanism sine cosine algorithm with application to constrained engineering optimization problems publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.119041 – volume: 61 year: 2022 ident: CR21 article-title: ABFIA: A hybrid algorithm based on artificial bee colony and Fibonacci indicator algorithm publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2022.101651 – ident: CR2 – volume: 32 start-page: 53 issue: 1 year: 2008 end-page: 60 ident: CR13 article-title: Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data publication-title: Comput. Biol. Chem. doi: 10.1016/j.compbiolchem.2007.10.001 – volume: 1 start-page: 28 issue: 4 year: 2006 end-page: 39 ident: CR6 article-title: Ant colony optimization publication-title: IEEE Comput. Intell. Mag. doi: 10.1109/MCI.2006.329691 – volume: 35 start-page: 300 issue: 2 year: 2018 end-page: 344 ident: CR30 article-title: A new chaotic whale optimization algorithm for features selection publication-title: J. Classif. doi: 10.1007/s00357-018-9261-2 – ident: CR10 – ident: CR33 – volume: 214 start-page: 108 issue: 1 year: 2009 end-page: 132 ident: CR20 article-title: A comparative study of artificial bee colony algorithm publication-title: Appl. Math. Comput. – volume: 388 start-page: 114194 year: 2022 ident: CR44 article-title: Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2021.114194 – volume: 12 start-page: 14945 issue: 1 year: 2022 ident: CR23 article-title: A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets publication-title: Sci. Rep. doi: 10.1038/s41598-022-18993-0 – volume: 247 year: 2022 ident: CR24 article-title: Binary golden eagle optimizer with time-varying flight length for feature selection publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2022.108771 – volume: 60 start-page: 1 year: 2017 end-page: 15 ident: CR48 article-title: Satin bowerbird optimizer: A new optimization algorithm to optimize anfis for software development effort estimation publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2017.01.006 – volume: 31 start-page: 171 year: 2019 end-page: 188 ident: CR32 article-title: Feature selection via a novel chaotic crow search algorithm publication-title: Neural Comput. Appl. doi: 10.1007/s00521-017-2988-6 – volume: 114 year: 2022 ident: CR37 article-title: Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2022.105082 – volume: 35 start-page: 3903 year: 2023 end-page: 3923 ident: CR50 article-title: Boosting archimedes optimization algorithm using trigonometric operators based on feature selection for facial analysis publication-title: Neural Comput. Appl. doi: 10.1007/s00521-022-07925-8 – volume: 1 start-page: 33 year: 2007 end-page: 57 ident: CR5 article-title: Particle swarm optimization: An overview publication-title: Swarm Intell. doi: 10.1007/s11721-007-0002-0 – volume: 21 start-page: 1263 issue: 9 year: 2009 end-page: 1284 ident: CR36 article-title: Learning from imbalanced data publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2008.239 – ident: CR7 – volume: 169 start-page: 1 year: 2016 end-page: 12 ident: CR46 article-title: A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm publication-title: Comput. Struct. doi: 10.1016/j.compstruc.2016.03.001 – volume: 391 year: 2022 ident: CR27 article-title: Dwarf mongoose optimization algorithm publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2022.114570 – volume: 11 start-page: 239 issue: 1 year: 2011 end-page: 248 ident: CR29 article-title: Chaotic maps based on binary particle swarm optimization for feature selection publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2009.11.014 – volume: 1 start-page: 1 year: 2013 end-page: 24 ident: CR8 article-title: Metaheuristic algorithms in modeling and optimization publication-title: Metaheuristic Appl. Struct. Infrastruct. – volume: 13 start-page: 1 issue: 1 year: 2022 ident: 50959_CR1 publication-title: Int. J. Appl. Metaheuristic Comput. (IJAMC) doi: 10.4018/IJAMC.292517 – ident: 50959_CR12 doi: 10.1109/EURCON.2005.1629899 – volume: 1 start-page: 3 issue: 1 year: 2011 ident: 50959_CR35 publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2011.02.002 – volume: 49 start-page: 188 year: 2019 ident: 50959_CR31 publication-title: Appl. Intell. doi: 10.1007/s10489-018-1261-8 – volume: 10 start-page: 3155 year: 2019 ident: 50959_CR42 publication-title: J. Ambient Intell. Hum. Comput. doi: 10.1007/s12652-018-1031-9 – volume: 22 start-page: 857 year: 2019 ident: 50959_CR3 publication-title: Pattern Anal. Appl. doi: 10.1007/s10044-018-0695-2 – volume: 391 year: 2022 ident: 50959_CR27 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2022.114570 – volume: 82 start-page: 26679 issue: 17 year: 2023 ident: 50959_CR26 publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-023-15467-x – volume: 169 start-page: 1 year: 2016 ident: 50959_CR46 publication-title: Comput. Struct. doi: 10.1016/j.compstruc.2016.03.001 – volume: 12 start-page: 150 year: 2020 ident: 50959_CR41 publication-title: Cognit Comput. doi: 10.1007/s12559-019-09668-6 – volume: 228 year: 2021 ident: 50959_CR39 publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2021.107283 – volume: 31 start-page: 171 year: 2019 ident: 50959_CR32 publication-title: Neural Comput. Appl. doi: 10.1007/s00521-017-2988-6 – volume: 158 year: 2021 ident: 50959_CR45 publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2021.107408 – volume: 31 start-page: 497 issue: 03 year: 2023 ident: 50959_CR25 publication-title: Int. J. Uncert. Fuzziness Knowl. Based Syst. doi: 10.1142/S0218488523500241 – volume: 97 start-page: 849 year: 2019 ident: 50959_CR38 publication-title: Future Gen. Comput. Syst. doi: 10.1016/j.future.2019.02.028 – year: 2022 ident: 50959_CR15 publication-title: Neurocomputing doi: 10.1016/j.neucom.2022.04.083 – volume: 1 start-page: 28 issue: 4 year: 2006 ident: 50959_CR6 publication-title: IEEE Comput. Intell. Mag. doi: 10.1109/MCI.2006.329691 – ident: 50959_CR33 – ident: 50959_CR9 doi: 10.1007/978-1-4419-1665-5_1 – volume: 32 start-page: 53 issue: 1 year: 2008 ident: 50959_CR13 publication-title: Comput. Biol. Chem. doi: 10.1016/j.compbiolchem.2007.10.001 – volume: 12 start-page: 14945 issue: 1 year: 2022 ident: 50959_CR23 publication-title: Sci. Rep. doi: 10.1038/s41598-022-18993-0 – volume: 35 start-page: 300 issue: 2 year: 2018 ident: 50959_CR30 publication-title: J. Classif. doi: 10.1007/s00357-018-9261-2 – volume: 4 start-page: 1883 issue: 2 year: 2009 ident: 50959_CR34 publication-title: Scholarpedia. doi: 10.4249/scholarpedia.1883 – volume: 4 start-page: 7262 issue: 8 year: 2017 ident: 50959_CR11 publication-title: Mater. Today Proc. doi: 10.1016/j.matpr.2017.07.055 – volume: 214 start-page: 108 issue: 1 year: 2009 ident: 50959_CR20 publication-title: Appl. Math. Comput. – volume: 1 start-page: 1 year: 2013 ident: 50959_CR8 publication-title: Metaheuristic Appl. Struct. Infrastruct. – volume: 1 start-page: 33 year: 2007 ident: 50959_CR5 publication-title: Swarm Intell. doi: 10.1007/s11721-007-0002-0 – ident: 50959_CR19 doi: 10.1109/KCIC.2017.8228458 – volume: 213 start-page: 54 year: 2016 ident: 50959_CR17 publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.03.101 – volume: 114 year: 2022 ident: 50959_CR37 publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2022.105082 – volume: 34 start-page: 1366 issue: 4 year: 2007 ident: 50959_CR28 publication-title: Chaos Solit. Fractals doi: 10.1016/j.chaos.2006.04.057 – volume: 19 start-page: 476 issue: 5 year: 2016 ident: 50959_CR18 publication-title: Iran. J. Basic Med. Sci. – volume: 11 start-page: 239 issue: 1 year: 2011 ident: 50959_CR29 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2009.11.014 – volume: 388 start-page: 114194 year: 2022 ident: 50959_CR44 publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2021.114194 – ident: 50959_CR10 doi: 10.1109/ICMLC.2007.4370294 – volume: 8 start-page: 102629 year: 2020 ident: 50959_CR40 publication-title: IEEE Access. doi: 10.1109/ACCESS.2020.2999093 – volume: 213 year: 2023 ident: 50959_CR49 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.119041 – volume: 247 year: 2022 ident: 50959_CR24 publication-title: Knowl. Based Syst. doi: 10.1016/j.knosys.2022.108771 – volume: 5 start-page: 1483565 issue: 1 year: 2018 ident: 50959_CR14 publication-title: Cogent Math. Stat. doi: 10.1080/25742558.2018.1483565 – volume: 61 year: 2022 ident: 50959_CR21 publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2022.101651 – volume: 8 start-page: 65 issue: 1 year: 1994 ident: 50959_CR43 publication-title: J. Chemometr. doi: 10.1002/cem.1180080107 – volume: 74 start-page: 1 year: 2018 ident: 50959_CR22 publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2018.04.012 – volume: 9 start-page: 162059 year: 2021 ident: 50959_CR47 publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3133286 – ident: 50959_CR2 doi: 10.1109/SOCPAR.2015.7492775 – volume: 35 start-page: 3903 year: 2023 ident: 50959_CR50 publication-title: Neural Comput. Appl. doi: 10.1007/s00521-022-07925-8 – ident: 50959_CR7 doi: 10.1007/978-3-540-73190-0_7 – volume: 21 start-page: 1263 issue: 9 year: 2009 ident: 50959_CR36 publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2008.239 – volume: 43 start-page: 1656 issue: 6 year: 2012 ident: 50959_CR16 publication-title: IEEE Trans. Cybern. doi: 10.1109/TSMCB.2012.2227469 – volume: 201 year: 2022 ident: 50959_CR4 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2022.117217 – volume: 60 start-page: 1 year: 2017 ident: 50959_CR48 publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2017.01.006
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Snippet	In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm... Abstract In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the...
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SubjectTerms	639/705/117 639/705/794 Algorithms Benchmarks Feature selection Foraging behavior Gene mapping Humanities and Social Sciences multidisciplinary Optimization algorithms Science Science (multidisciplinary)
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Title	CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection
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