{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:02:16Z","timestamp":1764784936934},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T00:00:00Z","timestamp":1659916800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T00:00:00Z","timestamp":1659916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["1808085MF174"],"award-info":[{"award-number":["1808085MF174"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["1808085QF181"],"award-info":[{"award-number":["1808085QF181"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976101"],"award-info":[{"award-number":["61976101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Anhui Provincial Department of Education","award":["KJ2019A0603"],"award-info":[{"award-number":["KJ2019A0603"]}]},{"name":"research and development project of anhui","award":["201904a05020072"],"award-info":[{"award-number":["201904a05020072"]}]},{"name":"Graduate Research and Innovation Projects of Huaibei Normal University","award":["yx2021023"],"award-info":[{"award-number":["yx2021023"]}]},{"name":"the Natural Science Research Project of Anhui Province","award":["YJS20210463"],"award-info":[{"award-number":["YJS20210463"]}]},{"name":"the funding plan for scientific research activities of academic and technical leaders and reserve candidates in Anhui Province","award":["2021H264"],"award-info":[{"award-number":["2021H264"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.1007\/s10489-022-03883-9","type":"journal-article","created":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T11:02:52Z","timestamp":1659956572000},"page":"9321-9343","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A many-objective evolutionary algorithm based on corner solution and cosine distance"],"prefix":"10.1007","volume":"53","author":[{"given":"Mengzhen","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangzhen","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Debao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaiyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,8]]},"reference":[{"key":"3883_CR1","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.ins.2021.01.015","volume":"563","author":"M Zhang","year":"2021","unstructured":"Zhang M, Wang L, Li W, Hu B, Li D, Wu Q (2021) Many-objective evolutionary algorithm with adaptive reference vector. Inf Sci 563:70\u201390. https:\/\/doi.org\/10.1016\/j.ins.2021.01.015","journal-title":"Inf Sci"},{"key":"3883_CR2","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.envsoft.2018.09.008","volume":"111","author":"M Di Matteo","year":"2019","unstructured":"Di Matteo M, Maier HR, Dandy GC (2019) Many-objective portfolio optimization approach for stormwater management project selection encouraging decision maker buy-in. Environ Model Softw 111:340\u2013355. https:\/\/doi.org\/10.1016\/j.envsoft.2018.09.008","journal-title":"Environ Model Softw"},{"issue":"6","key":"3883_CR3","doi-asserted-by":"publisher","first-page":"3293","DOI":"10.1007\/s10489-020-01887-x","volume":"51","author":"J Xu","year":"2021","unstructured":"Xu J, Zhang Z, Hu Z, Du L, Cai X (2021) A many-objective optimized task allocation scheduling model in cloud computing. Appl Intell 51(6):3293\u20133310. https:\/\/doi.org\/10.1007\/s10489-020-01887-x","journal-title":"Appl Intell"},{"issue":"2","key":"3883_CR4","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/s11831-019-09380-6","volume":"28","author":"Z Hu","year":"2021","unstructured":"Hu Z, Wei Z, Sun H, Yang J, Wei L (2021) Optimization of metal rolling control using soft computing approaches: a review. Arch Comput Methods Eng 28(2):405\u2013421. https:\/\/doi.org\/10.1007\/s11831-019-09380-6","journal-title":"Arch Comput Methods Eng"},{"key":"3883_CR5","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.isatra.2020.02.024","volume":"102","author":"Z Hu","year":"2020","unstructured":"Hu Z, Wei Z, Ma X, Sun H, Yang J (2020) Multi-parameter deep-perception and many-objective autonomous-control of rolling schedule on high speed cold tandem mill. ISA Transactions 102:193\u2013207. https:\/\/doi.org\/10.1016\/j.isatra.2020.02.024","journal-title":"ISA Transactions"},{"key":"3883_CR6","doi-asserted-by":"publisher","unstructured":"Liang Z, Luo T, Hu K, Ma X, Zhu Z (2020) An indicator-based many-objective evolutionary algorithm with boundary protection. IEEE Trans Cybern, p 1\u201314. https:\/\/doi.org\/10.1109\/TCYB.2019.2960302","DOI":"10.1109\/TCYB.2019.2960302"},{"issue":"9","key":"3883_CR7","doi-asserted-by":"publisher","first-page":"2689","DOI":"10.1109\/TCYB.2016.2638902","volume":"47","author":"Y Liu","year":"2017","unstructured":"Liu Y, Gong D, Sun J, Jin Y (2017) A many-objective evolutionary algorithm using a one-by-one selection strategy. IEEE Trans Cybern 47(9):2689\u20132702. https:\/\/doi.org\/10.1109\/TCYB.2016.2638902","journal-title":"IEEE Trans Cybern"},{"key":"3883_CR8","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.ins.2018.12.078","volume":"509","author":"Y Liu","year":"2020","unstructured":"Liu Y, Zhu N, Li K, Li M, Zheng J, Li K (2020) An angle dominance criterion for evolutionary many-objective optimization. Inf Sci 509:376\u2013399. https:\/\/doi.org\/10.1016\/j.ins.2018.12.078","journal-title":"Inf Sci"},{"issue":"3","key":"3883_CR9","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1109\/TEVC.2013.2262178","volume":"18","author":"M Li","year":"2014","unstructured":"Li M, Yang S, Liu X (2014) Shift-based density estimation for pareto-based algorithms in many-objective optimization. IEEE Trans Evol Comput 18(3):348\u2013365. https:\/\/doi.org\/10.1109\/TEVC.2013.2262178","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"3883_CR10","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1109\/TEVC.2018.2866854","volume":"23","author":"Y Tian","year":"2018","unstructured":"Tian Y, Cheng R, Zhang X, Su Y, Jin Y (2018) A strengthened dominance relation considering convergence and diversity for evolutionary many-objective optimization. IEEE Trans Evol Comput 23 (2):331\u2013345. https:\/\/doi.org\/10.1109\/TEVC.2018.2866854","journal-title":"IEEE Trans Evol Comput"},{"key":"3883_CR11","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1007\/978-3-642-37140-0_23","volume-title":"Effect of dominance balance in many-objective optimization. in Evolutionary Multi-Criterion Optimization","author":"K Narukawa","year":"2013","unstructured":"Narukawa K (2013) Effect of dominance balance in many-objective optimization. in Evolutionary Multi-Criterion Optimization. Springer, Berlin, pp 276\u2013290"},{"key":"3883_CR12","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1162\/EVCO_a_00009","volume":"19","author":"J Bader","year":"2011","unstructured":"Bader J, Zitzler E (2011) HypE: an algorithm for fast hypervolume-based many-objective optimization. Evol Comput 19:45\u201376. https:\/\/doi.org\/10.1162\/EVCO_a_00009","journal-title":"Evol Comput"},{"issue":"5","key":"3883_CR13","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1109\/TEVC.2020.2964705","volume":"24","author":"K Shang","year":"2020","unstructured":"Shang K, Ishibuchi H (2020) A new hypervolume-based evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput 24(5):839\u2013852. https:\/\/doi.org\/10.1109\/TEVC.2020.2964705","journal-title":"IEEE Trans Evol Comput"},{"issue":"10","key":"3883_CR14","doi-asserted-by":"publisher","first-page":"2202","DOI":"10.1109\/TCYB.2014.2367526","volume":"45","author":"S Jiang","year":"2014","unstructured":"Jiang S, Zhang J, Ong Y-S, Zhang AN, Tan PS (2014) A simple and fast hypervolume indicator-based multiobjective evolutionary algorithm. IEEE Trans Cybern 45(10):2202\u20132213. https:\/\/doi.org\/10.1109\/TCYB.2014.2367526","journal-title":"IEEE Trans Cybern"},{"issue":"4","key":"3883_CR15","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1162\/106365605774666895","volume":"13","author":"K Deb","year":"2005","unstructured":"Deb K, Mohan M, Mishra S (2005) Evaluating the -domination based multi-objective evolutionary algorithm for a quick computation of pareto-optimal solutions. Trans Cybern 13(4):501\u2013525. https:\/\/doi.org\/10.1162\/106365605774666895","journal-title":"Trans Cybern"},{"key":"3883_CR16","doi-asserted-by":"publisher","unstructured":"Brockhoff D, Zitzler E (2007) Improving hypervolume-based multiobjective evolutionary algorithms by using objective reduction methods. In: 2007 IEEE Congress on Evolutionary Computation. https:\/\/doi.org\/10.1109\/CEC.2007.4424730, pp 2086\u20132093","DOI":"10.1109\/CEC.2007.4424730"},{"issue":"2","key":"3883_CR17","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/TEVC.2018.2791283","volume":"23","author":"Y Sun","year":"2018","unstructured":"Sun Y, Yen GG, Yi Z (2018) IGD indicator-based evolutionary algorithm for many-objective optimization problems. IEEE Trans Evol Comput 23(2):173\u2013187. https:\/\/doi.org\/10.1109\/TEVC.2018.2791283","journal-title":"IEEE Trans Evol Comput"},{"key":"3883_CR18","doi-asserted-by":"publisher","unstructured":"Hern\u00e1ndez G\u00f3mez R, Coello Coello CA (2015) Improved metaheuristic based on the R2 indicator for many-objective optimization. In: Proceedings of the 2015 annual conference on genetic and evolutionary computation. https:\/\/doi.org\/10.1145\/2739480.2754776, pp 679\u2013686","DOI":"10.1145\/2739480.2754776"},{"key":"3883_CR19","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.asoc.2018.02.048","volume":"67","author":"F Li","year":"2018","unstructured":"Li F, Cheng R, Liu J, Jin Y (2018) A two-stage R2 indicator based evolutionary algorithm for many-objective optimization. Appl Soft Comput 67:245\u2013260. https:\/\/doi.org\/10.1016\/j.asoc.2018.02.048","journal-title":"Appl Soft Comput"},{"issue":"3","key":"3883_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/evco_a_00279","volume":"29","author":"RLWBL Jiao","year":"2020","unstructured":"Jiao RLWBL (2020) A decomposition-based evolutionary algorithm with correlative selection mechanism for many-objective optimization. Evol Comput 29(3):1\u201336. https:\/\/doi.org\/10.1162\/evco_a_00279","journal-title":"Evol Comput"},{"key":"3883_CR21","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.eswa.2018.09.025","volume":"118","author":"Z Li","year":"2019","unstructured":"Li Z, Lin K, Nouioua M, Jiang S, Gu Y (2019) DCDG-EA: Dynamic convergence\u2013diversity guided evolutionary algorithm for many-objective optimization. Expert Systems with Applications 118:35\u201351. https:\/\/doi.org\/10.1016\/j.eswa.2018.09.025","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"3883_CR22","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1109\/TEVC.2016.2587808","volume":"21","author":"Y Xiang","year":"2016","unstructured":"Xiang Y, Zhou Y, Li M, Chen Z (2016) A vector angle-based evolutionary algorithm for unconstrained many-objective optimization. IEEE Trans Evol Comput 21(1):131\u2013152. https:\/\/doi.org\/10.1109\/TEVC.2016.2587808","journal-title":"IEEE Trans Evol Comput"},{"key":"3883_CR23","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.neucom.2019.11.011","volume":"381","author":"C Pan","year":"2020","unstructured":"Pan C, Huang J, Hao J, Gong J (2020) Towards zero-shot learning generalization via a cosine distance loss. Neurocomputing 381:167\u2013176. https:\/\/doi.org\/10.1016\/j.neucom.2019.11.011","journal-title":"Neurocomputing"},{"key":"3883_CR24","doi-asserted-by":"publisher","unstructured":"Zitzler E, Laumanns M, Thiele L (2001) SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Evolutionary methods for design, optimization and control with applications to industrial problems. Proceedings of the EUROGEN\u20192001. Athens. Greece, September 19-21. https:\/\/doi.org\/10.3929\/ethz-a-004284029","DOI":"10.3929\/ethz-a-004284029"},{"issue":"1","key":"3883_CR25","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1109\/TEVC.2020.2999100","volume":"25","author":"J Yuan","year":"2021","unstructured":"Yuan J, Liu H-L, Gu F, Zhang Q, He Z (2021) Investigating the properties of indicators and an evolutionary many-objective algorithm using promising regions. IEEE Trans Evol Comput 25(1):75\u201386. https:\/\/doi.org\/10.1109\/TEVC.2020.2999100","journal-title":"IEEE Trans Evol Comput"},{"key":"3883_CR26","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.ins.2021.05.080","volume":"574","author":"J Zhou","year":"2021","unstructured":"Zhou J, Gao L, Li X, Zhang C, Hu C (2021) Hyperplane-driven and projection-assisted search for solving many-objective optimization problems. Inf Sci 574:394\u2013412. https:\/\/doi.org\/10.1016\/j.ins.2021.05.080","journal-title":"Inf Sci"},{"key":"3883_CR27","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1016\/j.ins.2021.12.103","volume":"589","author":"J Zhou","year":"2022","unstructured":"Zhou J, Rao S, Gao L, Lu C, Zheng J, Chan FTS (2022) Self-regulated bi-partitioning evolution for many-objective optimization. Inf Sci 589:827\u2013848. https:\/\/doi.org\/10.1016\/j.ins.2021.12.103","journal-title":"Inf Sci"},{"issue":"4","key":"3883_CR28","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1109\/TEVC.2010.2093579","volume":"15","author":"HK Singh","year":"2011","unstructured":"Singh HK, Isaacs A, Ray T (2011) A pareto corner search evolutionary algorithm and dimensionality reduction in many-objective optimization problems. IEEE Trans Evol Comput 15 (4):539\u2013556. https:\/\/doi.org\/10.1109\/TEVC.2010.2093579","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"3883_CR29","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1109\/TCYB.2013.2247594","volume":"44","author":"H Wang","year":"2014","unstructured":"Wang H, Yao X (2014) Corner sort for pareto-based many-objective optimization. IEEE Trans Cybern 44(1):92\u2013102. https:\/\/doi.org\/10.1109\/TCYB.2013.2247594","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"3883_CR30","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1109\/4235.996017","journal-title":"IEEE Trans Evol Comput"},{"key":"3883_CR31","doi-asserted-by":"publisher","first-page":"106872","DOI":"10.1016\/j.asoc.2020.106872","volume":"99","author":"J Zhou","year":"2021","unstructured":"Zhou J, Yao X, Gao L, Hu C (2021) An indicator and adaptive region division based evolutionary algorithm for many-objective optimization. Appl Soft Comput 99:106872. https:\/\/doi.org\/10.1016\/j.asoc.2020.106872","journal-title":"Appl Soft Comput"},{"issue":"3","key":"3883_CR32","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1109\/TCYB.2019.2918087","volume":"51","author":"Z Liang","year":"2021","unstructured":"Liang Z, Hu K, Ma X, Zhu Z (2021) A many-objective evolutionary algorithm based on a two-round selection strategy. IEEE Transactions on Cybernetics 51(3):1417\u20131429. https:\/\/doi.org\/10.1109\/TCYB.2019.2918087","journal-title":"IEEE Transactions on Cybernetics"},{"key":"3883_CR33","doi-asserted-by":"publisher","first-page":"100769","DOI":"10.1016\/j.swevo.2020.100769","volume":"60","author":"Z Liang","year":"2021","unstructured":"Liang Z, Zeng J, Liu L, Zhu Z (2021) A many-objective optimization algorithm with mutation strategy based on variable classification and elite individual. Swarm Evol Comput 60:100769. https:\/\/doi.org\/10.1016\/j.swevo.2020.100769","journal-title":"Swarm Evol Comput"},{"key":"3883_CR34","doi-asserted-by":"publisher","first-page":"100776","DOI":"10.1016\/j.swevo.2020.100776","volume":"60","author":"W Qiu","year":"2021","unstructured":"Qiu W, Zhu J, Wu G, Fan M, Suganthan PN (2021) Evolutionary many-objective algorithm based on fractional dominance relation and improved objective space decomposition strategy. Swarm Evol Comput 60:100776. https:\/\/doi.org\/10.1016\/j.swevo.2020.100776","journal-title":"Swarm Evol Comput"},{"issue":"4","key":"3883_CR35","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1007\/s10489-020-01874-2","volume":"51","author":"Z Xiong","year":"2021","unstructured":"Xiong Z, Yang J, Hu Z, Zhao Z, Wang X (2021) Evolutionary many-objective optimization algorithm based on angle and clustering. Appl Intell 51(4):2045\u20132062. https:\/\/doi.org\/10.1007\/s10489-020-01874-2","journal-title":"Appl Intell"},{"issue":"2","key":"3883_CR36","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1109\/TEVC.2020.3035825","volume":"25","author":"P Zhang","year":"2020","unstructured":"Zhang P, Li J, Li T, Chen H (2020) A new many-objective evolutionary algorithm based on determinantal point processes. IEEE Trans Evol Comput 25(2):334\u2013345. https:\/\/doi.org\/10.1109\/TEVC.2020.3035825","journal-title":"IEEE Trans Evol Comput"},{"issue":"2","key":"3883_CR37","first-page":"115","volume":"9","author":"K Deb","year":"1995","unstructured":"Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Complex Syst 9(2):115\u2013148","journal-title":"Complex Syst"},{"key":"3883_CR38","first-page":"30","volume":"26","author":"K Deb","year":"1996","unstructured":"Deb K, Goyal M (1996) A combined genetic adaptive search (geneAS) for engineering design. Computer Science and informatics 26:30\u201345","journal-title":"Computer Science and informatics"},{"key":"3883_CR39","doi-asserted-by":"publisher","first-page":"101026","DOI":"10.1016\/j.swevo.2021.101026","volume":"69","author":"Y Su","year":"2022","unstructured":"Su Y, Luo N, Lin Q, Li X (2022) Many-objective optimization by using an immune algorithm. Swarm Evol Comput 69:101026. https:\/\/doi.org\/10.1016\/j.swevo.2021.101026","journal-title":"Swarm Evol Comput"},{"issue":"2","key":"3883_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3300148","volume":"52","author":"M Li","year":"2019","unstructured":"Li M, Yao X (2019) Quality evaluation of solution sets in multiobjective optimisation: a survey. ACM Computing Surveys (CSUR) 52(2):1\u201338. https:\/\/doi.org\/10.1145\/3300148","journal-title":"ACM Computing Surveys (CSUR)"},{"issue":"2","key":"3883_CR41","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/TEVC.2003.810758","volume":"7","author":"E Zitzler","year":"2003","unstructured":"Zitzler E, Thiele L, Laumanns M, Fonseca CM, Da Fonseca VG (2003) Performance assessment of multiobjective optimizers: An analysis and review. IEEE Trans Evol Comput 7(2):117\u2013132. https:\/\/doi.org\/10.1109\/TEVC.2003.810758","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"3883_CR42","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1145\/321906.321910","volume":"22","author":"HT Kung","year":"1975","unstructured":"Kung HT, Luccio F, Preparata FP (1975) On finding the maxima of a set of vectors. J. ACM 22(4):469\u2013476. https:\/\/doi.org\/10.1145\/321906.321910","journal-title":"J. ACM"},{"issue":"5","key":"3883_CR43","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1109\/TEVC.2003.817234","volume":"7","author":"MT Jensen","year":"2003","unstructured":"Jensen MT (2003) Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms. IEEE Trans Evol Comput 7(5):503\u2013515. https:\/\/doi.org\/10.1109\/TEVC.2003.817234","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"3883_CR44","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","volume":"18","author":"K Deb","year":"2013","unstructured":"Deb K, Jain H (2013) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans Evol Comput 18(4):577\u2013601. https:\/\/doi.org\/10.1109\/TEVC.2013.2281535","journal-title":"IEEE Trans Evol Comput"},{"key":"3883_CR45","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.ins.2021.03.008","volume":"563","author":"L Li","year":"2021","unstructured":"Li L, Yen GG, Sahoo A, Chang L, Gu T (2021) On the estimation of pareto front and dimensional similarity in many-objective evolutionary algorithm. Inf Sci 563:375\u2013400. https:\/\/doi.org\/10.1016\/j.ins.2021.03.008","journal-title":"Inf Sci"},{"key":"3883_CR46","doi-asserted-by":"publisher","unstructured":"Deb K, Thiele L, Laumanns M, Zitzler E (2005) Scalable test problems for evolutionary multiobjective optimization. In: Evolutionary Multiobjective Optimization, p 105\u2013145. https:\/\/doi.org\/10.1007\/1-84628-137-7_6","DOI":"10.1007\/1-84628-137-7_6"},{"key":"3883_CR47","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s40747-017-0039-7","volume":"47","author":"YTXZSYYJXY Ran Cheng","year":"2017","unstructured":"Ran Cheng YTXZSYYJXY, Li Miqing (2017) A benchmark test suite for evolutionary many-objective optimization. IEEE Trans Cybern 47:67\u201381. https:\/\/doi.org\/10.1007\/s40747-017-0039-7","journal-title":"IEEE Trans Cybern"},{"issue":"5","key":"3883_CR48","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1109\/TEVC.2005.861417","volume":"10","author":"S Huband","year":"2006","unstructured":"Huband S, Hingston P, Barone L, While L (2006) A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans Evol Comput 10(5):477\u2013506. https:\/\/doi.org\/10.1109\/TEVC.2005.861417","journal-title":"IEEE Trans Evol Comput"},{"key":"3883_CR49","doi-asserted-by":"publisher","first-page":"106078","DOI":"10.1016\/j.asoc.2020.106078","volume":"89","author":"R Tanabe","year":"2020","unstructured":"Tanabe R, Ishibuchi H (2020) An easy-to-use real-world multi-objective optimization problem suite. Appl Soft Comput 89:106078. https:\/\/doi.org\/10.1016\/j.asoc.2020.106078","journal-title":"Appl Soft Comput"},{"issue":"4","key":"3883_CR50","doi-asserted-by":"publisher","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. IEEE Comput Intell Mag 12(4):73\u201387. https:\/\/doi.org\/10.1109\/MCI.2017.2742868","journal-title":"IEEE Comput Intell Mag"},{"issue":"2","key":"3883_CR51","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1109\/TCYB.2019.2932451","volume":"51","author":"Q Zhu","year":"2021","unstructured":"Zhu Q, Lin Q, Li J, Coello Coello CA, Ming Z, Chen J, Zhang J (2021) An elite gene guided reproduction operator for many-objective optimization. IEEE Trans Cybern 51(2):765\u2013778. https:\/\/doi.org\/10.1109\/TCYB.2019.2932451","journal-title":"IEEE Trans Cybern"},{"key":"3883_CR52","doi-asserted-by":"publisher","first-page":"100775","DOI":"10.1016\/j.swevo.2020.100775","volume":"60","author":"J Zou","year":"2021","unstructured":"Zou J, Liu J, Yang S, Zheng J (2021) A many-objective evolutionary algorithm based on rotation and decomposition. Swarm Evol Comput 60:100775. https:\/\/doi.org\/10.1016\/j.swevo.2020.100775","journal-title":"Swarm Evol Comput"},{"issue":"2","key":"3883_CR53","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1109\/TEVC.2015.2443001","volume":"20","author":"Y Yuan","year":"2016","unstructured":"Yuan Y, Xu H, Wang B, Zhang B, Yao X (2016) Balancing convergence and diversity in decomposition-based many-objective optimizers. IEEE Trans Evol Comput 20(2):180\u2013198. https:\/\/doi.org\/10.1109\/TEVC.2015.2443001","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"3883_CR54","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1109\/TEVC.2013.2281534","volume":"18","author":"H Jain","year":"2014","unstructured":"Jain H, Deb K (2014) An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: Handling constraints and extending to an adaptive approach. IEEE Trans Evol Comput 18(4):602\u2013622. https:\/\/doi.org\/10.1109\/TEVC.2013.2281534","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"3883_CR55","doi-asserted-by":"publisher","first-page":"61","DOI":"10.5957\/jsr.2004.48.1.61","volume":"48","author":"MG Parsons","year":"2004","unstructured":"Parsons MG, Scott RL (2004) Formulation of multicriterion design optimization problems for solution with scalar numerical optimization methods. J Ship Res 48(1):61\u201376. https:\/\/doi.org\/10.5957\/jsr.2004.48.1.61","journal-title":"J Ship Res"},{"issue":"4","key":"3883_CR56","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1080\/03052150108940926","volume":"33","author":"T Ray","year":"2001","unstructured":"Ray T, Tai K, Seow KC (2001) Multiobjective design optimization by an evolutionary algorithm. Eng Optim 33(4):399\u2013424. https:\/\/doi.org\/10.1080\/03052150108940926","journal-title":"Eng Optim"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03883-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03883-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03883-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T09:23:52Z","timestamp":1682846632000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03883-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,8]]},"references-count":56,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["3883"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03883-9","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,8]]},"assertion":[{"value":"10 June 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}