{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T06:26:51Z","timestamp":1768544811075,"version":"3.49.0"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Large-scale multi-objective feature selection problems are widely existing in the fields of text classification, image processing, and biological omics. Numerous features usually mean more correlation and redundancy between features, so effective features are usually sparse. SparseEA is an evolutionary algorithm for solving Large-scale Sparse Multi-objective Optimization Problems (i.e., most decision variables of the optimal solutions are zero). It determines feature Scores by calculating the fitness of individual features, which does not reflect the correlation between features well. In this manuscript, ReliefF was used to calculate the weights of features, with unimportant features being removed first. Then combine the weights calculated by ReliefF with Scores of SparseEA to guide the evolution process. Moreover, the Scores of features remain constant throughout all runs in SparseEA. Therefore, the fitness values of excellent and poor individuals in each iteration are used to update the Scores. In addition, difference operators of Differential Evolution are introduced into SparseEA to increase the diversity of solutions and help the algorithm jump out of the local optimal solution. Comparative experiments are performed on large-scale datasets selected from scikit-feature repository. The results show that the proposed algorithm is superior to the original SparseEA and the state-of-the-art algorithms.<\/jats:p>","DOI":"10.1007\/s40747-023-01177-2","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T08:01:59Z","timestamp":1690272119000},"page":"485-507","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Enhanced SparseEA for large-scale multi-objective feature selection problems"],"prefix":"10.1007","volume":"10","author":[{"given":"Shu-Chuan","family":"Chu","sequence":"first","affiliation":[]},{"given":"Zhongjie","family":"Zhuang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3128-9025","authenticated-orcid":false,"given":"Jeng-Shyang","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Ali Wagdy","family":"Mohamed","sequence":"additional","affiliation":[]},{"given":"Chia-Cheng","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"key":"1177_CR1","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.patrec.2020.02.021","volume":"133","author":"R Rivera-L\u00f3pez","year":"2020","unstructured":"Rivera-L\u00f3pez R, Mezura-Montes E, Canul-Reich J, Cruz-Ch\u00e1vez MA (2020) A permutational-based differential evolution algorithm for feature subset selection. Pattern Recognit Lett 133:86\u201393","journal-title":"Pattern Recognit Lett"},{"issue":"3","key":"1177_CR2","first-page":"80","volume":"4","author":"X-D Wang","year":"2019","unstructured":"Wang X-D, Chen R-C, Yan F (2019) High-dimensional data clustering using k-means subspace feature selection. J Netw Intell 4(3):80\u201387","journal-title":"J Netw Intell"},{"key":"1177_CR3","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.apm.2021.04.018","volume":"98","author":"RA Ibrahim","year":"2021","unstructured":"Ibrahim RA, Abd Elaziz M, Ewees AA, El-Abd M, Lu S (2021) New feature selection paradigm based on hyper-heuristic technique. Appl Math Model 98:14\u201337","journal-title":"Appl Math Model"},{"key":"1177_CR4","first-page":"1289","volume":"3","author":"G Forman","year":"2003","unstructured":"Forman G et al (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 3:1289\u20131305","journal-title":"J Mach Learn Res"},{"issue":"3","key":"1177_CR5","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1007\/s11042-018-6083-5","volume":"78","author":"X Deng","year":"2019","unstructured":"Deng X, Li Y, Weng J, Zhang J (2019) Feature selection for text classification: a review. Multimed Tools Appl 78(3):3797\u20133816","journal-title":"Multimed Tools Appl"},{"key":"1177_CR6","doi-asserted-by":"crossref","unstructured":"Chen Y, Tao J, Wang J, Liao Z, Xiong J, Wang L (2019) The image annotation method by convolutional features from intermediate layer of deep learning based on internet of things. In: 2019 15th international conference on mobile ad-hoc and sensor networks (MSN). IEEE, pp 315\u2013320","DOI":"10.1109\/MSN48538.2019.00066"},{"issue":"1","key":"1177_CR7","first-page":"1","volume":"10","author":"Z Wang","year":"2019","unstructured":"Wang Z, Dong J, Zhen J, Zhu F (2019) Template protection based on chaotic map and DNA encoding for multimodal biometrics at feature level fusion. J Inf Hiding Multimed Signal Process 10(1):1\u201310","journal-title":"J Inf Hiding Multimed Signal Process"},{"key":"1177_CR8","unstructured":"Chaudhary V, Deshbhratar A, Kumar V, Paul D (2018) Time series based LSTM model to predict air pollutant\u2019s concentration for prominent cities in India, UDM"},{"issue":"12","key":"1177_CR9","first-page":"4531","volume":"15","author":"W Lin","year":"2021","unstructured":"Lin W, Yang C, Zhang Z, Xue X, Haga R (2021) A quantitative assessment method of network information security vulnerability detection risk based on the meta feature system of network security data. KSII Trans Internet Inf Syst (TIIS) 15(12):4531\u20134544","journal-title":"KSII Trans Internet Inf Syst (TIIS)"},{"issue":"2","key":"1177_CR10","first-page":"423","volume":"22","author":"J Jung","year":"2021","unstructured":"Jung J, Park J, Cho S-J, Han S, Park M, Cho H-H (2021) Feature engineering and evaluation for android malware detection scheme. J Internet Technol 22(2):423\u2013440","journal-title":"J Internet Technol"},{"key":"1177_CR11","doi-asserted-by":"crossref","unstructured":"Molina LC, Belanche L, Nebot \u00c0 (2002) Feature selection algorithms: a survey and experimental evaluation. In: 2002 IEEE international conference on data mining, proceedings. IEEE, pp 306\u2013313","DOI":"10.1109\/ICDM.2002.1183917"},{"key":"1177_CR12","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.ins.2020.11.056","volume":"561","author":"J-S Pan","year":"2021","unstructured":"Pan J-S, Liu N, Chu S-C, Lai T (2021) An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems. Inf Sci 561:304\u2013325","journal-title":"Inf Sci"},{"key":"1177_CR13","doi-asserted-by":"crossref","unstructured":"Fister D, Fister I, Jagri\u010d T, Brest J (2019) Wrapper-based feature selection using self-adaptive differential evolution. In: Zamuda A, Das S, Suganthan PN, Panigrahi BK (eds) Zamuda A, Das S, Suganthan PN, Panigrahi BK (eds) Swarm, evolutionary, and memetic computing and fuzzy and neural computing. Springer, pp 135\u2013154","DOI":"10.1007\/978-3-030-37838-7_13"},{"key":"1177_CR14","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.patrec.2014.10.007","volume":"52","author":"H Banka","year":"2015","unstructured":"Banka H, Dara S (2015) A hamming distance based binary particle swarm optimization (hdbpso) algorithm for high dimensional feature selection, classification and validation. Pattern Recognit Lett 52:94\u2013100","journal-title":"Pattern Recognit Lett"},{"key":"1177_CR15","unstructured":"Ram\u00edrez-Gallego S, Garc\u00eda S, Xiong N, Herrera F (2018) Belief: a distance-based redundancy-proof feature selection method for big data. arXiv preprint arXiv:1804.05774"},{"issue":"3","key":"1177_CR16","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.neulet.2009.06.052","volume":"461","author":"R Chaves","year":"2009","unstructured":"Chaves R, Ram\u00edrez J, G\u00f3rriz J, L\u00f3pez M, Salas-Gonzalez D, Alvarez I, Segovia F (2009) Svm-based computer-aided diagnosis of the Alzheimer\u2019s disease using t-test nmse feature selection with feature correlation weighting. Neurosci Lett 461(3):293\u2013297","journal-title":"Neurosci Lett"},{"key":"1177_CR17","first-page":"1","volume":"15","author":"L Sun","year":"2022","unstructured":"Sun L, Zhang J, Ding W, Xu J (2022) Mixed measure-based feature selection using the fisher score and neighborhood rough sets. Appl Intell 15:1\u201325","journal-title":"Appl Intell"},{"issue":"2","key":"1177_CR18","first-page":"18","volume":"2","author":"B Azhagusundari","year":"2013","unstructured":"Azhagusundari B, Thanamani AS et al (2013) Feature selection based on information gain. Int J Innov Technol Explor Eng (IJITEE) 2(2):18\u201321","journal-title":"Int J Innov Technol Explor Eng (IJITEE)"},{"key":"1177_CR19","unstructured":"Janecek A, Gansterer W, Demel M, Ecker G (2008) On the relationship between feature selection and classification accuracy. In: New challenges for feature selection in data mining and knowledge discovery. PMLR, pp 90\u2013105"},{"key":"1177_CR20","volume":"241","author":"SC Chu","year":"2022","unstructured":"Chu SC, Xu XW, Yang SY, Pan JS (2022) Parallel fish migration optimization with compact technology based on memory principle for wireless sensor networks. Knowl Based Syst 241:108124","journal-title":"Knowl Based Syst"},{"issue":"6","key":"1177_CR21","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.53106\/160792642021112206001","volume":"22","author":"J-S Pan","year":"2021","unstructured":"Pan J-S, Song P-C, Pan C-A, Abraham A (2021) The phasmatodea population evolution algorithm and its application in 5g heterogeneous network downlink power allocation problem. J Internet Technol 22(6):1199\u20131213","journal-title":"J Internet Technol"},{"key":"1177_CR22","doi-asserted-by":"crossref","first-page":"26766","DOI":"10.1109\/ACCESS.2021.3056407","volume":"9","author":"P Agrawal","year":"2021","unstructured":"Agrawal P, Abutarboush HF, Ganesh T, Mohamed AW (2021) Metaheuristic algorithms on feature selection: a survey of one decade of research (2009\u20132019). IEEE Access 9:26766\u201326791","journal-title":"IEEE Access"},{"key":"1177_CR23","volume":"195","author":"P Hu","year":"2020","unstructured":"Hu P, Pan J-S, Chu S-C (2020) Improved binary grey wolf optimizer and its application for feature selection. Knowl Based Syst 195:105746","journal-title":"Knowl Based Syst"},{"key":"1177_CR24","doi-asserted-by":"crossref","unstructured":"Fu G, Sun C, Tan Y, Zhang G, Jin Y (2020) A surrogate-assisted evolutionary algorithm with random feature selection for large-scale expensive problems. In: International conference on parallel problem solving from nature. Springer, pp 125\u2013139","DOI":"10.1007\/978-3-030-58112-1_9"},{"key":"1177_CR25","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.116834","volume":"200","author":"S Ahmed","year":"2022","unstructured":"Ahmed S, Sheikh KH, Mirjalili S, Sarkar R (2022) Binary simulated normal distribution optimizer for feature selection: theory and application in COVID-19 datasets. Expert Syst Appl 200:116834","journal-title":"Expert Syst Appl"},{"issue":"23","key":"1177_CR26","doi-asserted-by":"crossref","first-page":"16229","DOI":"10.1007\/s00521-021-06224-y","volume":"33","author":"J Too","year":"2021","unstructured":"Too J, Mafarja M, Mirjalili S (2021) Spatial bound whale optimization algorithm: an efficient high-dimensional feature selection approach. Neural Comput Appl 33(23):16229\u201316250","journal-title":"Neural Comput Appl"},{"key":"1177_CR27","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.knosys.2018.08.003","volume":"161","author":"M Mafarja","year":"2018","unstructured":"Mafarja M, Aljarah I, Heidari AA, Faris H, Fournier-Viger P, Li X, Mirjalili S (2018) Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowl Based Syst 161:185\u2013204","journal-title":"Knowl Based Syst"},{"key":"1177_CR28","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107224","volume":"227","author":"W BinSaeedan","year":"2021","unstructured":"BinSaeedan W, Alramlawi S (2021) Cs-bpso: hybrid feature selection based on chi-square and binary PSO algorithm for Arabic email authorship analysis. Knowl Based Syst 227:107224","journal-title":"Knowl Based Syst"},{"key":"1177_CR29","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.asoc.2017.06.029","volume":"69","author":"H Wang","year":"2018","unstructured":"Wang H, Wang W, Cui L, Sun H, Zhao J, Wang Y, Xue Y (2018) A hybrid multi-objective firefly algorithm for big data optimization. Appl Soft Comput 69:806\u2013815","journal-title":"Appl Soft Comput"},{"key":"1177_CR30","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.ins.2021.05.064","volume":"574","author":"G Li","year":"2021","unstructured":"Li G, Wang G-G, Dong J, Yeh W-C, Li K (2021) Dlea: a dynamic learning evolution algorithm for many-objective optimization. Inf Sci 574:567\u2013589","journal-title":"Inf Sci"},{"issue":"9","key":"1177_CR31","doi-asserted-by":"crossref","first-page":"5880","DOI":"10.1109\/TSMC.2019.2956288","volume":"51","author":"Y Tian","year":"2019","unstructured":"Tian Y, He C, Cheng R, Zhang X (2019) A multistage evolutionary algorithm for better diversity preservation in multiobjective optimization. IEEE Trans Syst Man Cybern Syst 51(9):5880\u20135894","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"1177_CR32","doi-asserted-by":"crossref","first-page":"7692","DOI":"10.1109\/TITS.2021.3071786","volume":"23","author":"C Wang","year":"2021","unstructured":"Wang C, Wang Z, Tian Y, Zhang X, Xiao J (2021) A dual-population based evolutionary algorithm for multi-objective location problem under uncertainty of facilities. IEEE Trans Intell Transp Syst 23:7692\u20137707","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"5","key":"1177_CR33","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1109\/TEVC.2010.2041060","volume":"14","author":"LB Said","year":"2010","unstructured":"Said LB, Bechikh S, Gh\u00e9dira K (2010) The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making. IEEE Trans Evol Comput 14(5):801\u2013818","journal-title":"IEEE Trans Evol Comput"},{"key":"1177_CR34","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/j.asoc.2018.10.027","volume":"74","author":"Z Fan","year":"2019","unstructured":"Fan Z, Fang Y, Li W, Cai X, Wei C, Goodman E (2019) Moea\/d with angle-based constrained dominance principle for constrained multi-objective optimization problems. Appl Soft Comput 74:621\u2013633","journal-title":"Appl Soft Comput"},{"key":"1177_CR35","volume":"245","author":"J-S Pan","year":"2022","unstructured":"Pan J-S, Liu N, Chu S-C (2022) A competitive mechanism based multi-objective differential evolution algorithm and its application in feature selection. Knowl Based Syst 245:108582","journal-title":"Knowl Based Syst"},{"key":"1177_CR36","doi-asserted-by":"crossref","first-page":"106247","DOI":"10.1109\/ACCESS.2020.3000040","volume":"8","author":"Q Al-Tashi","year":"2020","unstructured":"Al-Tashi Q, Abdulkadir SJ, Rais HM, Mirjalili S, Alhussian H, Ragab MG, Alqushaibi A (2020) Binary multi-objective grey wolf optimizer for feature selection in classification. IEEE Access 8:106247\u2013106263","journal-title":"IEEE Access"},{"key":"1177_CR37","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.ins.2019.08.040","volume":"507","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Gong D-W, Gao X-Z, Tian T, Sun X-Y (2020) Binary differential evolution with self-learning for multi-objective feature selection. Inf Sci 507:67\u201385","journal-title":"Inf Sci"},{"key":"1177_CR38","volume":"88","author":"X-H Wang","year":"2020","unstructured":"Wang X-H, Zhang Y, Sun X-Y, Wang Y-L, Du C-H (2020) Multi-objective feature selection based on artificial bee colony: an acceleration approach with variable sample size. Appl Soft Comput 88:106041","journal-title":"Appl Soft Comput"},{"issue":"21","key":"1177_CR39","doi-asserted-by":"crossref","first-page":"7652","DOI":"10.1016\/j.eswa.2015.06.004","volume":"42","author":"KZ Gao","year":"2015","unstructured":"Gao KZ, Suganthan PN, Chua TJ, Chong CS, Cai TX, Pan QK (2015) A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion. Expert Syst Appl 42(21):7652\u20137663","journal-title":"Expert Syst Appl"},{"key":"1177_CR40","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.ins.2020.03.032","volume":"523","author":"A-D Li","year":"2020","unstructured":"Li A-D, Xue B, Zhang M (2020) Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection. Inf Sci 523:245\u2013265","journal-title":"Inf Sci"},{"key":"1177_CR41","first-page":"1","volume":"2018","author":"F Cheng","year":"2018","unstructured":"Cheng F, Guo W, Zhang X (2018) Mofsrank: a multiobjective evolutionary algorithm for feature selection in learning to rank. Complexity 2018:1\u201314","journal-title":"Complexity"},{"issue":"9","key":"1177_CR42","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1109\/TIP.2008.2001050","volume":"17","author":"K Huang","year":"2008","unstructured":"Huang K, Aviyente S (2008) Wavelet feature selection for image classification. IEEE Trans Image Process 17(9):1709\u20131720","journal-title":"IEEE Trans Image Process"},{"issue":"8","key":"1177_CR43","doi-asserted-by":"crossref","first-page":"385","DOI":"10.6026\/97320630004385","volume":"4","author":"G Pok","year":"2010","unstructured":"Pok G, Liu J-CS, Ryu KH (2010) Effective feature selection framework for cluster analysis of microarray data. Bioinformation 4(8):385","journal-title":"Bioinformation"},{"key":"1177_CR44","doi-asserted-by":"crossref","unstructured":"Sahni G, Mewara B, Lalwani S, Kumar R (2022) CF-PPI: centroid based new feature extraction approach for protein\u2013protein interaction prediction. J Exp Theor Artif Intell 1\u201321","DOI":"10.1080\/0952813X.2022.2052189"},{"issue":"1","key":"1177_CR45","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/TEVC.2016.2600642","volume":"22","author":"X Zhang","year":"2016","unstructured":"Zhang X, Tian Y, Cheng R, Jin Y (2016) A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization. IEEE Trans Evol Comput 22(1):97\u2013112","journal-title":"IEEE Trans Evol Comput"},{"key":"1177_CR46","doi-asserted-by":"crossref","unstructured":"Miguel Antonio L, Coello Coello CA (2016) Decomposition-based approach for solving large scale multi-objective problems. In: International conference on parallel problem solving from nature. Springer, pp 525\u2013534","DOI":"10.1007\/978-3-319-45823-6_49"},{"key":"1177_CR47","doi-asserted-by":"crossref","unstructured":"Qian H, Yu Y (2017) Solving high-dimensional multi-objective optimization problems with low effective dimensions. In: Proceedings of the AAAI conference on artificial intelligence, vol 31","DOI":"10.1609\/aaai.v31i1.10664"},{"issue":"3","key":"1177_CR48","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1109\/TEVC.2018.2881153","volume":"23","author":"W Hong","year":"2018","unstructured":"Hong W, Tang K, Zhou A, Ishibuchi H, Yao X (2018) A scalable indicator-based evolutionary algorithm for large-scale multiobjective optimization. IEEE Trans Evol Comput 23(3):525\u2013537","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"1177_CR49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12293-021-00328-7","volume":"13","author":"H Li","year":"2021","unstructured":"Li H, He F, Chen Y, Pan Y (2021) Mlfs-ccde: multi-objective large-scale feature selection by cooperative coevolutionary differential evolution. Memet Comput 13(1):1\u201318","journal-title":"Memet Comput"},{"issue":"2","key":"1177_CR50","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1109\/TEVC.2019.2918140","volume":"24","author":"Y Tian","year":"2019","unstructured":"Tian Y, Zhang X, Wang C, Jin Y (2019) An evolutionary algorithm for large-scale sparse multiobjective optimization problems. IEEE Trans Evol Comput 24(2):380\u2013393","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"1177_CR51","doi-asserted-by":"crossref","first-page":"3115","DOI":"10.1109\/TCYB.2020.2979930","volume":"51","author":"Y Tian","year":"2020","unstructured":"Tian Y, Lu C, Zhang X, Tan KC, Jin Y (2020) Solving large-scale multiobjective optimization problems with sparse optimal solutions via unsupervised neural networks. IEEE Trans Cybern 51(6):3115\u20133128","journal-title":"IEEE Trans Cybern"},{"key":"1177_CR52","doi-asserted-by":"crossref","first-page":"6784","DOI":"10.1109\/TCYB.2020.3041325","volume":"52","author":"Y Tian","year":"2020","unstructured":"Tian Y, Lu C, Zhang X, Cheng F, Jin Y (2020) A pattern mining-based evolutionary algorithm for large-scale sparse multiobjective optimization problems. IEEE Trans Cybern 52:6784\u20136797","journal-title":"IEEE Trans Cybern"},{"key":"1177_CR53","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tian Y, Zhang X (2021) Improved sparseea for sparse large-scale multi-objective optimization problems. Complex Intell Syst 1\u201316","DOI":"10.1007\/s40747-021-00553-0"},{"issue":"1","key":"1177_CR54","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1109\/TII.2012.2198658","volume":"9","author":"SM Elsayed","year":"2012","unstructured":"Elsayed SM, Sarker RA, Essam DL (2012) An improved self-adaptive differential evolution algorithm for optimization problems. IEEE Trans Ind Inform 9(1):89\u201399","journal-title":"IEEE Trans Ind Inform"},{"key":"1177_CR55","doi-asserted-by":"crossref","DOI":"10.1016\/j.swevo.2018.10.013","volume":"50","author":"A Viktorin","year":"2019","unstructured":"Viktorin A, Senkerik R, Pluhacek M, Kadavy T, Zamuda A (2019) Distance based parameter adaptation for success-history based differential evolution. Swarm Evol Comput 50:100462","journal-title":"Swarm Evol Comput"},{"key":"1177_CR56","doi-asserted-by":"crossref","unstructured":"Brest J, Zamuda A, Boskovic B, Maucec MS, Zumer V (2009) Dynamic optimization using self-adaptive differential evolution. In: IEEE congress on evolutionary computation. IEEE, pp 415\u2013422","DOI":"10.1109\/CEC.2009.4982976"},{"key":"1177_CR57","doi-asserted-by":"crossref","unstructured":"Hou GP, Ma X (2010) A novel binary differential evolution for discrete optimization. In: Key engineering materials, vol 439. Trans Tech Publ, pp 1493\u20131498","DOI":"10.4028\/www.scientific.net\/KEM.439-440.1493"},{"key":"1177_CR58","volume":"69","author":"Y He","year":"2022","unstructured":"He Y, Zhang F, Mirjalili S, Zhang T (2022) Novel binary differential evolution algorithm based on taper-shaped transfer functions for binary optimization problems. Swarm Evol Comput 69:101022","journal-title":"Swarm Evol Comput"},{"key":"1177_CR59","doi-asserted-by":"crossref","unstructured":"Deng C, Zhao B, Yang Y, Peng H, Wei Q (2011) Novel binary encoding differential evolution algorithm. In: International conference in swarm intelligence. Springer, pp 416\u2013423","DOI":"10.1007\/978-3-642-21515-5_49"},{"key":"1177_CR60","doi-asserted-by":"crossref","unstructured":"Hota AR, Pat A (2010) An adaptive quantum-inspired differential evolution algorithm for 0\u20131 knapsack problem. In: Second world congress on nature and biologically inspired computing (NaBIC). IEEE, pp 703\u2013708","DOI":"10.1109\/NABIC.2010.5716320"},{"key":"1177_CR61","doi-asserted-by":"crossref","unstructured":"Pampara G, Engelbrecht AP, Franken N (2006) Binary differential evolution. In: IEEE international conference on evolutionary computation. IEEE, pp 1873\u20131879","DOI":"10.1109\/CEC.2007.4424711"},{"key":"1177_CR62","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.ress.2012.11.002","volume":"111","author":"Y-F Li","year":"2013","unstructured":"Li Y-F, Sansavini G, Zio E (2013) Non-dominated sorting binary differential evolution for the multi-objective optimization of cascading failures protection in complex networks. Reliab Eng Syst Saf 111:195\u2013205","journal-title":"Reliab Eng Syst Saf"},{"key":"1177_CR63","doi-asserted-by":"crossref","unstructured":"Bidgoli AA, Rahnamayan S, Ebrahimpour-Komleh H (2019) Opposition-based multi-objective binary differential evolution for multi-label feature selection. In: International conference on evolutionary multi-criterion optimization. Springer, pp 553\u2013564","DOI":"10.1007\/978-3-030-12598-1_44"},{"key":"1177_CR64","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.ins.2016.05.037","volume":"367","author":"A Banitalebi","year":"2016","unstructured":"Banitalebi A, Abd Aziz MI, Aziz ZA (2016) A self-adaptive binary differential evolution algorithm for large scale binary optimization problems. Inf Sci 367:487\u2013511","journal-title":"Inf Sci"},{"issue":"4","key":"1177_CR65","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/MCI.2017.2742868","volume":"12","author":"Y Tian","year":"2017","unstructured":"Tian Y, Cheng R, Zhang X, Jin Y (2017) Platemo: a matlab platform for evolutionary multi-objective optimization [educational forum]. IEEE Comput Intell Mag 12(4):73\u201387","journal-title":"IEEE Comput Intell Mag"},{"key":"1177_CR66","first-page":"27","volume":"20","author":"HE Lin","year":"1999","unstructured":"Lin HE, Wang K, Guo-Bin LI, Jin H (1999) The analysis and research of genetic algorithms\u2019 population diversity. J Harbin Eng Univ 20:27\u201333","journal-title":"J Harbin Eng Univ"},{"issue":"4","key":"1177_CR67","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1109\/TEVC.2017.2749619","volume":"22","author":"Y Tian","year":"2017","unstructured":"Tian Y, Cheng R, Zhang X, Cheng F, Jin Y (2017) An indicator-based multiobjective evolutionary algorithm with reference point adaptation for better versatility. IEEE Trans Evol Comput 22(4):609\u2013622","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"1177_CR68","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1109\/TEVC.2020.3016049","volume":"25","author":"H Xu","year":"2020","unstructured":"Xu H, Xue B, Zhang M (2020) A duplication analysis-based evolutionary algorithm for biobjective feature selection. IEEE Trans Evol Comput 25(2):205\u2013218","journal-title":"IEEE Trans Evol Comput"},{"issue":"3","key":"1177_CR69","first-page":"439","volume":"24","author":"Y Liu","year":"2019","unstructured":"Liu Y, Ishibuchi H, Masuyama N, Nojima Y (2019) Adapting reference vectors and scalarizing functions by growing neural gas to handle irregular pareto fronts. IEEE Trans Evol Comput 24(3):439\u2013453","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"1177_CR70","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-II. IEEE Trans Evol Comput 6(2):182\u2013197","journal-title":"IEEE Trans Evol Comput"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01177-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-023-01177-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-023-01177-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T22:19:23Z","timestamp":1707603563000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-023-01177-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,25]]},"references-count":70,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["1177"],"URL":"https:\/\/doi.org\/10.1007\/s40747-023-01177-2","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,25]]},"assertion":[{"value":"4 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}