A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems

In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the design of the ASBO is to use the average information and the subtraction of the bes...

Full description

Saved in:

Bibliographic Details
Published in:	PeerJ. Computer science Vol. 8; p. e910
Main Authors:	Dehghani, Mohammad, Hubálovský, Štěpán, Trojovský, Pavel
Format:	Journal Article
Language:	English
Published:	United States PeerJ. Ltd 07.03.2022 PeerJ, Inc PeerJ Inc
Subjects:	Algorithm of best and worst members of the population Algorithms Algorithms and Analysis of Algorithms Analysis Birth rate Design Evolution & development Exploitation Genetic algorithms Heuristic methods Linear programming Mathematical optimization Mathematical programming Optimization Optimization algorithm Optimization algorithms Optimization problem Optimization Theory and Computation Performance evaluation Pheromones Population Problem solving Simulation Subtraction Teaching Optimization algorithm Algorithm of best and worst members of the population Optimization Optimization problem
ISSN:	2376-5992, 2376-5992
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the design of the ASBO is to use the average information and the subtraction of the best and worst population members for guiding the algorithm population in the problem search space. The proposed ASBO is mathematically modeled with the ability to solve optimization problems. Twenty-three test functions, including unimodal and multimodal functions, have been employed to evaluate ASBO’s performance in effectively solving optimization problems. The optimization results of the unimodal functions, which have only one main peak, show the high ASBO’s exploitation power in converging towards global optima. In addition, the optimization results of the high-dimensional multimodal functions and fixed-dimensional multimodal functions, which have several peaks and local optima, indicate the high exploration power of ASBO in accurately searching the problem-solving space and not getting stuck in nonoptimal peaks. The simulation results show the proper balance between exploration and exploitation in ASBO in order to discover and present the optimal solution. In addition, the results obtained from the implementation of ASBO in optimizing these objective functions are analyzed compared with the results of nine well-known metaheuristic algorithms. Analysis of the optimization results obtained from ASBO against the performance of the nine compared algorithms indicates the superiority and competitiveness of the proposed algorithm in providing more appropriate solutions.
AbstractList	In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the design of the ASBO is to use the average information and the subtraction of the best and worst population members for guiding the algorithm population in the problem search space. The proposed ASBO is mathematically modeled with the ability to solve optimization problems. Twenty-three test functions, including unimodal and multimodal functions, have been employed to evaluate ASBO’s performance in effectively solving optimization problems. The optimization results of the unimodal functions, which have only one main peak, show the high ASBO’s exploitation power in converging towards global optima. In addition, the optimization results of the high-dimensional multimodal functions and fixed-dimensional multimodal functions, which have several peaks and local optima, indicate the high exploration power of ASBO in accurately searching the problem-solving space and not getting stuck in nonoptimal peaks. The simulation results show the proper balance between exploration and exploitation in ASBO in order to discover and present the optimal solution. In addition, the results obtained from the implementation of ASBO in optimizing these objective functions are analyzed compared with the results of nine well-known metaheuristic algorithms. Analysis of the optimization results obtained from ASBO against the performance of the nine compared algorithms indicates the superiority and competitiveness of the proposed algorithm in providing more appropriate solutions. In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the design of the ASBO is to use the average information and the subtraction of the best and worst population members for guiding the algorithm population in the problem search space. The proposed ASBO is mathematically modeled with the ability to solve optimization problems. Twenty-three test functions, including unimodal and multimodal functions, have been employed to evaluate ASBO's performance in effectively solving optimization problems. The optimization results of the unimodal functions, which have only one main peak, show the high ASBO's exploitation power in converging towards global optima. In addition, the optimization results of the high-dimensional multimodal functions and fixed-dimensional multimodal functions, which have several peaks and local optima, indicate the high exploration power of ASBO in accurately searching the problem-solving space and not getting stuck in nonoptimal peaks. The simulation results show the proper balance between exploration and exploitation in ASBO in order to discover and present the optimal solution. In addition, the results obtained from the implementation of ASBO in optimizing these objective functions are analyzed compared with the results of nine well-known metaheuristic algorithms. Analysis of the optimization results obtained from ASBO against the performance of the nine compared algorithms indicates the superiority and competitiveness of the proposed algorithm in providing more appropriate solutions.In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal solutions for various optimization problems. The core idea in the design of the ASBO is to use the average information and the subtraction of the best and worst population members for guiding the algorithm population in the problem search space. The proposed ASBO is mathematically modeled with the ability to solve optimization problems. Twenty-three test functions, including unimodal and multimodal functions, have been employed to evaluate ASBO's performance in effectively solving optimization problems. The optimization results of the unimodal functions, which have only one main peak, show the high ASBO's exploitation power in converging towards global optima. In addition, the optimization results of the high-dimensional multimodal functions and fixed-dimensional multimodal functions, which have several peaks and local optima, indicate the high exploration power of ASBO in accurately searching the problem-solving space and not getting stuck in nonoptimal peaks. The simulation results show the proper balance between exploration and exploitation in ASBO in order to discover and present the optimal solution. In addition, the results obtained from the implementation of ASBO in optimizing these objective functions are analyzed compared with the results of nine well-known metaheuristic algorithms. Analysis of the optimization results obtained from ASBO against the performance of the nine compared algorithms indicates the superiority and competitiveness of the proposed algorithm in providing more appropriate solutions.
ArticleNumber	e910
Audience	Academic
Author	Trojovský, Pavel Hubálovský, Štěpán Dehghani, Mohammad
Author_xml	– sequence: 1 givenname: Mohammad surname: Dehghani fullname: Dehghani, Mohammad organization: Department of Mathematics/Faculty of Science, University of Hradec Králové, Hradec Kralove, Czech Republic – sequence: 2 givenname: Štěpán surname: Hubálovský fullname: Hubálovský, Štěpán organization: Department of Applied Cybernetics/Faculty of Science, University of Hradec Králové, Hradec Kralove, Czech Republic – sequence: 3 givenname: Pavel orcidid: 0000-0001-8992-125X surname: Trojovský fullname: Trojovský, Pavel organization: Department of Mathematics/Faculty of Science, University of Hradec Králové, Hradec Kralove, Czech Republic
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/35494852$$D View this record in MEDLINE/PubMed
BookMark	eNptkk1v1DAQhiNUREvpjTOKxAUkdnGcOI4vSKuKj5UqIfFxtsbOOPUqiYOdbIE_wt_Fu9uFpmpyiDXzzJvMm_dpctK7HpPkeUaWnGf87YDoNwsdliIjj5IzmvNywYSgJ3fOp8lFCBtCSMayeIknyWnOClFUjJ4lf1ZpjzepG0bb2d8wWten0DbO2_G6SxUErNNdaYseGkyhr9MwqdGD3qPOpOM1pgrDuO_dOB9PHXYKfTh2BzdM7UHaOJ8G125t36Rb8NZNYf7uwTvVYheeJY8NtAEvbp_nyfcP779dflpcff64vlxdLTTjYlyA1jkloEwFgpYKSaWYEcCU0CURRgtRldpgqeLqQGskSiAq0KQosRLG5OfJ-qBbO9jIwdsO_C_pwMp9wflGgh-tblFmVOQF1RkQXhZlzqqCCwaglWJaCaKi1ruD1jCpDmuNffSpnYnOO729lo3bSkGKgnIWBV7dCnj3Y4qeys4GjW0LPUanJC1ZVbKMUxrRl_fQjZt8H62KVM5JnrOy-E81EBewvXG7P7cTlStO4gJZUeSRWj5AxbvGzuoYOGNjfTbwejYQmRF_jg1MIcj11y9z9sVdU_65cYxgBOgB0N6F4NFIbcd9GOJX2FZmRO6iLvdRlzrIGPU49Obe0FH3QfwvprAEXA
CitedBy_id	crossref_primary_10_1002_jnm_70030 crossref_primary_10_1016_j_advengsoft_2022_103387 crossref_primary_10_32604_cmc_2023_030379 crossref_primary_10_1016_j_eswa_2025_127303 crossref_primary_10_1007_s11276_023_03588_y crossref_primary_10_3233_IDT_220130 crossref_primary_10_32604_cmc_2023_034695 crossref_primary_10_3390_biomimetics8050386 crossref_primary_10_1016_j_eswa_2022_119246 crossref_primary_10_1080_13682199_2023_2187518 crossref_primary_10_1155_2023_9709608 crossref_primary_10_1016_j_heliyon_2024_e29339 crossref_primary_10_1080_0954898X_2024_2449173 crossref_primary_10_1007_s10479_024_05837_5 crossref_primary_10_1007_s00366_023_01830_x crossref_primary_10_1038_s41598_025_06121_7 crossref_primary_10_1016_j_chaos_2024_114723 crossref_primary_10_1016_j_eswa_2025_127533 crossref_primary_10_1016_j_vehcom_2024_100824
Cites_doi	10.1016/j.swevo.2015.07.002 10.1007/s12652-020-02580-0 10.22266/ijies2021.0630.46 10.3390/s21155214 10.1109/TEVC.2008.919004 10.1023/A:1015059928466 10.22266/ijies2020.1231.32 10.3390/app10186173 10.1023/A:1008202821328 10.1016/j.knosys.2021.106926 10.1016/j.eswa.2020.113377 10.1016/j.advengsoft.2016.01.008 10.1007/s00521-019-04452-x 10.20998/2074-272X.2019.4.10 10.1007/s10489-020-01836-8 10.1166/jctn.2019.8261 10.1109/3477.484436 10.1016/j.cad.2010.12.015 10.1109/ICSPCT.2014.6884887 10.1109/4235.585893 10.1007/s42452-020-03511-6 10.1023/A:1022602019183 10.1016/j.advengsoft.2013.12.007 10.3390/electronics10111320 10.1007/s10489-020-01893-z 10.32604/cmc.2022.021072 10.1109/ICNN.1995.488968 10.1016/j.cie.2021.107250 10.22266/ijies2020.0831.19 10.1016/j.asoc.2017.11.043 10.1016/j.cie.2021.107224 10.3390/app11031286 10.1002/int.22535 10.3390/su122310053 10.1080/10286608.2013.820280 10.3390/app10217683 10.22266/ijies2020.1031.45 10.1007/s10898-017-0589-7 10.1016/j.knosys.2020.106711 10.3390/e23040491 10.1007/BFb0055923 10.22266/ijies2020.1031.26 10.1016/j.eswa.2020.114522 10.1016/j.ins.2009.03.004 10.1016/j.advengsoft.2017.05.014 10.3390/s21134567 10.1016/j.engappai.2020.103541 10.1016/j.cie.2020.107050
ContentType	Journal Article
Copyright	2022 Dehghani et al. COPYRIGHT 2022 PeerJ. Ltd. 2022 Dehghani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 Dehghani et al. 2022 Dehghani et al.
Copyright_xml	– notice: 2022 Dehghani et al. – notice: COPYRIGHT 2022 PeerJ. Ltd. – notice: 2022 Dehghani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 Dehghani et al. 2022 Dehghani et al.
DBID	AAYXX CITATION NPM ISR 3V. 7XB 8AL 8FE 8FG 8FK ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- M0N P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA
DOI	10.7717/peerj-cs.910
DatabaseName	CrossRef PubMed Gale In Context: Science ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) Computing Database (Alumni Edition) ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC ProQuest Central ProQuest Technology Collection ProQuest One Community College ProQuest Central Korea ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database Computing Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) Directory of Open Access Journals
DatabaseTitle	CrossRef PubMed Publicly Available Content Database Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies & Aerospace Collection ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	CrossRef MEDLINE - Academic Publicly Available Content Database PubMed
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	2376-5992
ExternalDocumentID	oai_doaj_org_article_129342c1a076463584795aacbb5cb90b PMC9044275 A708471443 35494852 10_7717_peerj_cs_910
Genre	Journal Article
GrantInformation_xml	– fundername: Excellence Project PřF UHK grantid: 2208/2021–2022 – fundername: Faculty of Science, University of Hradec Kralove, Czech Republic
GroupedDBID	53G 5VS 8FE 8FG AAFWJ AAYXX ABUWG ADBBV AFFHD AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS ARAPS AZQEC BCNDV BENPR BGLVJ BPHCQ CCPQU CITATION DWQXO FRP GNUQQ GROUPED_DOAJ H13 HCIFZ IAO ICD IEA ISR ITC K6V K7- M~E OK1 P62 PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC RPM 3V. ARCSS M0N NPM 7XB 8AL 8FK JQ2 PKEHL PQEST PQUKI PRINS Q9U 7X8 PUEGO 5PM
ID	FETCH-LOGICAL-c579t-acc320abf8a926be08b5f9a5b9c609fc9986cfe6b511a2de0b9eebac046e89ff3
IEDL.DBID	P5Z
ISICitedReferencesCount	24
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000768601100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2376-5992
IngestDate	Fri Oct 03 12:37:14 EDT 2025 Tue Nov 04 01:46:09 EST 2025 Fri Sep 05 06:55:46 EDT 2025 Fri Jul 25 22:04:28 EDT 2025 Tue Nov 11 08:19:17 EST 2025 Tue Nov 04 17:27:46 EST 2025 Thu Nov 13 14:32:45 EST 2025 Thu Jan 02 22:54:08 EST 2025 Sat Nov 29 03:45:06 EST 2025 Tue Nov 18 22:36:39 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	Optimization algorithm Algorithm of best and worst members of the population Optimization Optimization problem
Language	English
License	https://creativecommons.org/licenses/by/4.0 2022 Dehghani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c579t-acc320abf8a926be08b5f9a5b9c609fc9986cfe6b511a2de0b9eebac046e89ff3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0001-8992-125X
OpenAccessLink	https://www.proquest.com/docview/2637033564?pq-origsite=%requestingapplication%
PMID	35494852
PQID	2637033564
PQPubID	2045934
PageCount	e910
ParticipantIDs	doaj_primary_oai_doaj_org_article_129342c1a076463584795aacbb5cb90b pubmedcentral_primary_oai_pubmedcentral_nih_gov_9044275 proquest_miscellaneous_2658651722 proquest_journals_2637033564 gale_infotracmisc_A708471443 gale_infotracacademiconefile_A708471443 gale_incontextgauss_ISR_A708471443 pubmed_primary_35494852 crossref_citationtrail_10_7717_peerj_cs_910 crossref_primary_10_7717_peerj_cs_910
PublicationCentury	2000
PublicationDate	2022-03-07
PublicationDateYYYYMMDD	2022-03-07
PublicationDate_xml	– month: 03 year: 2022 text: 2022-03-07 day: 07
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: San Diego – name: San Diego, USA
PublicationTitle	PeerJ. Computer science
PublicationTitleAlternate	PeerJ Comput Sci
PublicationYear	2022
Publisher	PeerJ. Ltd PeerJ, Inc PeerJ Inc
Publisher_xml	– name: PeerJ. Ltd – name: PeerJ, Inc – name: PeerJ Inc
References	Dehghani (10.7717/peerj-cs.910/ref-10) 2021; 11 Dehghani (10.7717/peerj-cs.910/ref-18) 2021; 21 Abdollahzadeh (10.7717/peerj-cs.910/ref-1) 2021; 36 Faramarzi (10.7717/peerj-cs.910/ref-26) 2020; 152 Rashedi (10.7717/peerj-cs.910/ref-46) 2009; 179 Dehghani (10.7717/peerj-cs.910/ref-11) 2020c; 13 Kaur (10.7717/peerj-cs.910/ref-32) 2020; 90 Doumari (10.7717/peerj-cs.910/ref-24) 2021b; 23 Prakash (10.7717/peerj-cs.910/ref-42) 2014a Hashim (10.7717/peerj-cs.910/ref-30) 2021; 51 Prakash (10.7717/peerj-cs.910/ref-43) 2014b Zeidabadi (10.7717/peerj-cs.910/ref-52) 2022; 70 Dehghani (10.7717/peerj-cs.910/ref-9) 2020b; 12 Moghdani (10.7717/peerj-cs.910/ref-39) 2018; 64 Vassallo (10.7717/peerj-cs.910/ref-49) 2019; 16 Goldberg (10.7717/peerj-cs.910/ref-28) 1988; 3 Mohammadi-Balani (10.7717/peerj-cs.910/ref-40) 2021; 152 Abualigah (10.7717/peerj-cs.910/ref-3) 2021; 157 Prakash (10.7717/peerj-cs.910/ref-44) 2021; 10 Cavazzuti (10.7717/peerj-cs.910/ref-6) 2013 Dehghani (10.7717/peerj-cs.910/ref-14) 2020f; 13 Rao (10.7717/peerj-cs.910/ref-45) 2011; 43 Karami (10.7717/peerj-cs.910/ref-31) 2021; 156 Dhiman (10.7717/peerj-cs.910/ref-19) 2021; 222 Lera (10.7717/peerj-cs.910/ref-35) 2018; 71 Mirjalili (10.7717/peerj-cs.910/ref-38) 2014; 69 Pereira (10.7717/peerj-cs.910/ref-41) 2021; 170 Beyer (10.7717/peerj-cs.910/ref-5) 2002; 1 Dehghani (10.7717/peerj-cs.910/ref-15) 2019; 2019 Wolpert (10.7717/peerj-cs.910/ref-51) 1997; 1 Dhiman (10.7717/peerj-cs.910/ref-20) 2020; 12 Dhiman (10.7717/peerj-cs.910/ref-21) 2017; 114 MiarNaeimi (10.7717/peerj-cs.910/ref-36) 2021; 213 Dehghani (10.7717/peerj-cs.910/ref-13) 2020e; 10 Zhao (10.7717/peerj-cs.910/ref-53) 2020; 32 Kaveh (10.7717/peerj-cs.910/ref-33) 2016; 6 Banzhaf (10.7717/peerj-cs.910/ref-4) 1998; 1 Dorigo (10.7717/peerj-cs.910/ref-22) 1996; 26 Faramarzi (10.7717/peerj-cs.910/ref-25) 2014; 31 Dehghani (10.7717/peerj-cs.910/ref-16) 2020g; 13 Mirjalili (10.7717/peerj-cs.910/ref-37) 2016; 95 Simon (10.7717/peerj-cs.910/ref-47) 2008; 12 Storn (10.7717/peerj-cs.910/ref-48) 1997; 11 Han (10.7717/peerj-cs.910/ref-29) 2021; 51 Dehghani (10.7717/peerj-cs.910/ref-17) 2020; 2 Kennedy (10.7717/peerj-cs.910/ref-34) 1995 Fogel (10.7717/peerj-cs.910/ref-27) 1966 Dehghani (10.7717/peerj-cs.910/ref-12) 2020d; 10 Wilcoxon (10.7717/peerj-cs.910/ref-50) 1992 Abedinpourshotorban (10.7717/peerj-cs.910/ref-2) 2016; 26 Dehghani (10.7717/peerj-cs.910/ref-7) 2021; 21 Doumari (10.7717/peerj-cs.910/ref-23) 2021a; 14 Dehghani (10.7717/peerj-cs.910/ref-8) 2020a; 13
References_xml	– year: 2014b ident: 10.7717/peerj-cs.910/ref-43 article-title: An efficient workflow scheduling approach in cloud computing – volume: 26 start-page: 8 year: 2016 ident: 10.7717/peerj-cs.910/ref-2 article-title: Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2015.07.002 – volume: 12 start-page: 8457 year: 2020 ident: 10.7717/peerj-cs.910/ref-20 article-title: A novel algorithm for global optimization: rat swarm optimizer publication-title: Journal of Ambient Intelligence and Humanized Computing doi: 10.1007/s12652-020-02580-0 – volume: 14 start-page: 545 issue: 3 year: 2021a ident: 10.7717/peerj-cs.910/ref-23 article-title: Ring toss game-based optimization algorithm for solving various optimization problems publication-title: International Journal of Intelligent Engineering and Systems doi: 10.22266/ijies2021.0630.46 – volume: 21 start-page: 5214 issue: 15 year: 2021 ident: 10.7717/peerj-cs.910/ref-7 article-title: Cat and mouse based optimizer: a new nature-inspired optimization algorithm publication-title: Sensors doi: 10.3390/s21155214 – volume: 12 start-page: 702 issue: 6 year: 2008 ident: 10.7717/peerj-cs.910/ref-47 article-title: Biogeography-based optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2008.919004 – volume: 1 start-page: 3 year: 2002 ident: 10.7717/peerj-cs.910/ref-5 article-title: Evolution strategies—a comprehensive introduction publication-title: Natural Computing doi: 10.1023/A:1015059928466 – start-page: 77 year: 2013 ident: 10.7717/peerj-cs.910/ref-6 article-title: Deterministic optimization – volume: 13 start-page: 364 issue: 6 year: 2020c ident: 10.7717/peerj-cs.910/ref-11 article-title: MLO: multi leader optimizer publication-title: International Journal of Intelligent Engineering and Systems doi: 10.22266/ijies2020.1231.32 – volume: 10 start-page: 6173 issue: 18 year: 2020e ident: 10.7717/peerj-cs.910/ref-13 article-title: A spring search algorithm applied to engineering optimization problems publication-title: Applied Sciences doi: 10.3390/app10186173 – volume: 11 start-page: 341 issue: 4 year: 1997 ident: 10.7717/peerj-cs.910/ref-48 article-title: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces publication-title: Journal of Global Optimization doi: 10.1023/A:1008202821328 – volume: 222 start-page: 106926 issue: 35 year: 2021 ident: 10.7717/peerj-cs.910/ref-19 article-title: SSC: a hybrid nature-inspired meta-heuristic optimization algorithm for engineering applications publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2021.106926 – volume: 152 start-page: 113377 issue: 4 year: 2020 ident: 10.7717/peerj-cs.910/ref-26 article-title: Marine predators algorithm: a nature-inspired metaheuristic publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113377 – start-page: 196 year: 1992 ident: 10.7717/peerj-cs.910/ref-50 article-title: Individual comparisons by ranking methods – volume: 95 start-page: 51 issue: 12 year: 2016 ident: 10.7717/peerj-cs.910/ref-37 article-title: The whale optimization algorithm publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2016.01.008 – volume: 32 start-page: 9383 issue: 13 year: 2020 ident: 10.7717/peerj-cs.910/ref-53 article-title: Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm publication-title: Neural Computing and Applications doi: 10.1007/s00521-019-04452-x – volume: 2019 start-page: 69 issue: 4 year: 2019 ident: 10.7717/peerj-cs.910/ref-15 article-title: Energy commitment: a planning of energy carrier based on energy consumption publication-title: Electrical Engineering & Electromechanics doi: 10.20998/2074-272X.2019.4.10 – volume: 51 start-page: 775 issue: 2 year: 2021 ident: 10.7717/peerj-cs.910/ref-29 article-title: State-transition simulated annealing algorithm for constrained and unconstrained multi-objective optimization problems publication-title: Applied Intelligence doi: 10.1007/s10489-020-01836-8 – volume: 16 start-page: 3854 issue: 9 year: 2019 ident: 10.7717/peerj-cs.910/ref-49 article-title: Contemporary technologies and methods for cross-platform application development publication-title: Journal of Computational and Theoretical Nanoscience doi: 10.1166/jctn.2019.8261 – volume: 26 start-page: 29 issue: 1 year: 1996 ident: 10.7717/peerj-cs.910/ref-22 article-title: Ant system: optimization by a colony of cooperating agents publication-title: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) doi: 10.1109/3477.484436 – volume: 43 start-page: 303 issue: 3 year: 2011 ident: 10.7717/peerj-cs.910/ref-45 article-title: Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems publication-title: Computer-Aided Design doi: 10.1016/j.cad.2010.12.015 – year: 2014a ident: 10.7717/peerj-cs.910/ref-42 article-title: A novel scheduling approach for workflow management in cloud computing doi: 10.1109/ICSPCT.2014.6884887 – volume: 1 start-page: 67 issue: 1 year: 1997 ident: 10.7717/peerj-cs.910/ref-51 article-title: No free lunch theorems for optimization publication-title: IEEE Transactions on Evolutionary computation doi: 10.1109/4235.585893 – volume: 2 start-page: 1 issue: 10 year: 2020 ident: 10.7717/peerj-cs.910/ref-17 article-title: Momentum search algorithm: a new meta-heuristic optimization algorithm inspired by momentum conservation law publication-title: SN Applied Sciences doi: 10.1007/s42452-020-03511-6 – volume: 3 start-page: 95 issue: 2 year: 1988 ident: 10.7717/peerj-cs.910/ref-28 article-title: Genetic algorithms and machine learning publication-title: Machine Learning doi: 10.1023/A:1022602019183 – volume: 69 start-page: 46 year: 2014 ident: 10.7717/peerj-cs.910/ref-38 article-title: Grey wolf optimizer publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2013.12.007 – volume: 10 start-page: 1320 issue: 11 year: 2021 ident: 10.7717/peerj-cs.910/ref-44 article-title: Multi-dependency and time based resource scheduling algorithm for scientific applications in cloud computing publication-title: Electronics doi: 10.3390/electronics10111320 – volume: 51 start-page: 1531 issue: 3 year: 2021 ident: 10.7717/peerj-cs.910/ref-30 article-title: Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems publication-title: Applied Intelligence doi: 10.1007/s10489-020-01893-z – volume: 70 start-page: 5631 issue: 3 year: 2022 ident: 10.7717/peerj-cs.910/ref-52 article-title: MLA: a new mutated leader algorithm for solving optimization problems publication-title: Computers, Materials & Continua doi: 10.32604/cmc.2022.021072 – year: 1995 ident: 10.7717/peerj-cs.910/ref-34 article-title: Particle swarm optimization doi: 10.1109/ICNN.1995.488968 – volume: 157 start-page: 107250 issue: 11 year: 2021 ident: 10.7717/peerj-cs.910/ref-3 article-title: Aquila optimizer: a novel meta-heuristic optimization algorithm publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2021.107250 – volume: 6 start-page: 469 issue: 4 year: 2016 ident: 10.7717/peerj-cs.910/ref-33 article-title: A novel meta-heuristic algorithm: tug of war optimization publication-title: International Journal of Optimization in Civil Engineering – year: 1966 ident: 10.7717/peerj-cs.910/ref-27 article-title: Artificial intelligence through simulated evolution – volume: 13 start-page: 216 issue: 10 year: 2020g ident: 10.7717/peerj-cs.910/ref-16 article-title: HOGO: hide objects game optimization publication-title: International Journal of Intelligent Engineering and Systems doi: 10.22266/ijies2020.0831.19 – volume: 64 start-page: 161 issue: 5 year: 2018 ident: 10.7717/peerj-cs.910/ref-39 article-title: Volleyball premier league algorithm publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2017.11.043 – volume: 156 start-page: 107224 issue: 4 year: 2021 ident: 10.7717/peerj-cs.910/ref-31 article-title: Flow Direction Algorithm (FDA): a novel optimization approach for solving optimization problems publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2021.107224 – volume: 11 start-page: 1286 issue: 3 year: 2021 ident: 10.7717/peerj-cs.910/ref-10 article-title: Binary spring search algorithm for solving various optimization problems publication-title: Applied Sciences doi: 10.3390/app11031286 – volume: 36 start-page: 5887 issue: 10 year: 2021 ident: 10.7717/peerj-cs.910/ref-1 article-title: Artificial gorilla troops optimizer: a new nature-inspired metaheuristic algorithm for global optimization problems publication-title: International Journal of Intelligent Systems doi: 10.1002/int.22535 – volume: 12 start-page: 10053 issue: 23 year: 2020b ident: 10.7717/peerj-cs.910/ref-9 article-title: Genetic algorithm for energy commitment in a power system supplied by multiple energy carriers publication-title: Sustainability doi: 10.3390/su122310053 – volume: 31 start-page: 209 issue: 3 year: 2014 ident: 10.7717/peerj-cs.910/ref-25 article-title: A novel hybrid cellular automata-linear programming approach for the optimal sizing of planar truss structures publication-title: Civil Engineering and Environmental Systems doi: 10.1080/10286608.2013.820280 – volume: 10 start-page: 7683 issue: 21 year: 2020d ident: 10.7717/peerj-cs.910/ref-12 article-title: DM: Dehghani method for modifying optimization algorithms publication-title: Applied Sciences doi: 10.3390/app10217683 – volume: 13 start-page: 514 issue: 5 year: 2020a ident: 10.7717/peerj-cs.910/ref-8 article-title: Football game based optimization: an application to solve energy commitment problem publication-title: International Journal of Intelligent Engineering and Systems doi: 10.22266/ijies2020.1031.45 – volume: 71 start-page: 193 issue: 1 year: 2018 ident: 10.7717/peerj-cs.910/ref-35 article-title: GOSH: derivative-free global optimization using multi-dimensional space-filling curves publication-title: Journal of Global Optimization doi: 10.1007/s10898-017-0589-7 – volume: 213 start-page: 106711 issue: 2 year: 2021 ident: 10.7717/peerj-cs.910/ref-36 article-title: Horse herd optimization algorithm: a nature-inspired algorithm for high-dimensional optimization problems publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2020.106711 – volume: 23 start-page: 491 issue: 4 year: 2021b ident: 10.7717/peerj-cs.910/ref-24 article-title: A new two-stage algorithm for solving optimization problems publication-title: Entropy doi: 10.3390/e23040491 – volume: 1 volume-title: Genetic programming: an introduction year: 1998 ident: 10.7717/peerj-cs.910/ref-4 doi: 10.1007/BFb0055923 – volume: 13 start-page: 286 issue: 5 year: 2020f ident: 10.7717/peerj-cs.910/ref-14 article-title: Darts game optimizer: a new optimization technique based on darts game publication-title: International Journal of Intelligent Engineering and Systems doi: 10.22266/ijies2020.1031.26 – volume: 170 start-page: 114522 issue: 11 year: 2021 ident: 10.7717/peerj-cs.910/ref-41 article-title: Lichtenberg algorithm: a novel hybrid physics-based meta-heuristic for global optimization publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.114522 – volume: 179 start-page: 2232 issue: 13 year: 2009 ident: 10.7717/peerj-cs.910/ref-46 article-title: GSA: a gravitational search algorithm publication-title: Information Sciences doi: 10.1016/j.ins.2009.03.004 – volume: 114 start-page: 48 issue: 10 year: 2017 ident: 10.7717/peerj-cs.910/ref-21 article-title: Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2017.05.014 – volume: 21 start-page: 4567 issue: 13 year: 2021 ident: 10.7717/peerj-cs.910/ref-18 article-title: Teamwork optimization algorithm: a new optimization approach for function minimization/maximization publication-title: Sensors doi: 10.3390/s21134567 – volume: 90 start-page: 103541 issue: 2 year: 2020 ident: 10.7717/peerj-cs.910/ref-32 article-title: Tunicate swarm algorithm: a new bio-inspired based metaheuristic paradigm for global optimization publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2020.103541 – volume: 152 start-page: 107050 year: 2021 ident: 10.7717/peerj-cs.910/ref-40 article-title: Golden eagle optimizer: a nature-inspired metaheuristic algorithm publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2020.107050
SSID	ssj0001511119
Score	2.4478326
Snippet	In this paper, a novel evolutionary-based method, called Average and Subtraction-Based Optimizer (ASBO), is presented to attain suitable quasi-optimal...
SourceID	doaj pubmedcentral proquest gale pubmed crossref
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source
StartPage	e910
SubjectTerms	Algorithm of best and worst members of the population Algorithms Algorithms and Analysis of Algorithms Analysis Birth rate Design Evolution & development Exploitation Genetic algorithms Heuristic methods Linear programming Mathematical optimization Mathematical programming Optimization Optimization algorithm Optimization algorithms Optimization problem Optimization Theory and Computation Performance evaluation Pheromones Population Problem solving Simulation Subtraction Teaching
SummonAdditionalLinks	– databaseName: Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQxYEL70egIINAHFCo6_gRHxdEBZcK8ZB6s2zHaRd1k1WSLT-Fv8tMko0SIcSFq2eiOJ7xzDfRPAh5KTQrECenTAeZikLz1OXapEoZEcFlg1cU_bAJfXqan52Zz7NRX5gTNrQHHg7uCP2R4OHYQcAtwDuCNTXSueC9DN4wj9aXaTMLpob6YDQFZsh01xCyHG1jbH6koX1rsFh25oP6Vv1_GuSZR1pmS87cz8ltcnPEjXQ17PcOuRaru-TWfiYDHa_oPfJrRQEo0xoswWYssaTu8rxu1t3FhqLPKigugQaDJaGuKmi7810z1DfQuqSACKmHrfW0nzWgQ7qJODak3VO309AvCpCXgvbiXwl6BWF3vWuX7x4H1rT3yfeTD9_ef0zH4QtpkNp0qQsh48z5MneGKx9Z7mVpnPQmKGbKAGGaCmVUHk7a8SIyb2L0LkC8HXNTltkDclDVVXxEaJY5VyiBUOsY0Fv0IjhuHFfa5yrzJiFv9uKwYexMjgMyLi1EKCg82wvPhtaC8BLyauLeDh05_sL3DiU78WAf7X4BtMuO2mX_pV0JeYF6YbFTRoWpOOdu17b209cvdqUZenYhsoS8HpnKGuXlxsoG-HpsrrXgPFxwwlUOS_Je_exoSlrLVQZWOZNKJOT5RMYnMT2uiiBY4JG5koBFeUIeDto6fXcm-w5AQNELPV4czJJSrS_6RuOGCcG1fPw_TvIJucGxcgTT9_QhOeiaXXxKroerbt02z_rb-xvVNk7w priority: 102 providerName: Directory of Open Access Journals
Title	A new optimization algorithm based on average and subtraction of the best and worst members of the population for solving various optimization problems
URI	https://www.ncbi.nlm.nih.gov/pubmed/35494852 https://www.proquest.com/docview/2637033564 https://www.proquest.com/docview/2658651722 https://pubmed.ncbi.nlm.nih.gov/PMC9044275 https://doaj.org/article/129342c1a076463584795aacbb5cb90b
Volume	8
WOSCitedRecordID	wos000768601100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: DOA dateStart: 20150101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: M~E dateStart: 20150101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: P5Z dateStart: 20150527 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: K7- dateStart: 20150527 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: BENPR dateStart: 20150527 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: PIMPY dateStart: 20150527 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9NAEF7RlgMXyptAiRYE4oDcuvY-vCeUolRUqFFUQApcVrvrdVrU2CFOyo2_wd9lxtmksRBcuPjgmSTrzOub9ewMIS-ZjHPEyVEsHY9YLpPIZFJFQijmIWRDVGTNsAk5GGSjkRqGDbc6lFWufGLjqPPK4R75QSJSUM6UC_Z2-j3CqVH4djWM0NgiO9glAUc3DPnX6z0Wjg5BLevdJSQuB1PvZ98iV-8rPDK7EYmahv1_uuWNuNSumdwIQse7_7v8O-R2gJ-0t9SXu-SGL--R3dVoBxos_T751aOAt2kFDmUSTmpSczmGb5yfTyiGvpziLTAEcEjUlDmtF3Y-Wx6ToFVBAVhSC-traD8qAJl04nH6SL2iTtezwyggZwpGgJsb9Aqy92pRt387zL2pH5DPx_1P795HYYZD5LhU88g4lyaxsUVmVCKsjzPLC2W4VU7EqnCQ7QlXeGFBVCbJfWyV99Y4SNt9pooifUi2y6r0jwlNU2NywRCxHQII9JY5kyiTCGkzkVrVIW9W8tQuNDjHORuXGhIdlL5upK9drUH6HfJqzT1dNvb4C98RqsaaB9txNzeq2VgH69YImljiDk0sBQMIByFfcWOctdxZFdsOeYGKpbHhRokVPWOzqGt98vFM92SMAIGxtENeB6aiQnmZcEACnh57dLU491qc4BFcm7zSQR08Uq2vFbBDnq_J-Emssis9CBZ4eCY4QNqkQx4t1X393ClvGgkBRbYMofXHtCnlxXnTr1zFjCWSP_n3sp6SWwkeLcH6PrlHtuezhX9Gbrqr-UU965ItOcq6ZOeoPxiedZudE7h-kBFcT3_2u43hA314cjr88huIG2Tx
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LbxMxELZKQYIL5U2ggEFUHNDSrddrrw8IhUfVKCVCUKTejO31pkXNJuwmRfwS_gW_kZl9pFkhuPXALdqZPGzP4xtnHoQ85TJMEScHoXRxwFPJApNIFQihuAeXDV6RV8Mm5GiUHB6qD2vkV1sLg2mVrU2sDHU6dXhHvs1EBMIZxYK_mn0LcGoU_rvajtCoxWLof3yHkK18OXgL57vF2O67gzd7QTNVIHCxVPPAOBex0NgsMYoJ68PExpkysVVOhCpzEH8Il3lhAYoYlvrQKu-tcRBI-kRlWQSfe4Fc5FEiUa-GMji704nRAKk6v15CoLQ98774GrjyhcIS3RXPVw0I-NMNrPjBbo7mitPb3fjftusaudrAa9qv9eE6WfP5DbLRjq6gjSW7SX72KcQTdAoGc9JUolJzMoYVzI8mFF17SvERKDoYXGrylJYLOy_qMhA6zSgAZ2phPyoaLBBeTTxOVylb6mw5G41CZEBByfHyhp6aAvOOu9_dzPUpb5HP57I9t8l6Ps39XUKjyJhUcESkOwByveXOMGWYkDYRkVU98ryVH-2aBu44R-REQyCH0qYradOu1CBtPbK15J7VjUv-wvcaRXHJg-3GqwfTYqwb66URFHLmdkwoBQeICpBGxcY4a2NnVWh75AkKssaGIjlmLI3Noiz14NNH3ZchAiDOox551jBlUzwv0xSAwOqxB1mHc7PDCRbPdcmtzOvG4pb6TOB75PGSjO_ELMLcw8ECT5yIGCA765E7tXot1x3FVaMkoMiO4nU2pkvJj4-qfuwq5JzJ-N6_f9Yjcnnv4P2-3h-MhvfJFYZlNJjLKDfJ-rxY-AfkkjudH5fFw8qoUPLlvNXyNz4Avsw
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELZKQYgL5c1CAYOoOKCwqeNHfECoUCqqotWKh1T1YmzH2Ra1yZJsi_gl_Bd-HTPZZNsIwa0HbqvM7MP2zDcz3nkQ8pSrOEM_OYqVFxHPFItsqnQkpeYBTDZYRd4Mm1CjUbq7q8dL5FdXC4NplR0mNkCdlR7vyIdMJiCciZB8mLdpEePNrVfTbxFOkMJ_WrtxGnMR2Qk_vkP4Vr_c3oSzXmNs6-2nN--idsJA5IXSs8h6n7DYujy1mkkX4tSJXFvhtJexzj3EItLnQTpwSyzLQux0CM56CCpDqvM8gc-9QC4qiDExnXAs9k7vdwSCkZ7n2isImobTEKqvka9faCzXPWMFm2EBf5qEMzaxn695xgBurfzPW3eNXG3dbrox15PrZCkUN8hKN9KCtgh3k_zcoBBn0BKA9KitUKX2cAIrmO0fUTT5GcVHAAAAxNQWGa2P3ayal4fQMqfgUFMHe9PQYIHw6ijg1JW6o04XM9MoRAwUlB8vdeiJrTAfuf_d7byf-hb5fC7bc5ssF2UR7hKaJNZmkqOnug7Ob3DcW6Ytk8qlMnF6QJ53smR829gd54scGgjwUPJMI3nG1wYkb0DWFtzTeUOTv_C9RrFc8GAb8uZBWU1Mi2oGnUXO_LqNleTguoKro4W13jnhnY7dgDxBoTbYaKRAuZvY47o22x8_mA0Vo2PEeTIgz1qmvMTzsm1hCKwee5P1OFd7nICEvk_u5N-0SFybU-EfkMcLMr4TswuLAAcLPCKVAlx5NiB35qq2WHcimgZKQFE9JextTJ9SHOw3fdp1zDlT4t6_f9Yjchm00bzfHu3cJ1cYVtdgiqNaJcuz6jg8IJf8yeygrh42-ELJl_PWyt80ksfw
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+new+optimization+algorithm+based+on+average+and+subtraction+of+the+best+and+worst+members+of+the+population+for+solving+various+optimization+problems&rft.jtitle=PeerJ.+Computer+science&rft.au=Dehghani%2C+Mohammad&rft.au=Hub%C3%A1lovsk%C3%BD%2C+%C5%A0t%C4%9Bp%C3%A1n&rft.au=Trojovsk%C3%BD%2C+Pavel&rft.date=2022-03-07&rft.pub=PeerJ+Inc&rft.eissn=2376-5992&rft.volume=8&rft_id=info:doi/10.7717%2Fpeerj-cs.910&rft_id=info%3Apmid%2F35494852&rft.externalDocID=PMC9044275
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2376-5992&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2376-5992&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2376-5992&client=summon