{"id":"https://openalex.org/W4226186048","doi":"https://doi.org/10.1109/access.2022.3164669","title":"Multi-Layer Perceptron Training Optimization Using Nature Inspired Computing","display_name":"Multi-Layer Perceptron Training Optimization Using Nature Inspired Computing","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4226186048","doi":"https://doi.org/10.1109/access.2022.3164669"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3164669","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3164669","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09749093.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09749093.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103094335","display_name":"Ali Al Bataineh","orcid":"https://orcid.org/0000-0003-4576-2781"},"institutions":[{"id":"https://openalex.org/I169768744","display_name":"Norwich University","ror":"https://ror.org/04we7d902","country_code":"US","type":"education","lineage":["https://openalex.org/I169768744"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Al Bataineh","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Norwich University, Northfield, VT, USA"],"raw_orcid":"https://orcid.org/0000-0003-4576-2781","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Norwich University, Northfield, VT, USA","institution_ids":["https://openalex.org/I169768744"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067033094","display_name":"Devinder Kaur","orcid":"https://orcid.org/0000-0003-0567-8585"},"institutions":[{"id":"https://openalex.org/I90871651","display_name":"University of Toledo","ror":"https://ror.org/01pbdzh19","country_code":"US","type":"education","lineage":["https://openalex.org/I90871651"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Devinder Kaur","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, The University of Toledo, Toledo, OH, USA"],"raw_orcid":"https://orcid.org/0000-0003-0567-8585","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, The University of Toledo, Toledo, OH, USA","institution_ids":["https://openalex.org/I90871651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022738525","display_name":"Seyed Mohammad Jafar Jalali","orcid":"https://orcid.org/0000-0003-3565-2001"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Seyed Mohammad J. Jalali","raw_affiliation_strings":["Institute for Intelligent Systems Research and Innovation, Deakin University, Burwood, VIC, Australia"],"raw_orcid":"https://orcid.org/0000-0003-3565-2001","affiliations":[{"raw_affiliation_string":"Institute for Intelligent Systems Research and Innovation, Deakin University, Burwood, VIC, Australia","institution_ids":["https://openalex.org/I149704539"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":9.4474,"has_fulltext":true,"cited_by_count":80,"citation_normalized_percentile":{"value":0.98350552,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"10","issue":null,"first_page":"36963","last_page":"36977"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9779000282287598,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9751999974250793,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.8519668579101562},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6801261305809021},{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.6517112255096436},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.649633526802063},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6261728405952454},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5653067231178284},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.527834951877594},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.4554261267185211},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.44058042764663696},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4359922707080841},{"id":"https://openalex.org/keywords/rprop","display_name":"Rprop","score":0.4156447649002075},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.359472393989563},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3432929515838623},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.29033535718917847},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19544225931167603},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.11616411805152893},{"id":"https://openalex.org/keywords/types-of-artificial-neural-networks","display_name":"Types of artificial neural networks","score":0.07774877548217773}],"concepts":[{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.8519668579101562},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6801261305809021},{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.6517112255096436},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.649633526802063},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6261728405952454},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5653067231178284},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.527834951877594},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.4554261267185211},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.44058042764663696},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4359922707080841},{"id":"https://openalex.org/C98359873","wikidata":"https://www.wikidata.org/wiki/Q1320470","display_name":"Rprop","level":5,"score":0.4156447649002075},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.359472393989563},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3432929515838623},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29033535718917847},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19544225931167603},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.11616411805152893},{"id":"https://openalex.org/C177973122","wikidata":"https://www.wikidata.org/wiki/Q7860946","display_name":"Types of artificial neural networks","level":4,"score":0.07774877548217773}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3164669","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3164669","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09749093.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:f86e0dedab704d23989fe3216785fff4","is_oa":false,"landing_page_url":"https://doaj.org/article/f86e0dedab704d23989fe3216785fff4","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 36963-36977 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3164669","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3164669","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/9668973/09749093.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.4099999964237213}],"awards":[],"funders":[{"id":"https://openalex.org/F4320313633","display_name":"University of Toledo","ror":"https://ror.org/01pbdzh19"},{"id":"https://openalex.org/F4320313821","display_name":"Norwich University","ror":"https://ror.org/04we7d902"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4226186048.pdf","grobid_xml":"https://content.openalex.org/works/W4226186048.grobid-xml"},"referenced_works_count":89,"referenced_works":["https://openalex.org/W11219134","https://openalex.org/W21080000","https://openalex.org/W170613937","https://openalex.org/W192749064","https://openalex.org/W759587468","https://openalex.org/W883434633","https://openalex.org/W1481731616","https://openalex.org/W1490180010","https://openalex.org/W1498436455","https://openalex.org/W1510336429","https://openalex.org/W1529965533","https://openalex.org/W1543584211","https://openalex.org/W1543659671","https://openalex.org/W1546636571","https://openalex.org/W1570415071","https://openalex.org/W1595580983","https://openalex.org/W1606402766","https://openalex.org/W1935515061","https://openalex.org/W1947097685","https://openalex.org/W1969557815","https://openalex.org/W1971259134","https://openalex.org/W1976990135","https://openalex.org/W1978831934","https://openalex.org/W1984556360","https://openalex.org/W1989082727","https://openalex.org/W1999380910","https://openalex.org/W2001619934","https://openalex.org/W2002302337","https://openalex.org/W2005972692","https://openalex.org/W2027987815","https://openalex.org/W2031014442","https://openalex.org/W2031659228","https://openalex.org/W2032658116","https://openalex.org/W2033731173","https://openalex.org/W2044735270","https://openalex.org/W2050546928","https://openalex.org/W2070448096","https://openalex.org/W2075912154","https://openalex.org/W2097571405","https://openalex.org/W2100356383","https://openalex.org/W2119120756","https://openalex.org/W2136361056","https://openalex.org/W2152195021","https://openalex.org/W2153060984","https://openalex.org/W2156552085","https://openalex.org/W2159677278","https://openalex.org/W2161934507","https://openalex.org/W2162506329","https://openalex.org/W2164934768","https://openalex.org/W2165301864","https://openalex.org/W2167326325","https://openalex.org/W2290402024","https://openalex.org/W2291002789","https://openalex.org/W2344109343","https://openalex.org/W2541011702","https://openalex.org/W2553852618","https://openalex.org/W2790029520","https://openalex.org/W2904345049","https://openalex.org/W2904364100","https://openalex.org/W2919979744","https://openalex.org/W2947963280","https://openalex.org/W2949867260","https://openalex.org/W2953911493","https://openalex.org/W2961703895","https://openalex.org/W2968058863","https://openalex.org/W2982498405","https://openalex.org/W2989608526","https://openalex.org/W2995882507","https://openalex.org/W2996404795","https://openalex.org/W3003170190","https://openalex.org/W3019802685","https://openalex.org/W3023649828","https://openalex.org/W3033509244","https://openalex.org/W3045578620","https://openalex.org/W3100933494","https://openalex.org/W3113180780","https://openalex.org/W3136399649","https://openalex.org/W3169509541","https://openalex.org/W4200459870","https://openalex.org/W4211129255","https://openalex.org/W4211183651","https://openalex.org/W4230022635","https://openalex.org/W4232872328","https://openalex.org/W4236316114","https://openalex.org/W4242223460","https://openalex.org/W6601814988","https://openalex.org/W6622126485","https://openalex.org/W6634100702","https://openalex.org/W6683250475"],"related_works":["https://openalex.org/W2388181322","https://openalex.org/W4230330636","https://openalex.org/W1499839691","https://openalex.org/W4237659430","https://openalex.org/W2134286587","https://openalex.org/W2186941647","https://openalex.org/W1994061829","https://openalex.org/W1973946912","https://openalex.org/W2035224218","https://openalex.org/W4401177694"],"abstract_inverted_index":{"Although":[0],"the":[1,33,39,69,81,92,103,124,134,176,185,189,252],"multi-layer":[2],"perceptron":[3],"(MLP)":[4],"neural":[5],"networks":[6,277],"provide":[7],"a":[8,19,90,99,115,143,271],"lot":[9],"of":[10,22,32,85,97,136,146,188,193],"flexibility":[11],"and":[12,16,24,87,117,167,179,226,245,265],"have":[13,29],"proven":[14],"useful":[15],"reliable":[17],"in":[18,254,282],"wide":[20],"range":[21],"classification":[23,186,235],"regression":[25],"problems,":[26],"they":[27],"still":[28],"limitations.":[30],"One":[31],"most":[34,47],"common":[35],"is":[36,52,64,76,94,172,197,231],"associated":[37],"with":[38,56,199],"optimization":[40,110,209,213,217,220],"algorithm":[41,205,224],"used":[42,49,131,173],"to":[43,79,102,120,132,152,174,274],"train":[44],"them.":[45],"The":[46,170,191,229],"commonly":[48],"training":[50,137,149,202,263,275],"method":[51],"stochastic":[53],"gradient":[54],"descent":[55],"backpropagation":[57,59,75,227],"(or":[58],"for":[60,148],"short)":[61],"because":[62],"it":[63,267],"mathematically":[65],"tractable":[66],"(given":[67],"that":[68,112,181],"activation":[70],"functions":[71],"are":[72,109],"differentiable).":[73],"However,":[74],"not":[77],"guaranteed":[78],"find":[80,121,175],"globally":[82],"optimal":[83,177],"set":[84],"weights":[86,178],"biases.":[88],"As":[89],"result,":[91],"MLP":[93,138,150,255,276],"often":[95],"incapable":[96],"obtaining":[98],"desirable":[100],"solution":[101],"problem.":[104],"Clonal":[105],"selection":[106],"algorithms":[107],"(CSA)":[108],"procedures":[111],"effectively":[113],"explore":[114],"complex":[116],"large":[118],"space":[119],"values":[122],"near":[123],"global":[125],"optimum.":[126],"Consequently,":[127],"CSA":[128,147,171,260],"can":[129,268],"be":[130,269],"solve":[133,153],"problem":[135],"networks.":[139],"This":[140],"paper":[141],"presents":[142],"novel":[144],"implementation":[145],"architectures":[151],"real-world":[154,280],"problems":[155],"such":[156],"as":[157],"breast":[158],"cancer":[159],"diagnosis,":[160],"active":[161],"sonar":[162],"target":[163],"classification,":[164,166],"wheat":[165],"flower":[168,222],"classification.":[169],"biases":[180],"will":[182],"significantly":[183],"increase":[184],"accuracy":[187],"MLP.":[190],"performance":[192,256],"our":[194],"proposed":[195],"approach":[196,273],"compared":[198],"other":[200,262],"popular":[201],"methods:":[203],"genetic":[204],"(GA),":[206],"ant":[207],"colony":[208],"(ACO),":[210],"particle":[211],"swarm":[212],"(PSO),":[214],"Harris":[215],"hawks":[216],"(HHO),":[218],"moth-flame":[219],"(MFO),":[221],"pollination":[223],"(FPA),":[225],"(BP).":[228],"comparison":[230],"benchmarked":[232],"using":[233,259],"five":[234],"datasets:":[236],"Iris":[237],"Flower,":[238],"Sonar,":[239],"Wheat":[240],"Seeds,":[241],"Breast":[242],"Cancer":[243],"Wisconsin,":[244],"Haberman\u2019s":[246],"Survival.":[247],"Comparative":[248],"study":[249],"results":[250],"illustrate":[251],"improvements":[253],"gained":[257],"by":[258],"over":[261],"methods,":[264],"hence":[266],"considered":[270],"competitive":[272],"when":[278],"solving":[279],"applications":[281],"various":[283],"disciplines.":[284]},"counts_by_year":[{"year":2026,"cited_by_count":8},{"year":2025,"cited_by_count":34},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":8}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
