{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T23:46:16Z","timestamp":1740181576260,"version":"3.37.3"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-02716-5","type":"journal-article","created":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T13:01:37Z","timestamp":1711717297000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modified Leader-Advocate-Believer Algorithm with Clustering-Based Search Space Reduction Method for Solving Engineering Design Problems"],"prefix":"10.1007","volume":"5","author":[{"given":"Ruturaj","family":"Reddy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Utkarsh","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2983-5004","authenticated-orcid":false,"given":"Ishaan R.","family":"Kale","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Apoorva","family":"Shastri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anand J.","family":"Kulkarni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"key":"2716_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2022.115652","volume":"403","author":"M Abdel-Basset","year":"2023","unstructured":"Abdel-Basset M, El-Shahat D, Jameel M, Abouhawwash M. Young\u2019s double-slit experiment optimizer: a novel metaheuristic optimization algorithm for global and constraint optimization problems. Comput Methods Appl Mech Eng. 2023;403: 115652.","journal-title":"Comput Methods Appl Mech Eng"},{"key":"2716_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2021.107408","volume":"158","author":"B Abdollahzadeh","year":"2021","unstructured":"Abdollahzadeh B, Gharehchopogh FS, Mirjalili S. African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng. 2021;158: 107408.","journal-title":"Comput Ind Eng"},{"key":"2716_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116158","volume":"191","author":"L Abualigah","year":"2022","unstructured":"Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH. Reptile Search Algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst Appl. 2022;191: 116158.","journal-title":"Expert Syst Appl"},{"key":"2716_CR4","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1007\/s10462-017-9587-x","volume":"52","author":"B Alatas","year":"2019","unstructured":"Alatas B. Sports inspired computational intelligence algorithms for global optimization. Artif Intell Rev. 2019;52:1579\u2013627. https:\/\/doi.org\/10.1007\/s10462-017-9587-x.","journal-title":"Artif Intell Rev"},{"key":"2716_CR5","doi-asserted-by":"publisher","first-page":"13170","DOI":"10.1016\/j.eswa.2011.04.126","volume":"38","author":"B Alatas","year":"2011","unstructured":"Alatas B. Artificial Chemical Reaction Optimization Algorithm for global optimization. Expert Syst Appl. 2011;38:13170\u201380. https:\/\/doi.org\/10.1016\/j.eswa.2011.04.126.","journal-title":"Expert Syst Appl"},{"issue":"2","key":"2716_CR6","doi-asserted-by":"publisher","first-page":"2755","DOI":"10.1109\/LRA.2022.3143301","volume":"7","author":"H Almubarak","year":"2022","unstructured":"Almubarak H, Stachowicz K, Sadegh N, Theodorou EA. Safety embedded differential dynamic programming using discrete barrier states. IEEE Robot Autom Lett. 2022;7(2):2755\u201362.","journal-title":"IEEE Robot Autom Lett"},{"key":"2716_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2020.100821","volume":"61","author":"S Aras","year":"2021","unstructured":"Aras S, Gedikli E, Kahraman HT. A novel stochastic fractal search algorithm with fitness-distance balance for global numerical optimization. Swarm Evol Comput. 2021;61: 100821.","journal-title":"Swarm Evol Comput"},{"key":"2716_CR8","doi-asserted-by":"publisher","unstructured":"Ashrafi SM and Dariane AB. A novel and effective algorithm for numerical optimization: melody search (ms). 2011 11th International Conference on Hybrid Intelligent Systems (HIS). 2011. https:\/\/doi.org\/10.1109\/his.2011.6122089","DOI":"10.1109\/his.2011.6122089"},{"key":"2716_CR9","unstructured":"Awad NH, Ali MZ, Liang JJ, Qu BY, Suganthan PN. Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Technical Report, Nanyang Technological University, Singapore 2016."},{"key":"2716_CR10","doi-asserted-by":"crossref","unstructured":"Awad NH, Ali MZ and Suganthan PN. Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems. In 2017 IEEE congress on evolutionary computation (CEC) (p. 372\u20139). IEEE. 2017.","DOI":"10.1109\/CEC.2017.7969336"},{"issue":"6","key":"2716_CR11","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1109\/TEVC.2006.872133","volume":"10","author":"J Brest","year":"2006","unstructured":"Brest J, Greiner S, Boskovic B, Mernik M, Zumer V. Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput. 2006;10(6):646\u201357.","journal-title":"IEEE Trans Evol Comput"},{"key":"2716_CR12","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1007\/s10845-018-1419-6","volume":"30","author":"I Brajevi\u0107","year":"2019","unstructured":"Brajevi\u0107 I, Ignjatovi\u0107 J. An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems. J Intell Manuf. 2019;30:2545\u201374.","journal-title":"J Intell Manuf"},{"issue":"9","key":"2716_CR13","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1145\/361002.361007","volume":"18","author":"JL Bentley","year":"1975","unstructured":"Bentley JL. Multidimensional binary search trees used for associative searching. Commun ACM. 1975;18(9):509\u201317.","journal-title":"Commun ACM"},{"key":"2716_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116924","volume":"198","author":"N Chopra","year":"2022","unstructured":"Chopra N, Ansari MM. Golden jackal optimization: a novel nature-inspired optimizer for engineering applications. Expert Syst Appl. 2022;198: 116924.","journal-title":"Expert Syst Appl"},{"issue":"15","key":"2716_CR15","first-page":"8121","volume":"219","author":"P Civicioglu","year":"2013","unstructured":"Civicioglu P. Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput. 2013;219(15):8121\u201344.","journal-title":"Appl Math Comput"},{"issue":"2","key":"2716_CR16","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/S0166-3615(99)00046-9","volume":"41","author":"CAC Coello","year":"2000","unstructured":"Coello CAC. Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind. 2000;41(2):113\u201327.","journal-title":"Comput Ind"},{"issue":"3","key":"2716_CR17","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/S1474-0346(02)00011-3","volume":"16","author":"CAC Coello","year":"2002","unstructured":"Coello CAC, Montes EM. Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv Eng Inform. 2002;16(3):193\u2013203.","journal-title":"Adv Eng Inform"},{"issue":"4","key":"2716_CR18","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1093\/imanum\/drn003","volume":"28","author":"FE Curtis","year":"2008","unstructured":"Curtis FE, Nocedal J. Flexible penalty functions for nonlinear constrained optimization. IMA J Numer Anal. 2008;28(4):749\u201369.","journal-title":"IMA J Numer Anal"},{"key":"2716_CR19","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.advengsoft.2017.05.014","volume":"114","author":"G Dhiman","year":"2017","unstructured":"Dhiman G, Kumar V. Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw. 2017;114:48\u201370.","journal-title":"Adv Eng Softw"},{"key":"2716_CR20","doi-asserted-by":"crossref","unstructured":"Dobnikar A, Steele NC, Pearson DW, Albrecht RF, Deb K and Agrawal S. A niched-penalty approach for constraint handling in genetic algorithms. In Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Portoro\u017e, Slovenia, 1999 (p. 235\u201343). Springer Vienna, 1999.","DOI":"10.1007\/978-3-7091-6384-9_40"},{"issue":"34","key":"2716_CR21","first-page":"226","volume":"96","author":"M Ester","year":"1996","unstructured":"Ester M, Kriegel HP, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise. kdd. 1996;96(34):226\u201331.","journal-title":"kdd"},{"key":"2716_CR22","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ins.2016.06.047","volume":"369","author":"B Ghasemishabankareh","year":"2016","unstructured":"Ghasemishabankareh B, Li X, Ozlen M. Cooperative coevolutionary differential evolution with improved augmented Lagrangian to solve constrained optimisation problems. Inf Sci. 2016;369:441\u201356.","journal-title":"Inf Sci"},{"key":"2716_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.100847","volume":"62","author":"F Han","year":"2021","unstructured":"Han F, Chen WT, Ling QH, Han H. Multi-objective particle swarm optimization with adaptive strategies for feature selection. Swarm Evol Comput. 2021;62: 100847.","journal-title":"Swarm Evol Comput"},{"issue":"2","key":"2716_CR24","first-page":"1407","volume":"186","author":"Q He","year":"2007","unstructured":"He Q, Wang L. A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl Math Comput. 2007;186(2):1407\u201322.","journal-title":"Appl Math Comput"},{"issue":"1","key":"2716_CR25","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.engappai.2006.03.003","volume":"20","author":"Q He","year":"2007","unstructured":"He Q, Wang L. An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell. 2007;20(1):89\u201399.","journal-title":"Eng Appl Artif Intell"},{"key":"2716_CR26","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1007\/s00521-016-2379-4","volume":"28","author":"TT Huan","year":"2017","unstructured":"Huan TT, Kulkarni AJ, Kanesan J, Huang CJ, Abraham A. Ideology algorithm: a socio-inspired optimization methodology. Neural Comput Appl. 2017;28:845\u201376.","journal-title":"Neural Comput Appl"},{"key":"2716_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/s40305-023-00466-4","author":"RP Huang","year":"2023","unstructured":"Huang RP, Xu ZS, Qu SJ, Yang XG, Goh M. Robust portfolio selection with distributional uncertainty and integer constraints. J Oper Res Soc China. 2023. https:\/\/doi.org\/10.1007\/s40305-023-00466-4.","journal-title":"J Oper Res Soc China"},{"key":"2716_CR28","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1007\/s00500-023-09151-3","volume":"28","author":"A Husseinzadeh Kashan","year":"2024","unstructured":"Husseinzadeh Kashan A, Karimiyan S, Kulkarni AJ. The Golf Sport Inspired Search metaheuristic algorithm and the game theoretic analysis of its operators\u2019 effectiveness. Soft Comput. 2024;28:1073\u2013125. https:\/\/doi.org\/10.1007\/s00500-023-09151-3.","journal-title":"Soft Comput"},{"key":"2716_CR29","doi-asserted-by":"crossref","unstructured":"Igel C, Suttorp T and Hansen N. A computational efficient covariance matrix update and a (1+ 1)-CMA for evolution strategies. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation (p. 453\u201360). 2006.","DOI":"10.1145\/1143997.1144082"},{"key":"2716_CR30","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1007\/s40747-021-00283-3","volume":"7","author":"IR Kale","year":"2021","unstructured":"Kale IR, Kulkarni AJ. Cohort intelligence with self-adaptive penalty function approach hybridized with colliding bodies optimization algorithm for discrete and mixed variable constrained problems. Complex Intell Syst. 2021;7:1565\u201396.","journal-title":"Complex Intell Syst"},{"issue":"1","key":"2716_CR31","first-page":"108","volume":"214","author":"D Karaboga","year":"2009","unstructured":"Karaboga D, Akay B. A comparative study of artificial bee colony algorithm. Appl Math Comput. 2009;214(1):108\u201332.","journal-title":"Appl Math Comput"},{"key":"2716_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103541","volume":"90","author":"S Kaur","year":"2020","unstructured":"Kaur S, Awasthi LK, Sangal AL, Dhiman G. Tunicate Swarm Algorithm: a new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intell. 2020;90: 103541.","journal-title":"Eng Appl Artif Intell"},{"key":"2716_CR33","doi-asserted-by":"crossref","unstructured":"Krishnasamy G, Kulkarni AJ and Shastri AS. An improved cohort intelligence with panoptic learning behavior for solving constrained problems. Constraint Handling in Metaheuristics and Applications. 2021;p. 29\u201354.","DOI":"10.1007\/978-981-33-6710-4_2"},{"key":"2716_CR34","first-page":"12","volume":"2011","author":"AJ Kulkarni","year":"2011","unstructured":"Kulkarni AJ, Tai K. A probability collectives approach with a feasibility-based rule for constrained optimization. Appl Comput Intell Soft Comput. 2011;2011:12\u201312.","journal-title":"Appl Comput Intell Soft Comput"},{"key":"2716_CR35","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/s13042-014-0272-y","volume":"7","author":"AJ Kulkarni","year":"2016","unstructured":"Kulkarni AJ, Shabir H. Solving 0\u20131 knapsack problem using cohort intelligence algorithm. Int J Mach Learn Cybern. 2016;7:427\u201341.","journal-title":"Int J Mach Learn Cybern"},{"issue":"3","key":"2716_CR36","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/TEVC.2005.857610","volume":"10","author":"JJ Liang","year":"2006","unstructured":"Liang JJ, Qin AK, Suganthan PN, Baskar S. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput. 2006;10(3):281\u201395.","journal-title":"IEEE Trans Evol Comput"},{"key":"2716_CR37","doi-asserted-by":"publisher","DOI":"10.1007\/s11768-023-00166-7","author":"S Liang","year":"2023","unstructured":"Liang S, Liu S, Hong Y, Chen J. Distributed Nash equilibrium seeking with order-reduced dynamics based on consensus exact penalty. Control Theory Technol. 2023. https:\/\/doi.org\/10.1007\/s11768-023-00166-7.","journal-title":"Control Theory Technol"},{"key":"2716_CR38","doi-asserted-by":"crossref","unstructured":"Luenberger DG, Ye Y, Luenberger DG and Ye Y. Penalty and barrier methods. Linear and Nonlinear Programming, 2016;p. 397\u2013428.","DOI":"10.1007\/978-3-319-18842-3_13"},{"key":"2716_CR39","unstructured":"Omran MGH, Clerc M. 2011. http:\/\/www.particleswarm.info\/ (Last accessed: Oct 3, 2023)"},{"key":"2716_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119211","volume":"213","author":"HL Minh","year":"2023","unstructured":"Minh HL, Sang-To T, Theraulaz G, Wahab MA, Cuong-Le T. Termite life cycle optimizer. Expert Syst Appl. 2023;213: 119211.","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2716_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/evco.1996.4.1.1","volume":"4","author":"Z Michalewicz","year":"1996","unstructured":"Michalewicz Z, Schoenauer M. Evolutionary algorithms for constrained parameter optimization problems. Evol Comput. 1996;4(1):1\u201332.","journal-title":"Evol Comput"},{"key":"2716_CR42","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Softw. 2016;95:51\u201367.","journal-title":"Adv Eng Softw"},{"key":"2716_CR43","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.asoc.2017.11.043","volume":"64","author":"R Moghdani","year":"2018","unstructured":"Moghdani R, Salimifard K. Volleyball premier league algorithm. Appl Soft Comput J. 2018;64:161\u201385. https:\/\/doi.org\/10.1016\/j.asoc.2017.11.043.","journal-title":"Appl Soft Comput J"},{"key":"2716_CR44","doi-asserted-by":"crossref","unstructured":"Mohamed AW, Hadi AA, Fattouh AM and Jambi KM. LSHADE with semi-parameter adaptation hybrid with CMA-ES for solving CEC 2017 benchmark problems. In 2017 IEEE Congress on evolutionary computation (CEC) (p. 145\u2013152). IEEE 2017.","DOI":"10.1109\/CEC.2017.7969307"},{"issue":"29\u201330","key":"2716_CR45","doi-asserted-by":"publisher","first-page":"2527","DOI":"10.1016\/S0045-7949(01)00137-7","volume":"79","author":"P Nanakorn","year":"2001","unstructured":"Nanakorn P, Meesomklin K. An adaptive penalty function in genetic algorithms for structural design optimization. Comput Struct. 2001;79(29\u201330):2527\u201339.","journal-title":"Comput Struct"},{"issue":"6\u20137","key":"2716_CR46","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1016\/j.camwa.2006.05.012","volume":"52","author":"PY Nie","year":"2006","unstructured":"Nie PY. A new penalty method for nonlinear programming. Comput Math Appl. 2006;52(6\u20137):883\u201396.","journal-title":"Comput Math Appl"},{"key":"2716_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110479","volume":"144","author":"B Ozkaya","year":"2023","unstructured":"Ozkaya B, Kahraman HT, Duman S, Guvenc U. Fitness-Distance-Constraint (FDC) based guide selection method for constrained optimization problems. Appl Soft Comput. 2023;144: 110479.","journal-title":"Appl Soft Comput"},{"issue":"7","key":"2716_CR48","doi-asserted-by":"publisher","first-page":"3026","DOI":"10.1109\/TCSI.2022.3167790","volume":"69","author":"J Peng","year":"2022","unstructured":"Peng J, Fan B, Liu W. Penalty-based distributed optimal control of DC microgrids with enhanced current regulation performance. IEEE Trans Circ Syst I Regul Pap. 2022;69(7):3026\u201336.","journal-title":"IEEE Trans Circ Syst I Regul Pap"},{"key":"2716_CR49","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1007\/s00170-009-2411-2","volume":"47","author":"Y Qin","year":"2010","unstructured":"Qin Y, Brockett A, Ma Y, Razali A, Zhao J, Harrison C, Pan W, Dai X, Loziak D. Micro-manufacturing: research, technology outcomes and development issues. Int J Adv Manuf Technol. 2010;47:821\u201337.","journal-title":"Int J Adv Manuf Technol"},{"key":"2716_CR50","doi-asserted-by":"crossref","unstructured":"Reddy R, Kulkarni AJ, Krishnasamy G, Shastri AS and Gandomi AH. LAB: a leader\u2013advocate\u2013believer-based optimization algorithm. Soft Comput. 2023;1\u201335.","DOI":"10.21203\/rs.3.rs-1927871\/v1"},{"issue":"3","key":"2716_CR51","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.cad.2010.12.015","volume":"43","author":"RV Rao","year":"2011","unstructured":"Rao RV, Savsani VJ, Vakharia DP. Teaching\u2013learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des. 2011;43(3):303\u201315.","journal-title":"Comput Aided Des"},{"key":"2716_CR52","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3527623","author":"HT Saeed Chilmeran","year":"2022","unstructured":"Saeed Chilmeran HT, Hamed ET, Ahmed HI, Al-Bayati AY. A method of two new augmented lagrange multiplier versions for solving constrained problems. Int J Math Math Sci. 2022. https:\/\/doi.org\/10.1155\/2022\/3527623.","journal-title":"Int J Math Math Sci"},{"issue":"7\u201310","key":"2716_CR53","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1016\/S0965-9978(02)00060-1","volume":"33","author":"CJ Shih","year":"2002","unstructured":"Shih CJ, Yang Y. Generalized Hopfield network based structural optimization using sequential unconstrained minimization technique with additional penalty strategy. Adv Eng Softw. 2002;33(7\u201310):721\u20139.","journal-title":"Adv Eng Softw"},{"issue":"12","key":"2716_CR54","doi-asserted-by":"publisher","first-page":"12367","DOI":"10.1002\/int.23091","volume":"37","author":"N Singh","year":"2022","unstructured":"Singh N, Houssein EH, Mirjalili S, Cao Y, Selvachandran G. An efficient improved African vultures optimization algorithm with dimension learning hunting for traveling salesman and large-scale optimization applications. Int J Intell Syst. 2022;37(12):12367\u2013421.","journal-title":"Int J Intell Syst"},{"key":"2716_CR55","unstructured":"Tian H, Jagana JS, Zhang Q and Ierapetritou M. Feasibility\/flexibility-based optimization for process design and operations."},{"key":"2716_CR56","unstructured":"Wu, G., Mallipeddi, R. and Suganthan, P.N., 2017. Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization. National University of Defense Technology, Changsha, Hunan, PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical Report."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-02716-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-02716-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-02716-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T13:13:41Z","timestamp":1711718021000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-02716-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,29]]},"references-count":56,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["2716"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-02716-5","relation":{},"ISSN":["2661-8907"],"issn-type":[{"type":"electronic","value":"2661-8907"}],"subject":[],"published":{"date-parts":[[2024,3,29]]},"assertion":[{"value":"6 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2024","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 have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This work does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human and\/or Animals"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"376"}}