Enhanced Marine Predators Algorithm for Solving Global Optimization and Feature Selection Problems

Feature selection (FS) is applied to reduce data dimensions while retaining much information. Many optimization methods have been applied to enhance the efficiency of FS algorithms. These approaches reduce the processing time and improve the accuracy of the learning models. In this paper, a develope...
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Autor*in:	Ahmed A. Ewees [verfasserIn] Fatma H. Ismail [verfasserIn] Rania M. Ghoniem [verfasserIn] Marwa A. Gaheen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	marine predators algorithm Aquila optimizer feature selection engineering problems

Übergeordnetes Werk:	In: Mathematics - MDPI AG, 2013, 10(2022), 21, p 4154
Übergeordnetes Werk:	volume:10 ; year:2022 ; number:21, p 4154

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/math10214154

Katalog-ID:	DOAJ020662726

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allfieldsSound	10.3390/math10214154 doi (DE-627)DOAJ020662726 (DE-599)DOAJ2bf99cc30b8e4df2afb4a8b94dabcba0 DE-627 ger DE-627 rakwb eng QA1-939 Ahmed A. Ewees verfasserin aut Enhanced Marine Predators Algorithm for Solving Global Optimization and Feature Selection Problems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Feature selection (FS) is applied to reduce data dimensions while retaining much information. Many optimization methods have been applied to enhance the efficiency of FS algorithms. These approaches reduce the processing time and improve the accuracy of the learning models. In this paper, a developed method called MPAO based on the marine predators algorithm (MPA) and the “narrowed exploration” strategy of the Aquila optimizer (AO) is proposed to handle FS, global optimization, and engineering problems. This modification enhances the exploration behavior of the MPA to update and explore the search space. Therefore, the narrowed exploration of the AO increases the searchability of the MPA, thereby improving its ability to obtain optimal or near-optimal results, which effectively helps the original MPA overcome the local optima issues in the problem domain. The performance of the proposed MPAO method is evaluated on solving FS and global optimization problems using some evaluation criteria, including the maximum value (Max), minimum value (Min), and standard deviation (Std) of the fitness function. Furthermore, the results are compared to some meta-heuristic methods over four engineering problems. Experimental results confirm the efficiency of the proposed MPAO method in solving FS, global optimization, and engineering problems. marine predators algorithm Aquila optimizer feature selection engineering problems Mathematics Fatma H. Ismail verfasserin aut Rania M. Ghoniem verfasserin aut Marwa A. Gaheen verfasserin aut In Mathematics MDPI AG, 2013 10(2022), 21, p 4154 (DE-627)737287764 (DE-600)2704244-3 22277390 nnns volume:10 year:2022 number:21, p 4154 https://doi.org/10.3390/math10214154 kostenfrei https://doaj.org/article/2bf99cc30b8e4df2afb4a8b94dabcba0 kostenfrei https://www.mdpi.com/2227-7390/10/21/4154 kostenfrei https://doaj.org/toc/2227-7390 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 21, p 4154
language	English
source	In Mathematics 10(2022), 21, p 4154 volume:10 year:2022 number:21, p 4154
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topic_facet	marine predators algorithm Aquila optimizer feature selection engineering problems Mathematics
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authorswithroles_txt_mv	Ahmed A. Ewees @@aut@@ Fatma H. Ismail @@aut@@ Rania M. Ghoniem @@aut@@ Marwa A. Gaheen @@aut@@
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callnumber-first	Q - Science
author	Ahmed A. Ewees
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topic_title	QA1-939 Enhanced Marine Predators Algorithm for Solving Global Optimization and Feature Selection Problems marine predators algorithm Aquila optimizer feature selection engineering problems
topic	misc QA1-939 misc marine predators algorithm misc Aquila optimizer misc feature selection misc engineering problems misc Mathematics
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title	Enhanced Marine Predators Algorithm for Solving Global Optimization and Feature Selection Problems
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title_sort	enhanced marine predators algorithm for solving global optimization and feature selection problems
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title_auth	Enhanced Marine Predators Algorithm for Solving Global Optimization and Feature Selection Problems
abstract	Feature selection (FS) is applied to reduce data dimensions while retaining much information. Many optimization methods have been applied to enhance the efficiency of FS algorithms. These approaches reduce the processing time and improve the accuracy of the learning models. In this paper, a developed method called MPAO based on the marine predators algorithm (MPA) and the “narrowed exploration” strategy of the Aquila optimizer (AO) is proposed to handle FS, global optimization, and engineering problems. This modification enhances the exploration behavior of the MPA to update and explore the search space. Therefore, the narrowed exploration of the AO increases the searchability of the MPA, thereby improving its ability to obtain optimal or near-optimal results, which effectively helps the original MPA overcome the local optima issues in the problem domain. The performance of the proposed MPAO method is evaluated on solving FS and global optimization problems using some evaluation criteria, including the maximum value (Max), minimum value (Min), and standard deviation (Std) of the fitness function. Furthermore, the results are compared to some meta-heuristic methods over four engineering problems. Experimental results confirm the efficiency of the proposed MPAO method in solving FS, global optimization, and engineering problems.
abstractGer	Feature selection (FS) is applied to reduce data dimensions while retaining much information. Many optimization methods have been applied to enhance the efficiency of FS algorithms. These approaches reduce the processing time and improve the accuracy of the learning models. In this paper, a developed method called MPAO based on the marine predators algorithm (MPA) and the “narrowed exploration” strategy of the Aquila optimizer (AO) is proposed to handle FS, global optimization, and engineering problems. This modification enhances the exploration behavior of the MPA to update and explore the search space. Therefore, the narrowed exploration of the AO increases the searchability of the MPA, thereby improving its ability to obtain optimal or near-optimal results, which effectively helps the original MPA overcome the local optima issues in the problem domain. The performance of the proposed MPAO method is evaluated on solving FS and global optimization problems using some evaluation criteria, including the maximum value (Max), minimum value (Min), and standard deviation (Std) of the fitness function. Furthermore, the results are compared to some meta-heuristic methods over four engineering problems. Experimental results confirm the efficiency of the proposed MPAO method in solving FS, global optimization, and engineering problems.
abstract_unstemmed	Feature selection (FS) is applied to reduce data dimensions while retaining much information. Many optimization methods have been applied to enhance the efficiency of FS algorithms. These approaches reduce the processing time and improve the accuracy of the learning models. In this paper, a developed method called MPAO based on the marine predators algorithm (MPA) and the “narrowed exploration” strategy of the Aquila optimizer (AO) is proposed to handle FS, global optimization, and engineering problems. This modification enhances the exploration behavior of the MPA to update and explore the search space. Therefore, the narrowed exploration of the AO increases the searchability of the MPA, thereby improving its ability to obtain optimal or near-optimal results, which effectively helps the original MPA overcome the local optima issues in the problem domain. The performance of the proposed MPAO method is evaluated on solving FS and global optimization problems using some evaluation criteria, including the maximum value (Max), minimum value (Min), and standard deviation (Std) of the fitness function. Furthermore, the results are compared to some meta-heuristic methods over four engineering problems. Experimental results confirm the efficiency of the proposed MPAO method in solving FS, global optimization, and engineering problems.
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title_short	Enhanced Marine Predators Algorithm for Solving Global Optimization and Feature Selection Problems
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