Classification of underwater acoustical dataset using neural network trained by Chimp Optimization Algorithm
Due to the variability of the radiated signal of the underwater targets, the classification of the underwater acoustical dataset is a challenging problem in the real world application. In this paper, to classify underwater acoustical targets, first, a new meta-heuristic Chimp Optimization Algorithm...
Uložené v:
| Vydané v: | Applied acoustics Ročník 157; s. 107005 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
01.01.2020
|
| Predmet: | |
| ISSN: | 0003-682X, 1872-910X |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Due to the variability of the radiated signal of the underwater targets, the classification of the underwater acoustical dataset is a challenging problem in the real world application. In this paper, to classify underwater acoustical targets, first, a new meta-heuristic Chimp Optimization Algorithm (ChOA) inspired by chimp hunting behaviour is developed for training an Artificial Neural Network (ANN). Second, a new underwater acoustical dataset is developed using passive propeller acoustic data collected in a laboratory. To evaluate the proposed classifier, this algorithm is compared to the Ion Motion Algorithm (IMA), Gray Wolf Optimization (GWO), and a hybrid algorithm. Measured metrics are convergence speed, the possibility of trapping in local minimum and classification accuracy. The results show that the newly proposed algorithm in most cases provides better or comparable performance compared to the other benchmark algorithms. |
|---|---|
| AbstractList | Due to the variability of the radiated signal of the underwater targets, the classification of the underwater acoustical dataset is a challenging problem in the real world application. In this paper, to classify underwater acoustical targets, first, a new meta-heuristic Chimp Optimization Algorithm (ChOA) inspired by chimp hunting behaviour is developed for training an Artificial Neural Network (ANN). Second, a new underwater acoustical dataset is developed using passive propeller acoustic data collected in a laboratory. To evaluate the proposed classifier, this algorithm is compared to the Ion Motion Algorithm (IMA), Gray Wolf Optimization (GWO), and a hybrid algorithm. Measured metrics are convergence speed, the possibility of trapping in local minimum and classification accuracy. The results show that the newly proposed algorithm in most cases provides better or comparable performance compared to the other benchmark algorithms. |
| ArticleNumber | 107005 |
| Author | Khishe, M. Mosavi, M.R. |
| Author_xml | – sequence: 1 givenname: M. surname: Khishe fullname: Khishe, M. email: m_khishe@alumni.iust.ac.ir organization: Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran – sequence: 2 givenname: M.R. surname: Mosavi fullname: Mosavi, M.R. email: m_mosavi@iust.ac.ir organization: Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran 16846-13114, Iran |
| BookMark | eNqFkM1qwzAQhEVJoUnaVyh6AaeSjP-ghwbTPwjk0kJuQpZWiVJbNpLckD59nbq99JLTsrPMMPvN0MS2FhC6pWRBCU3v9gvRCdn2PiwYocUgZoQkF2hK84xFBSWbCZoSQuIozdnmCs283w8rYUkyRXVZC--NNlIE01rcatxbBe4gAjg8xg63GisRhIeAe2_sFlvo3SBaCIfWfeDghLGgcHXE5c40HV53wTTma8xc1tvWmbBrrtGlFrWHm985R-9Pj2_lS7RaP7-Wy1UkY8pClIssy4nOK6arKtFSk6zKmdJpFlMQGnIiCwUy1ykFmsWgAKhKJUslpFrrIp6j-zFXutZ7B5pLE366nIrWnBJ-Isf3_I8cP5HjI7nBnv6zd840wh3PGx9GIwzPfRpw3EsDVoIyDmTgqjXnIr4BiGuUDg |
| CitedBy_id | crossref_primary_10_1007_s11042_022_12882_4 crossref_primary_10_1007_s42235_023_00414_1 crossref_primary_10_1016_j_jhydrol_2020_125130 crossref_primary_10_1007_s12555_019_1014_4 crossref_primary_10_1016_j_jobe_2022_105187 crossref_primary_10_1007_s12145_023_01160_y crossref_primary_10_1007_s12530_022_09456_y crossref_primary_10_1109_ACCESS_2023_3337602 crossref_primary_10_1016_j_cogr_2022_08_002 crossref_primary_10_1016_j_eswa_2022_117295 crossref_primary_10_1016_j_apor_2021_102837 crossref_primary_10_1016_j_energy_2022_125259 crossref_primary_10_1016_j_bspc_2021_102764 crossref_primary_10_1007_s11042_024_20313_9 crossref_primary_10_1080_21681163_2022_2157748 crossref_primary_10_1016_j_eswa_2022_116887 crossref_primary_10_3390_jmse10101565 crossref_primary_10_3390_jmse11010003 crossref_primary_10_1155_2022_9619530 crossref_primary_10_1109_ACCESS_2022_3186021 crossref_primary_10_1016_j_eswa_2024_124498 crossref_primary_10_1007_s11277_021_08902_5 crossref_primary_10_1007_s12530_022_09425_5 crossref_primary_10_1016_j_cose_2024_104166 crossref_primary_10_3390_math10071100 crossref_primary_10_1016_j_eswa_2021_115651 crossref_primary_10_1007_s42452_021_04598_1 crossref_primary_10_1007_s11063_022_10846_1 crossref_primary_10_1186_s40537_024_00979_6 crossref_primary_10_23919_PCMP_2023_000325 crossref_primary_10_1007_s11277_021_09432_w crossref_primary_10_14201_adcaij_29969 crossref_primary_10_2478_jaiscr_2024_0018 crossref_primary_10_1007_s11063_022_11055_6 crossref_primary_10_1016_j_heliyon_2024_e30134 crossref_primary_10_1016_j_knosys_2023_110494 crossref_primary_10_1080_10106049_2022_2136265 crossref_primary_10_1016_j_apacoust_2020_107332 crossref_primary_10_1007_s12530_023_09547_4 crossref_primary_10_1088_1742_6596_1963_1_012027 crossref_primary_10_1007_s13369_020_05228_5 crossref_primary_10_1016_j_est_2024_111008 crossref_primary_10_1109_JSEN_2025_3570232 crossref_primary_10_1016_j_heliyon_2024_e26799 crossref_primary_10_1109_ACCESS_2024_3520706 crossref_primary_10_1016_j_apacoust_2020_107859 crossref_primary_10_1016_j_solener_2023_02_036 crossref_primary_10_1016_j_chaos_2024_115972 crossref_primary_10_1007_s11276_023_03464_9 crossref_primary_10_1007_s00521_022_08075_7 crossref_primary_10_1142_S2301385025500761 crossref_primary_10_1007_s12065_023_00884_6 crossref_primary_10_1007_s00366_021_01591_5 crossref_primary_10_1016_j_heliyon_2024_e32400 crossref_primary_10_1080_13682199_2023_2178094 crossref_primary_10_1007_s42835_023_01585_x crossref_primary_10_1016_j_bspc_2023_105053 crossref_primary_10_1007_s00521_021_06775_0 crossref_primary_10_1007_s11042_025_20707_3 crossref_primary_10_1038_s41598_021_88799_z crossref_primary_10_1007_s12530_022_09443_3 crossref_primary_10_1007_s12665_021_10098_7 crossref_primary_10_1016_j_oceaneng_2024_117252 crossref_primary_10_1155_2022_1326325 crossref_primary_10_32604_cmc_2022_022322 crossref_primary_10_1016_j_heliyon_2024_e28681 crossref_primary_10_1155_2022_3569261 crossref_primary_10_1186_s42490_021_00056_6 crossref_primary_10_3390_math9182335 crossref_primary_10_1080_00103624_2022_2108828 crossref_primary_10_32604_cmc_2022_020820 crossref_primary_10_1007_s12652_022_03901_1 crossref_primary_10_1007_s12652_021_03564_4 crossref_primary_10_1109_JOE_2022_3164513 crossref_primary_10_1016_j_engappai_2024_109437 crossref_primary_10_1016_j_energy_2023_128454 crossref_primary_10_1049_rsn2_12031 crossref_primary_10_1007_s00500_021_05839_6 crossref_primary_10_1016_j_jclepro_2022_132697 crossref_primary_10_1371_journal_pone_0282514 crossref_primary_10_3233_JIFS_236157 crossref_primary_10_1007_s11063_023_11173_9 crossref_primary_10_1155_2022_6139558 crossref_primary_10_1007_s12530_024_09612_6 crossref_primary_10_1007_s42979_023_02008_4 crossref_primary_10_3390_math9091002 crossref_primary_10_32604_csse_2023_039111 crossref_primary_10_1016_j_apacoust_2022_108774 crossref_primary_10_1007_s00500_023_09174_w crossref_primary_10_1007_s00202_023_01944_x crossref_primary_10_1016_j_aei_2022_101636 crossref_primary_10_1007_s11277_022_10000_z crossref_primary_10_1007_s11265_024_01935_6 crossref_primary_10_1007_s11802_023_5309_y crossref_primary_10_1109_JSEN_2024_3419434 crossref_primary_10_1016_j_eswa_2025_129122 crossref_primary_10_1016_j_knosys_2025_114252 crossref_primary_10_1007_s11042_023_15543_2 crossref_primary_10_1016_j_eswa_2024_123431 crossref_primary_10_1155_2023_3056688 crossref_primary_10_1007_s11227_022_04886_6 crossref_primary_10_1016_j_apacoust_2023_109552 crossref_primary_10_1016_j_knosys_2021_107625 crossref_primary_10_1111_exsy_70076 crossref_primary_10_1016_j_aei_2025_103510 crossref_primary_10_1080_17445302_2023_2169066 crossref_primary_10_1016_j_epsr_2022_109109 crossref_primary_10_1007_s11277_021_08244_2 crossref_primary_10_1109_ACCESS_2021_3105485 crossref_primary_10_3233_JIFS_237339 crossref_primary_10_1016_j_oceaneng_2025_121286 crossref_primary_10_1016_j_eswa_2021_115270 crossref_primary_10_1016_j_asoc_2024_111539 crossref_primary_10_1109_ACCESS_2022_3227046 crossref_primary_10_1007_s42947_025_00611_7 crossref_primary_10_1007_s40747_021_00346_5 |
| Cites_doi | 10.1016/j.ins.2014.01.038 10.3923/tasr.2012.445.455 10.1007/s12110-002-1013-6 10.1016/j.eswa.2011.07.046 10.14311/NNW.2016.26.023 10.1016/j.apacoust.2018.03.012 10.1016/j.oceaneng.2019.04.013 10.1023/A:1021251113462 10.1007/s00500-016-2442-1 10.1163/156853994X00181 10.1016/j.energy.2014.07.078 10.1016/j.cub.2009.05.034 10.1023/A:1022995128597 10.1016/j.apacoust.2016.11.012 10.1016/j.swevo.2011.02.002 10.1007/978-3-540-73729-2_30 10.1016/j.scitotenv.2015.10.037 10.1007/s11277-017-4110-x 10.1016/j.apacoust.2019.05.006 10.24425/aoa.2019.126360 10.1016/j.eswa.2013.03.017 10.1525/aa.1996.98.1.02a00090 10.1016/j.apacoust.2016.11.016 10.1142/S0218126617501857 10.2307/3001968 10.1016/0167-8191(90)90086-O 10.1007/s00521-007-0084-z 10.1007/s10489-014-0645-7 |
| ContentType | Journal Article |
| Copyright | 2019 Elsevier Ltd |
| Copyright_xml | – notice: 2019 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.apacoust.2019.107005 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Physics |
| EISSN | 1872-910X |
| ExternalDocumentID | 10_1016_j_apacoust_2019_107005 S0003682X19305067 |
| GroupedDBID | --K --M -~X .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO ABMAC ABNEU ABYKQ ACDAQ ACFVG ACGFS ACRLP ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIKHN AITUG AIVDX AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BJAXD BKOJK BLXMC CS3 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W JJJVA KOM LY7 M41 MO0 N9A O-L O9- OAUVE OGIMB OZT P-8 P-9 P2P PC. Q38 ROL RPZ SDF SDG SDP SES SPC SPCBC SPD SSQ SST SSZ T5K XPP ZMT ~02 ~G- 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABFNM ABJNI ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADMUD ADNMO AEIPS AEUPX AFFNX AFJKZ AFPUW AGQPQ AI. AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS FEDTE FGOYB G-2 HVGLF HZ~ R2- SET SEW VH1 WUQ ZY4 ~HD |
| ID | FETCH-LOGICAL-c312t-8a7780f8b2fbb5fcf07b82df6731eafe80c9dec8f61e173edee1d6c26ce6fff93 |
| ISICitedReferencesCount | 114 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000495519000015&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0003-682X |
| IngestDate | Sat Nov 29 07:33:40 EST 2025 Tue Nov 18 21:00:27 EST 2025 Fri Feb 23 02:49:33 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Multi-Layer Perceptron Neural Networks Underwater acoustical dataset Classification Chimp Optimization Algorithm |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c312t-8a7780f8b2fbb5fcf07b82df6731eafe80c9dec8f61e173edee1d6c26ce6fff93 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_apacoust_2019_107005 crossref_primary_10_1016_j_apacoust_2019_107005 elsevier_sciencedirect_doi_10_1016_j_apacoust_2019_107005 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-01-01 2020-01-00 |
| PublicationDateYYYYMMDD | 2020-01-01 |
| PublicationDate_xml | – month: 01 year: 2020 text: 2020-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Applied acoustics |
| PublicationYear | 2020 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Stanford, Wallis, Mpongo, Goodall (b0190) 1994; 131 Mosavi, Kaveh, Khishe, Aghababaee (b0170) 2018; 5 Socha, Blum (b0110) 2007; 16 Mosavi, Khishe, Ghamgosar (b0045) 2016; 26 Mosavi, Khishe, Hatam Khani, Shabani (b0090) 2017; 13 Ravakhah, Khishe, Aghababaee, Hashemzadeh (b0075) 2017; 17 Stanford (b0185) 1996; 98 Mosavi, Khishe (b0055) 2017; 26 Couzin, Laidre (b0200) 2009; 19 Yu, Lam, Li (b0145) 2011 Mosavi, Khishe, Aghababaee, Mohammadzadeh (b0015) 2015; 73 Moallem, Razmjooy (b0140) 2012; 7 Boesch (b0205) 2002; 13 Karaboga, Akay, Ozturk (b0115) 2009; 4617 Mosavi, Khishe, Karimi, Malmir (b0215) 2017; 3 Mosavi, Khishe, Moridi (b0230) 2016; 3 Taghavi, Khishe (b0225) 2019; 6 Stanford, Wallis, Mpongo, Goodall (b0210) 1994; 131 Muangkote, Sunat, Chiewchanwattana (b0155) 2014 Khishe, Saffari (b0040) 2019 Mirjalili (b0160) 2015; 43 Mosavi, Khishe, Ebrahimi (b0020) 2015; 7 Tomkins, Bergman (b0195) 2012; 26 Hernández, Fernández, Guerraand, Pérez (b0035) 2017; 119 Pérez, Fernández, Shaout, Guerra (b0010) 2016; 542 Mosavi, Khishe, Akbarisani (b0080) 2017; 95 Ho, Pepyne (b0180) 2002; 115 Derrac, García, Molina, Herrera (b0235) 2011; 1 Khishe, Mosavi (b0030) 2019; 154 Blum, Socha (b0105) 2005 M. Khishe, M. Mosavi, and B. Safarpoor, “Sound of Propeller,” Mendeley Data, v1, 2017. Whitley, Starkweather, Bogart (b0095) 1990; 14 Aljarah, Faris, Mirjalili (b0175) 2018; 22 Wilcoxon (b0240) 1945; 1 Afrakhteh, Mosavi, Khishe, Ayatollahi (b0060) 2018 Pereira, Afonso, Papa, Vale, Ramos, Gastaldello, Souza (b0135) 2013 Uzlu, Kankal, Akpınar, Dede (b0150) 2014; 75 Khishe, Mohammadi (b0025) 2019; 181 Ilonen, Kamarainen, Lampinen (b0120) 2003; 17 Pereira, Rodrigues, Ribeiro, Papa, Weber (b0130) 2014 Kaveh, Khishe, Mosavi (b0085) 2018 Mendes, Cortez, Rocha, Neves (b0100) 2002 Khishe, Mosavi, Moridi (b0070) 2018; 137 Khishe, Mosavi, Kaveh (b0065) 2017; 118 Mosavi, Khishe, Parvizi, Naseri, Ayat (b0050) 2019; 44 Green, Wang, Alam (b0125) 2012; 39 Mirjalili, Mirjalili, Lewis (b0165) 2014; 269 Pérez, Fernández, Guerra, Hernández (b0005) 2013; 40 Mosavi (10.1016/j.apacoust.2019.107005_b0090) 2017; 13 Moallem (10.1016/j.apacoust.2019.107005_b0140) 2012; 7 Mirjalili (10.1016/j.apacoust.2019.107005_b0160) 2015; 43 Khishe (10.1016/j.apacoust.2019.107005_b0030) 2019; 154 Tomkins (10.1016/j.apacoust.2019.107005_b0195) 2012; 26 Mosavi (10.1016/j.apacoust.2019.107005_b0055) 2017; 26 10.1016/j.apacoust.2019.107005_b0220 Mosavi (10.1016/j.apacoust.2019.107005_b0230) 2016; 3 Karaboga (10.1016/j.apacoust.2019.107005_b0115) 2009; 4617 Khishe (10.1016/j.apacoust.2019.107005_b0025) 2019; 181 Khishe (10.1016/j.apacoust.2019.107005_b0040) 2019 Mosavi (10.1016/j.apacoust.2019.107005_b0170) 2018; 5 Khishe (10.1016/j.apacoust.2019.107005_b0070) 2018; 137 Khishe (10.1016/j.apacoust.2019.107005_b0065) 2017; 118 Mosavi (10.1016/j.apacoust.2019.107005_b0080) 2017; 95 Mosavi (10.1016/j.apacoust.2019.107005_b0050) 2019; 44 Ravakhah (10.1016/j.apacoust.2019.107005_b0075) 2017; 17 Whitley (10.1016/j.apacoust.2019.107005_b0095) 1990; 14 Ho (10.1016/j.apacoust.2019.107005_b0180) 2002; 115 Hernández (10.1016/j.apacoust.2019.107005_b0035) 2017; 119 Green (10.1016/j.apacoust.2019.107005_b0125) 2012; 39 Stanford (10.1016/j.apacoust.2019.107005_b0190) 1994; 131 Wilcoxon (10.1016/j.apacoust.2019.107005_b0240) 1945; 1 Mirjalili (10.1016/j.apacoust.2019.107005_b0165) 2014; 269 Stanford (10.1016/j.apacoust.2019.107005_b0210) 1994; 131 Boesch (10.1016/j.apacoust.2019.107005_b0205) 2002; 13 Mosavi (10.1016/j.apacoust.2019.107005_b0045) 2016; 26 Socha (10.1016/j.apacoust.2019.107005_b0110) 2007; 16 Mosavi (10.1016/j.apacoust.2019.107005_b0215) 2017; 3 Uzlu (10.1016/j.apacoust.2019.107005_b0150) 2014; 75 Pereira (10.1016/j.apacoust.2019.107005_b0130) 2014 Ilonen (10.1016/j.apacoust.2019.107005_b0120) 2003; 17 Pérez (10.1016/j.apacoust.2019.107005_b0005) 2013; 40 Pereira (10.1016/j.apacoust.2019.107005_b0135) 2013 Mendes (10.1016/j.apacoust.2019.107005_b0100) 2002 Muangkote (10.1016/j.apacoust.2019.107005_b0155) 2014 Derrac (10.1016/j.apacoust.2019.107005_b0235) 2011; 1 Aljarah (10.1016/j.apacoust.2019.107005_b0175) 2018; 22 Stanford (10.1016/j.apacoust.2019.107005_b0185) 1996; 98 Afrakhteh (10.1016/j.apacoust.2019.107005_b0060) 2018 Couzin (10.1016/j.apacoust.2019.107005_b0200) 2009; 19 Taghavi (10.1016/j.apacoust.2019.107005_b0225) 2019; 6 Mosavi (10.1016/j.apacoust.2019.107005_b0020) 2015; 7 Blum (10.1016/j.apacoust.2019.107005_b0105) 2005 Pérez (10.1016/j.apacoust.2019.107005_b0010) 2016; 542 Mosavi (10.1016/j.apacoust.2019.107005_b0015) 2015; 73 Kaveh (10.1016/j.apacoust.2019.107005_b0085) 2018 Yu (10.1016/j.apacoust.2019.107005_b0145) 2011 |
| References_xml | – volume: 14 start-page: 347 year: 1990 end-page: 361 ident: b0095 article-title: Genetic algorithms and neural networks: optimizing connections and connectivity publication-title: Parallel Comput – volume: 39 start-page: 555 year: 2012 end-page: 563 ident: b0125 article-title: Training neural networks using central force optimization and particle swarm optimization: insights and comparisons publication-title: Expert Syst Appl – volume: 542 start-page: 562 year: 2016 end-page: 577 ident: b0010 article-title: Airport take-off noise assessment aimed to identify responsible aircraft classes publication-title: Sci Total Environ – volume: 118 start-page: 15 year: 2017 end-page: 29 ident: b0065 article-title: Improved migration models of biogeography-based optimization for sonar data set classification using neural network publication-title: Appl Acoust – volume: 44 start-page: 137 year: 2019 end-page: 151 ident: b0050 article-title: Training multi-layer perceptron utilizing adaptive best-mass gravitational search algorithm to classify sonar dataset publication-title: Arch Acoust – start-page: 1 year: 2018 end-page: 24 ident: b0085 article-title: Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network publication-title: Analog Integrated Circuits Signal Process – start-page: 1 year: 2013 end-page: 6 ident: b0135 article-title: Multilayer perceptron neural networks training through charged system search and its application for non-technical losses detection publication-title: IEEE PES Conference on Innovative Smart Grid Technologies Latin America – volume: 137 start-page: 121 year: 2018 end-page: 139 ident: b0070 article-title: Chaotic fractal walk trainer for sonar data set classification using multi-layer perceptron neural network and its hardware implementation publication-title: Appl Acoust – volume: 7 start-page: 50 year: 2015 end-page: 59 ident: b0020 article-title: Classification of sonar targets using OMKC, genetic algorithms and statistical moments publication-title: J Adv Comput Res – start-page: 2 year: 2005 end-page: 9 ident: b0105 article-title: Training feed-forward neural networks with ant colony optimization: an application to pattern classification publication-title: 5th International Conference on Hybrid Intelligent Systems – volume: 13 start-page: 27 year: 2002 end-page: 46 ident: b0205 article-title: Cooperative hunting roles among Taï chimpanzees publication-title: Human Nature – volume: 17 year: 2017 ident: b0075 article-title: Sonar false alarm rate suppression using classification methods based on interior search algorithm publication-title: IJCSNS Int J Comput Sci Network Security – volume: 26 start-page: 94 year: 2012 end-page: 100 ident: b0195 article-title: Genomic monkey business estimates of nearly identical human-chimp DNA similarity re-evaluated using omitted data publication-title: J Creation – volume: 131 start-page: 1 year: 1994 end-page: 18 ident: b0210 article-title: Hunting decisions in wild chimpanzees behaviour publication-title: Behaviour – volume: 26 start-page: 393 year: 2016 end-page: 415 ident: b0045 article-title: Classification of sonar data set using neural network trained by gray wolf optimization publication-title: J Neural Network World – volume: 73 start-page: 11 year: 2015 end-page: 22 ident: b0015 article-title: Approximation of active sonar clutter's statistical parameters using array's effective beam-width publication-title: Iran J Marine Sci Technol – volume: 181 start-page: 98 year: 2019 end-page: 108 ident: b0025 article-title: Sonar target classification using multi-layer perceptron trained by salp swarm algorithm publication-title: Ocean Eng – volume: 131 start-page: 1 year: 1994 end-page: 18 ident: b0190 article-title: Hunting decisions in wild chimpanzees publication-title: Behaviour – volume: 13 start-page: 100 year: 2017 end-page: 111 ident: b0090 article-title: Training radial basis function neural network using stochastic fractal search algorithm to classify sonar dataset publication-title: Iran J Electric Electron Eng – volume: 16 start-page: 235 year: 2007 end-page: 247 ident: b0110 article-title: An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training publication-title: Neural Comput Appl – start-page: 1895 year: 2002 end-page: 1899 ident: b0100 article-title: Particle swarms for feedforward neural network training, learning publication-title: International Joint Conference on Neural Networks – reference: M. Khishe, M. Mosavi, and B. Safarpoor, “Sound of Propeller,” Mendeley Data, v1, 2017. – volume: 154 start-page: 176 year: 2019 end-page: 192 ident: b0030 article-title: Improved whale trainer for sonar datasets classification using neural network publication-title: Appl Acoust – volume: 22 start-page: 1 year: 2018 end-page: 15 ident: b0175 article-title: Optimizing connection weights in neural networks using the whale optimization algorithm publication-title: Soft Comput – volume: 17 start-page: 93 year: 2003 end-page: 105 ident: b0120 article-title: Differential evolution training algorithm for feed-forward neural networks publication-title: Neural Process Lett – volume: 269 start-page: 188 year: 2014 end-page: 209 ident: b0165 article-title: Let a biogeography-based optimizer train your multi-layer perceptron publication-title: J Inform Sci – volume: 40 start-page: 5148 year: 2013 end-page: 5159 ident: b0005 article-title: Aircraft class identification based on take-off noise signal segmentation in time publication-title: Expert Syst Appl – start-page: 209 year: 2014 end-page: 214 ident: b0155 article-title: An improved grey wolf optimizer for training q-gaussian radial basis functional-link nets publication-title: Comput Sci Eng Conf – volume: 7 start-page: 445 year: 2012 end-page: 455 ident: b0140 article-title: A multi-layer perceptron neural network trained by invasive weed optimization for potato color image segmentation publication-title: Trends Appl Sci Res – volume: 1 start-page: 80 year: 1945 end-page: 83 ident: b0240 article-title: Individual comparisons by ranking methods publication-title: Biometrics Bull – start-page: 2083 year: 2011 end-page: 2090 ident: b0145 article-title: Evolutionary artificial neural network based on chemical reaction optimization publication-title: Cong Evol Comput – volume: 119 start-page: 17 year: 2017 end-page: 28 ident: b0035 article-title: Marine mammal sound classification based on a parallel recognition model and octave analysis publication-title: Appl Acoust – start-page: 1 year: 2019 end-page: 19 ident: b0040 article-title: Classification of sonar targets using an MLP neural network trained by dragonfly algorithm publication-title: Wireless Personal Syst – volume: 3 start-page: 99 year: 2017 end-page: 112 ident: b0215 article-title: Marine propellers design using particle swarm optimization with independent groups to improve efficiency and reduce cavitation publication-title: J Marine Technol – volume: 26 start-page: 1 year: 2017 end-page: 20 ident: b0055 article-title: Training a feed-forward neural network using particle swarm optimizer with autonomous groups for sonar target classification publication-title: J Circuits, Syst, Comput – volume: 95 start-page: 4623 year: 2017 end-page: 4642 ident: b0080 article-title: Neural network trained by biogeography-based optimizer with chaos for sonar data set classification publication-title: J Wireless Personal Commun – volume: 98 start-page: 96 year: 1996 end-page: 113 ident: b0185 article-title: The hunting ecology of wild chimpanzees: implications for the evolutionary ecology of pliocene hominids publication-title: Am Anthropol – volume: 4617 start-page: 318 year: 2009 end-page: 329 ident: b0115 article-title: Artificial Bee Colony (ABC) optimization algorithm for training feed-forward neural networks publication-title: Lect Notes Comput Sci – volume: 5 start-page: 1 year: 2018 end-page: 12 ident: b0170 article-title: Design and implementation a sonar data set classifier using multi-layer perceptron neural network trained by elephant herding optimization publication-title: Iran J Marine Technol – volume: 1 start-page: 3 year: 2011 end-page: 18 ident: b0235 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 – volume: 3 start-page: 1 year: 2016 end-page: 13 ident: b0230 article-title: Classification of sonar target using hybrid particle swarm and gravitational search publication-title: J Marine Technol – volume: 19 start-page: 633 year: 2009 end-page: 635 ident: b0200 article-title: Fission-fusion populations publication-title: Curr Biol – start-page: 1 year: 2018 end-page: 15 ident: b0060 article-title: Accurate classification of EEG signals using neural networks trained by hybrid population-physic-based algorithm publication-title: Int J Automation Comput – volume: 6 start-page: 133 year: 2019 end-page: 146 ident: b0225 article-title: A Modified grey wolf optimizer by individual best memory and penalty factor for sonar and radar dataset’s classification publication-title: Iran J Marine Technol – volume: 115 start-page: 549 year: 2002 end-page: 570 ident: b0180 article-title: Simple explanation of the no-free-lunch theorem and its implications publication-title: J Optim Theory Appl – volume: 75 start-page: 295 year: 2014 end-page: 303 ident: b0150 article-title: Estimates of energy consumption in turkey using neural networks with the teaching-learning-based optimization algorithm publication-title: Energy – start-page: 14 year: 2014 end-page: 17 ident: b0130 article-title: Social-spider optimization-based artificial neural networks training and its applications for Parkinson’s disease identification publication-title: IEEE Symposium on Computer-based Medical Systems – volume: 43 start-page: 150 year: 2015 end-page: 161 ident: b0160 article-title: How effective is the grey wolf optimizer in training multi-layer perceptrons publication-title: Appl Intelligence – start-page: 1 year: 2018 ident: 10.1016/j.apacoust.2019.107005_b0085 article-title: Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network publication-title: Analog Integrated Circuits Signal Process – volume: 269 start-page: 188 year: 2014 ident: 10.1016/j.apacoust.2019.107005_b0165 article-title: Let a biogeography-based optimizer train your multi-layer perceptron publication-title: J Inform Sci doi: 10.1016/j.ins.2014.01.038 – volume: 7 start-page: 50 issue: 1 year: 2015 ident: 10.1016/j.apacoust.2019.107005_b0020 article-title: Classification of sonar targets using OMKC, genetic algorithms and statistical moments publication-title: J Adv Comput Res – volume: 5 start-page: 1 issue: 1 year: 2018 ident: 10.1016/j.apacoust.2019.107005_b0170 article-title: Design and implementation a sonar data set classifier using multi-layer perceptron neural network trained by elephant herding optimization publication-title: Iran J Marine Technol – volume: 13 start-page: 100 issue: 1 year: 2017 ident: 10.1016/j.apacoust.2019.107005_b0090 article-title: Training radial basis function neural network using stochastic fractal search algorithm to classify sonar dataset publication-title: Iran J Electric Electron Eng – volume: 7 start-page: 445 issue: 6 year: 2012 ident: 10.1016/j.apacoust.2019.107005_b0140 article-title: A multi-layer perceptron neural network trained by invasive weed optimization for potato color image segmentation publication-title: Trends Appl Sci Res doi: 10.3923/tasr.2012.445.455 – volume: 13 start-page: 27 year: 2002 ident: 10.1016/j.apacoust.2019.107005_b0205 article-title: Cooperative hunting roles among Taï chimpanzees publication-title: Human Nature doi: 10.1007/s12110-002-1013-6 – volume: 39 start-page: 555 issue: 1 year: 2012 ident: 10.1016/j.apacoust.2019.107005_b0125 article-title: Training neural networks using central force optimization and particle swarm optimization: insights and comparisons publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2011.07.046 – volume: 26 start-page: 393 issue: 4 year: 2016 ident: 10.1016/j.apacoust.2019.107005_b0045 article-title: Classification of sonar data set using neural network trained by gray wolf optimization publication-title: J Neural Network World doi: 10.14311/NNW.2016.26.023 – start-page: 1 year: 2013 ident: 10.1016/j.apacoust.2019.107005_b0135 article-title: Multilayer perceptron neural networks training through charged system search and its application for non-technical losses detection – volume: 137 start-page: 121 year: 2018 ident: 10.1016/j.apacoust.2019.107005_b0070 article-title: Chaotic fractal walk trainer for sonar data set classification using multi-layer perceptron neural network and its hardware implementation publication-title: Appl Acoust doi: 10.1016/j.apacoust.2018.03.012 – volume: 181 start-page: 98 year: 2019 ident: 10.1016/j.apacoust.2019.107005_b0025 article-title: Sonar target classification using multi-layer perceptron trained by salp swarm algorithm publication-title: Ocean Eng doi: 10.1016/j.oceaneng.2019.04.013 – start-page: 2083 year: 2011 ident: 10.1016/j.apacoust.2019.107005_b0145 article-title: Evolutionary artificial neural network based on chemical reaction optimization publication-title: Cong Evol Comput – volume: 115 start-page: 549 issue: 3 year: 2002 ident: 10.1016/j.apacoust.2019.107005_b0180 article-title: Simple explanation of the no-free-lunch theorem and its implications publication-title: J Optim Theory Appl doi: 10.1023/A:1021251113462 – volume: 26 start-page: 94 issue: 1 year: 2012 ident: 10.1016/j.apacoust.2019.107005_b0195 article-title: Genomic monkey business estimates of nearly identical human-chimp DNA similarity re-evaluated using omitted data publication-title: J Creation – volume: 22 start-page: 1 issue: 1 year: 2018 ident: 10.1016/j.apacoust.2019.107005_b0175 article-title: Optimizing connection weights in neural networks using the whale optimization algorithm publication-title: Soft Comput doi: 10.1007/s00500-016-2442-1 – volume: 131 start-page: 1 year: 1994 ident: 10.1016/j.apacoust.2019.107005_b0210 article-title: Hunting decisions in wild chimpanzees behaviour publication-title: Behaviour doi: 10.1163/156853994X00181 – volume: 6 start-page: 133 issue: 1 year: 2019 ident: 10.1016/j.apacoust.2019.107005_b0225 article-title: A Modified grey wolf optimizer by individual best memory and penalty factor for sonar and radar dataset’s classification publication-title: Iran J Marine Technol – start-page: 1 year: 2019 ident: 10.1016/j.apacoust.2019.107005_b0040 article-title: Classification of sonar targets using an MLP neural network trained by dragonfly algorithm publication-title: Wireless Personal Syst – volume: 75 start-page: 295 year: 2014 ident: 10.1016/j.apacoust.2019.107005_b0150 article-title: Estimates of energy consumption in turkey using neural networks with the teaching-learning-based optimization algorithm publication-title: Energy doi: 10.1016/j.energy.2014.07.078 – volume: 19 start-page: 633 issue: 15 year: 2009 ident: 10.1016/j.apacoust.2019.107005_b0200 article-title: Fission-fusion populations publication-title: Curr Biol doi: 10.1016/j.cub.2009.05.034 – start-page: 1 year: 2018 ident: 10.1016/j.apacoust.2019.107005_b0060 article-title: Accurate classification of EEG signals using neural networks trained by hybrid population-physic-based algorithm publication-title: Int J Automation Comput – ident: 10.1016/j.apacoust.2019.107005_b0220 – volume: 17 start-page: 93 year: 2003 ident: 10.1016/j.apacoust.2019.107005_b0120 article-title: Differential evolution training algorithm for feed-forward neural networks publication-title: Neural Process Lett doi: 10.1023/A:1022995128597 – volume: 3 start-page: 1 issue: 1 year: 2016 ident: 10.1016/j.apacoust.2019.107005_b0230 article-title: Classification of sonar target using hybrid particle swarm and gravitational search publication-title: J Marine Technol – volume: 118 start-page: 15 year: 2017 ident: 10.1016/j.apacoust.2019.107005_b0065 article-title: Improved migration models of biogeography-based optimization for sonar data set classification using neural network publication-title: Appl Acoust doi: 10.1016/j.apacoust.2016.11.012 – start-page: 209 year: 2014 ident: 10.1016/j.apacoust.2019.107005_b0155 article-title: An improved grey wolf optimizer for training q-gaussian radial basis functional-link nets publication-title: Comput Sci Eng Conf – volume: 1 start-page: 3 issue: 1 year: 2011 ident: 10.1016/j.apacoust.2019.107005_b0235 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 – start-page: 2 year: 2005 ident: 10.1016/j.apacoust.2019.107005_b0105 article-title: Training feed-forward neural networks with ant colony optimization: an application to pattern classification – start-page: 14 year: 2014 ident: 10.1016/j.apacoust.2019.107005_b0130 article-title: Social-spider optimization-based artificial neural networks training and its applications for Parkinson’s disease identification – volume: 4617 start-page: 318 year: 2009 ident: 10.1016/j.apacoust.2019.107005_b0115 article-title: Artificial Bee Colony (ABC) optimization algorithm for training feed-forward neural networks publication-title: Lect Notes Comput Sci doi: 10.1007/978-3-540-73729-2_30 – volume: 542 start-page: 562 year: 2016 ident: 10.1016/j.apacoust.2019.107005_b0010 article-title: Airport take-off noise assessment aimed to identify responsible aircraft classes publication-title: Sci Total Environ doi: 10.1016/j.scitotenv.2015.10.037 – volume: 73 start-page: 11 issue: 73 year: 2015 ident: 10.1016/j.apacoust.2019.107005_b0015 article-title: Approximation of active sonar clutter's statistical parameters using array's effective beam-width publication-title: Iran J Marine Sci Technol – volume: 95 start-page: 4623 issue: 4 year: 2017 ident: 10.1016/j.apacoust.2019.107005_b0080 article-title: Neural network trained by biogeography-based optimizer with chaos for sonar data set classification publication-title: J Wireless Personal Commun doi: 10.1007/s11277-017-4110-x – volume: 154 start-page: 176 year: 2019 ident: 10.1016/j.apacoust.2019.107005_b0030 article-title: Improved whale trainer for sonar datasets classification using neural network publication-title: Appl Acoust doi: 10.1016/j.apacoust.2019.05.006 – volume: 44 start-page: 137 issue: 1 year: 2019 ident: 10.1016/j.apacoust.2019.107005_b0050 article-title: Training multi-layer perceptron utilizing adaptive best-mass gravitational search algorithm to classify sonar dataset publication-title: Arch Acoust doi: 10.24425/aoa.2019.126360 – start-page: 1895 year: 2002 ident: 10.1016/j.apacoust.2019.107005_b0100 article-title: Particle swarms for feedforward neural network training, learning – volume: 131 start-page: 1 issue: 1 year: 1994 ident: 10.1016/j.apacoust.2019.107005_b0190 article-title: Hunting decisions in wild chimpanzees publication-title: Behaviour doi: 10.1163/156853994X00181 – volume: 40 start-page: 5148 issue: 13 year: 2013 ident: 10.1016/j.apacoust.2019.107005_b0005 article-title: Aircraft class identification based on take-off noise signal segmentation in time publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2013.03.017 – volume: 3 start-page: 99 issue: 2 year: 2017 ident: 10.1016/j.apacoust.2019.107005_b0215 article-title: Marine propellers design using particle swarm optimization with independent groups to improve efficiency and reduce cavitation publication-title: J Marine Technol – volume: 17 issue: 7 year: 2017 ident: 10.1016/j.apacoust.2019.107005_b0075 article-title: Sonar false alarm rate suppression using classification methods based on interior search algorithm publication-title: IJCSNS Int J Comput Sci Network Security – volume: 98 start-page: 96 issue: 1 year: 1996 ident: 10.1016/j.apacoust.2019.107005_b0185 article-title: The hunting ecology of wild chimpanzees: implications for the evolutionary ecology of pliocene hominids publication-title: Am Anthropol doi: 10.1525/aa.1996.98.1.02a00090 – volume: 119 start-page: 17 year: 2017 ident: 10.1016/j.apacoust.2019.107005_b0035 article-title: Marine mammal sound classification based on a parallel recognition model and octave analysis publication-title: Appl Acoust doi: 10.1016/j.apacoust.2016.11.016 – volume: 26 start-page: 1 issue: 11 year: 2017 ident: 10.1016/j.apacoust.2019.107005_b0055 article-title: Training a feed-forward neural network using particle swarm optimizer with autonomous groups for sonar target classification publication-title: J Circuits, Syst, Comput doi: 10.1142/S0218126617501857 – volume: 1 start-page: 80 issue: 6 year: 1945 ident: 10.1016/j.apacoust.2019.107005_b0240 article-title: Individual comparisons by ranking methods publication-title: Biometrics Bull doi: 10.2307/3001968 – volume: 14 start-page: 347 year: 1990 ident: 10.1016/j.apacoust.2019.107005_b0095 article-title: Genetic algorithms and neural networks: optimizing connections and connectivity publication-title: Parallel Comput doi: 10.1016/0167-8191(90)90086-O – volume: 16 start-page: 235 year: 2007 ident: 10.1016/j.apacoust.2019.107005_b0110 article-title: An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training publication-title: Neural Comput Appl doi: 10.1007/s00521-007-0084-z – volume: 43 start-page: 150 issue: 1 year: 2015 ident: 10.1016/j.apacoust.2019.107005_b0160 article-title: How effective is the grey wolf optimizer in training multi-layer perceptrons publication-title: Appl Intelligence doi: 10.1007/s10489-014-0645-7 |
| SSID | ssj0000255 |
| Score | 2.5498602 |
| Snippet | Due to the variability of the radiated signal of the underwater targets, the classification of the underwater acoustical dataset is a challenging problem in... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 107005 |
| SubjectTerms | Chimp Optimization Algorithm Classification Multi-Layer Perceptron Neural Networks Underwater acoustical dataset |
| Title | Classification of underwater acoustical dataset using neural network trained by Chimp Optimization Algorithm |
| URI | https://dx.doi.org/10.1016/j.apacoust.2019.107005 |
| Volume | 157 |
| WOSCitedRecordID | wos000495519000015&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-910X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000255 issn: 0003-682X databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELfKBhI8IBggxpf8wFuV4jhLbD9W0xBfGggG6ltkO_baqU2rNivjv-ds52swaSDEi5We5Dry_XJ3Pt8HQi-l0pKpWERCKBEdwI-IHxgYnG5gBeXSB2N--8COj_lkIj4NBu-bXJjtnJUlv7gQq__KaqABs13q7F-wu_1TIMAzMB1GYDuMf8R43-bSBQC1xqDLE1t_l64cIsg_377LXc3ICjRYNTz33gJX1xKIZYgKD50jgnF6OJ0tVsOPIFoWdc7mcDw_Xa5n1XTRN20be7ZZorslmrq4e-95HbX8XW7kdhZon0d91wMlv7ge2pyYLgApyNgkyrhvkg4aJohVziiIVTK5JHdDZerfZHhwJ5yNwFjwL-zi7wSQGSFpp7XaWMIvvqYOrAemKElB-d5Au5SlAkTc7vjt0eRdp5hpmjYNFN2EXsL41atdbav07I-Te-hufXDA48Dw-2hgyj10p1dOcg_d8uG8evMAzS-DAC8t7kCAOxDgGgTYgwAHEOAaBLgGAVY_sAcB7oMAtyB4iL6-Pjo5fBPVfTUincS0irhkjBPLFbVKpVZbwhSnhc1YEhtpDSdaFEZzm8UmZokpjImLTNNMm8xaK5JHaKdcluYxwhYOwEQXqjBwMDdcwRfv7mGlVCa1JJX7KG12MNd10Xn38vO8iS48y5udz93O52Hn99Grdt4qlF25doZoGJTXxmMwCnPA1TVzn_zD3KfodvdpPEM71frcPEc39baabdYvagj-BMqMnf8 |
| linkProvider | Elsevier |
| 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=Classification+of+underwater+acoustical+dataset+using+neural+network+trained+by+Chimp+Optimization+Algorithm&rft.jtitle=Applied+acoustics&rft.au=Khishe%2C+M.&rft.au=Mosavi%2C+M.R.&rft.date=2020-01-01&rft.pub=Elsevier+Ltd&rft.issn=0003-682X&rft.eissn=1872-910X&rft.volume=157&rft_id=info:doi/10.1016%2Fj.apacoust.2019.107005&rft.externalDocID=S0003682X19305067 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0003-682X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0003-682X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0003-682X&client=summon |