{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:31:59Z","timestamp":1775068319648,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00428-0","type":"journal-article","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T12:05:37Z","timestamp":1753963537000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Multi-objective: hybrid particle swarm optimization with firefly algorithm for feature selection with Leaky ReLU"],"prefix":"10.1007","volume":"5","author":[{"given":"Ashish Kumar","family":"Singh","sequence":"first","affiliation":[]},{"given":"Anoj","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,31]]},"reference":[{"key":"428_CR1","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R. Particle swarm optimization. In: Proc IEEE Int Conf Neural Netw. 1995. vol. 4, pp. 1942\u20138.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"428_CR2","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.ins.2019.05.048","volume":"498","author":"MR Chen","year":"2019","unstructured":"Chen MR, Zeng GQ, Lu KD. A many-objective population extremal optimization algorithm with an adaptive hybrid mutation operation. Inf Sci. 2019;498:62\u201390.","journal-title":"Inf Sci"},{"issue":"1\u20133","key":"428_CR3","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1016\/j.neucom.2009.07.014","volume":"73","author":"CL Huang","year":"2009","unstructured":"Huang CL. ACO-based hybrid classification system with feature subset selection and model parameters optimization. Neurocomputing. 2009;73(1\u20133):438\u201348.","journal-title":"Neurocomputing"},{"key":"428_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2024.102740","volume":"84","author":"C Yuan","year":"2024","unstructured":"Yuan C, Zhao D, Heidari AA, et al. Artemisinin optimization based on malaria therapy: algorithm and applications to medical image segmentation. Displays. 2024;84: 102740.","journal-title":"Displays"},{"issue":"15","key":"428_CR5","doi-asserted-by":"publisher","first-page":"3185","DOI":"10.1080\/00207721.2024.2367079","volume":"55","author":"J Lian","year":"2024","unstructured":"Lian J, Zhu T, Ma L, et al. The educational competition optimizer. Int J Syst Sci. 2024;55(15):3185\u2013222.","journal-title":"Int J Syst Sci"},{"key":"428_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128289","volume":"607","author":"A Qi","year":"2024","unstructured":"Qi A, Zhao D, Heidari AA, et al. FATA: an efficient optimization method based on geophysics. Neurocomputing. 2024;607: 128289.","journal-title":"Neurocomputing"},{"key":"428_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128427","volume":"607","author":"C Yuan","year":"2024","unstructured":"Yuan C, Zhao D, Heidari AA, et al. Polar lights optimizer: algorithm and applications in image segmentation and feature selection. Neurocomputing. 2024;607: 128427.","journal-title":"Neurocomputing"},{"key":"428_CR8","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.neucom.2023.02.010","volume":"532","author":"H Su","year":"2023","unstructured":"Su H, Zhao D, Heidari AA, et al. RIME: a physics-based optimization. Neurocomputing. 2023;532:183\u2013214.","journal-title":"Neurocomputing"},{"key":"428_CR9","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1016\/j.neucom.2015.07.057","volume":"171","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Gong DW, Zhang WQ. Feature selection of unreliable data using an improved multi-objective PSO algorithm. Neurocomputing. 2016;171:1281\u201390.","journal-title":"Neurocomputing"},{"issue":"2","key":"428_CR10","first-page":"26","volume":"3","author":"AK Singh","year":"2015","unstructured":"Singh AK, Shanker U. A hybrid approach for replica placement-replacement (HARP-R ALGO) algorithm in data-grid. I-Manager\u2019s J Comput Sci. 2015;3(2):26\u201335.","journal-title":"I-Manager\u2019s J Comput Sci"},{"key":"428_CR11","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/806954","volume":"2015","author":"I Ahmad","year":"2015","unstructured":"Ahmad I. Feature selection using particle swarm optimization in intrusion detection. Int J Distrib Sens Netw. 2015;2015: 806954.","journal-title":"Int J Distrib Sens Netw"},{"issue":"1","key":"428_CR12","first-page":"1598","volume":"29","author":"M Ghosh","year":"2020","unstructured":"Ghosh M, Guha R, Alam I, et al. Binary Genetic Swarm Optimization: a combination of GA and PSO for feature selection. J Intell Syst. 2020;29(1):1598\u2013610.","journal-title":"J Intell Syst"},{"key":"428_CR13","doi-asserted-by":"crossref","unstructured":"Tran B, Xue B, Zhang M. Improved PSO for feature selection on high-dimensional datasets. In: Simulated evolution and learning. Lecture notes in computer science. 2014. vol. 8886, pp. 541\u201352.","DOI":"10.1007\/978-3-319-13563-2_43"},{"issue":"Pt D","key":"428_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107168","volume":"126","author":"G Yuan","year":"2023","unstructured":"Yuan G, Lu L, Zhou X. Feature selection using a sinusoidal sequence combined with mutual information. Eng Appl Artif Intell. 2023;126(Pt D): 107168.","journal-title":"Eng Appl Artif Intell"},{"key":"428_CR15","doi-asserted-by":"crossref","unstructured":"Aboud A, Fdhila R, Alimi AM. MOPSO for dynamic feature selection problem based on big data fusion. In: Proc IEEE Int Conf Syst Man Cybern (SMC). 2016. pp. 2677\u201382.","DOI":"10.1109\/SMC.2016.7844846"},{"key":"428_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13638-019-1477-2","volume":"2019","author":"X Jin","year":"2019","unstructured":"Jin X, He T, Lin Y. Multi-objective model selection algorithm for online sequential ultimate learning machine. EURASIP J Wirel Commun Netw. 2019;2019:1\u20137.","journal-title":"EURASIP J Wirel Commun Netw"},{"key":"428_CR17","doi-asserted-by":"crossref","unstructured":"Jia Y, Wang J, Xiao Q. Multiple groups of gradient particle swarm optimization and its application in optimal operation of reservoir. In: Proc Int Conf Nat Comput (ICNC). 2014. pp. 622\u20136.","DOI":"10.1109\/ICNC.2014.6975907"},{"key":"428_CR18","doi-asserted-by":"crossref","unstructured":"Tapia MGC, Coello CAC. Applications of multi-objective evolutionary algorithms in economics and finance: a survey. In: Proc IEEE Congr Evol Comput (CEC). 2007. pp. 532\u20139.","DOI":"10.1109\/CEC.2007.4424516"},{"key":"428_CR19","first-page":"6129","volume":"53","author":"M Abdel-Basset","year":"2020","unstructured":"Abdel-Basset M, Ding W, El-Shahat D. A hybrid Harris Hawks optimization algorithm with simulated annealing for feature selection. Artif Intell Rev. 2020;53:6129\u201378.","journal-title":"Artif Intell Rev"},{"issue":"6","key":"428_CR20","doi-asserted-by":"publisher","first-page":"1733","DOI":"10.1109\/TCYB.2017.2714145","volume":"48","author":"B Tran","year":"2018","unstructured":"Tran B, Xue B, Zhang M. A new representation in PSO for discretization-based feature selection. IEEE Trans Cybern. 2018;48(6):1733\u201346.","journal-title":"IEEE Trans Cybern"},{"key":"428_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105885","volume":"148","author":"B Shi","year":"2022","unstructured":"Shi B, Chen J, Chen H, et al. Prediction of recurrent spontaneous abortion using evolutionary machine learning with joint self-adaptive SIME mould algorithm. Comput Biol Med. 2022;148: 105885.","journal-title":"Comput Biol Med"},{"key":"428_CR22","volume":"222","author":"M Sewwandi","year":"2023","unstructured":"Sewwandi M, Li Y, Zhang J. Granule-specific feature selection for continuous data classification using neighbourhood rough sets. Expert Syst Appl. 2023;222: 121765.","journal-title":"Expert Syst Appl"},{"key":"428_CR23","doi-asserted-by":"crossref","unstructured":"Fix E, Hodges JL. Discriminatory analysis\u2013Nonparametric discrimination: consistency properties. USAF Sch Aviat Med Tech Rep. 1951. vol. 21-49-004, p. 4.","DOI":"10.1037\/e471672008-001"},{"issue":"6","key":"428_CR24","doi-asserted-by":"publisher","first-page":"4370","DOI":"10.1016\/j.ygeno.2020.07.027","volume":"112","author":"M Rostami","year":"2020","unstructured":"Rostami M, Forouzandeh S, Berahmand K, Soltani M. Integration of multi-objective PSO-based feature selection and node centrality for medical datasets. Genomics. 2020;112(6):4370\u201384.","journal-title":"Genomics"},{"key":"428_CR25","doi-asserted-by":"crossref","unstructured":"Clerc M. Discrete particle swarm optimization, illustrated by the Traveling Salesman Problem. In: New Optimization Techniques in Engineering. Stud Fuzz Soft Comput. 2004. vol. 141, pp. 219\u201339.","DOI":"10.1007\/978-3-540-39930-8_8"},{"key":"428_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119062","volume":"641","author":"AD Li","year":"2023","unstructured":"Li AD, Xue B, Zhang M. Multi-objective particle swarm optimization for key quality feature selection in complex manufacturing processes. Inf Sci. 2023;641: 119062.","journal-title":"Inf Sci"},{"key":"428_CR27","doi-asserted-by":"crossref","unstructured":"McNabb AW, Monson CK, Seppi KD. Parallel PSO using MapReduce. In: Proc IEEE Congr Evol Comput (CEC). 2007. pp. 7\u201314.","DOI":"10.1109\/CEC.2007.4424448"},{"key":"428_CR28","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart RC. A discrete binary version of the particle swarm algorithm. In: Proc IEEE Int Conf Syst Man Cybern. 1997. pp. 4104\u20138.","DOI":"10.1109\/ICSMC.1997.637339"},{"key":"428_CR29","doi-asserted-by":"crossref","unstructured":"Krohling RA. Gaussian swarm: a novel particle swarm optimization algorithm. In: Proc IEEE Conf Cybern Intell Syst. 2004. vol. 1, pp. 372\u20136.","DOI":"10.1109\/ICCIS.2004.1460443"},{"issue":"Suppl 1","key":"428_CR30","first-page":"404","volume":"14","author":"AK Singh","year":"2023","unstructured":"Singh AK, Kumar A. An improved dynamic weighted particle swarm optimization (IDW-PSO) for continuous optimization problem. Int J Syst Assur Eng Manag. 2023;14(Suppl 1):404\u201318.","journal-title":"Int J Syst Assur Eng Manag"},{"key":"428_CR31","doi-asserted-by":"crossref","unstructured":"Bangyal WH, Ahmad J, Shafi I, Abbas Q. Forward only counter propagation network for balance scale weight & distance classification task. In: Proc World Congr Nat Biol Inspired Comput. 2011. pp. 342\u20137.","DOI":"10.1109\/NaBIC.2011.6089615"},{"key":"428_CR32","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1109\/TEVC.2023.3292527","volume":"28","author":"R Jiao","year":"2023","unstructured":"Jiao R, Zhang M, Xue B, et al. A survey on evolutionary multiobjective feature selection in classification: approaches, applications, and challenges. IEEE Trans Evol Comput. 2023;28:1156\u201376.","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"428_CR33","first-page":"670","volume":"9","author":"WH Bangyal","year":"2019","unstructured":"Bangyal WH, Ahmad J, Rauf HT. Optimization of neural network using improved bat algorithm for data classification. J Med Imaging Health Inform. 2019;9(4):670\u201381.","journal-title":"J Med Imaging Health Inform"},{"key":"428_CR34","unstructured":"Sigillito VG, Wing SC, Hutton LV, Baker KA. Ionosphere. UCI Mach Learn Repos. 1989."},{"key":"428_CR35","unstructured":"Cole R, Fanty M. ISOLET. UCI Mach Learn Repos. 1994."},{"key":"428_CR36","unstructured":"Aeberhard S, Forina M. Wine. UCI Mach Learn Repos. 1991."},{"key":"428_CR37","unstructured":"Forsyth R. Zoo. UCI Mach Learn Repos. 1990."},{"key":"428_CR38","unstructured":"German B. Glass Identification. UCI Mach Learn Repos. 1987."},{"key":"428_CR39","unstructured":"Guyon I. Madelon. UCI Mach Learn Repos. 2008."},{"key":"428_CR40","unstructured":"Sejnowski TJ, Gorman RP. Connectionist Bench (Sonar, Mines vs. Rocks). UCI Mach Learn Repos. 1989."},{"key":"428_CR41","unstructured":"Zwitter M, Soklic M. Primary Tumor. UCI Mach Learn Repos. 1988."},{"key":"428_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108064","volume":"172","author":"J Lian","year":"2024","unstructured":"Lian J, Hui G, Ma L, et al. Parrot optimizer: algorithm and applications to medical problems. Comput Biol Med. 2024;172: 108064.","journal-title":"Comput Biol Med"},{"issue":"1","key":"428_CR43","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1109\/TCBB.2015.2476796","volume":"14","author":"Y Zhang","year":"2015","unstructured":"Zhang Y, Gong DW, Cheng J. Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE\/ACM Trans Comput Biol Bioinform. 2015;14(1):64\u201375.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"428_CR44","doi-asserted-by":"publisher","first-page":"5291","DOI":"10.1007\/s00521-022-07950-7","volume":"35","author":"B Sowan","year":"2023","unstructured":"Sowan B, Eshtay M, Dahal K, et al. Hybrid PSO feature selection-based association classification approach for breast cancer detection. Neural Comput Appl. 2023;35:5291\u2013317.","journal-title":"Neural Comput Appl"},{"key":"428_CR45","first-page":"56","volume":"70","author":"S Rajamohana","year":"2018","unstructured":"Rajamohana S, Umamaheswari K. Hybrid approach of improved binary particle swarm optimization and shuffled frog leaping for feature selection. Comput Electr Eng. 2018;70:56\u201374.","journal-title":"Comput Electr Eng"},{"key":"428_CR46","doi-asserted-by":"crossref","unstructured":"Pervaiz S, Bangyal WH, Nisar K, Rehman NU. Population initialization of Seagull Optimization Algorithm with pseudo random numbers for continuous optimization. In: Proc Int Conf Front Inf Technol (FIT). 2021. pp. 49\u201354.","DOI":"10.1109\/FIT53504.2021.00019"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00428-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00428-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00428-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T09:36:38Z","timestamp":1757324198000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00428-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,31]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["428"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00428-0","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,31]]},"assertion":[{"value":"20 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"192"}}