{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T04:29:40Z","timestamp":1780460980484,"version":"3.54.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2018,1,27]],"date-time":"2018-01-27T00:00:00Z","timestamp":1517011200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61502408"],"award-info":[{"award-number":["61502408"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61403326"],"award-info":[{"award-number":["61403326"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673331"],"award-info":[{"award-number":["61673331"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"he Hunan Province Natural Science Foundation","award":["14JJ2072"],"award-info":[{"award-number":["14JJ2072"]}]},{"name":"CERNET Innovation Project","award":["NGII20150302"],"award-info":[{"award-number":["NGII20150302"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502408"],"award-info":[{"award-number":["61502408"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Education Department Major Project of Hunan Province","award":["17A212"],"award-info":[{"award-number":["17A212"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2019,6]]},"DOI":"10.1007\/s00500-018-3033-0","type":"journal-article","created":{"date-parts":[[2018,1,27]],"date-time":"2018-01-27T17:23:02Z","timestamp":1517073782000},"page":"3723-3739","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["A predictive strategy based on special points for evolutionary dynamic multi-objective optimization"],"prefix":"10.1007","volume":"23","author":[{"given":"Qingya","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengxiang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhua","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gan","family":"Ruan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,1,27]]},"reference":[{"key":"3033_CR1","doi-asserted-by":"crossref","unstructured":"Abello MB, Bui LT, Michalewicz Z (2011) An adaptive approach for solving dynamic scheduling with time-varying number of tasks Part II. In: 2011 IEEE congress of evolutionary computation (CEC), IEEE, pp 1711\u20131718","DOI":"10.1109\/CEC.2011.5949821"},{"key":"3033_CR2","first-page":"133","volume":"5","author":"VS Aragn","year":"2005","unstructured":"Aragn VS, Esquivel SC, Coello Coello C (2005) Evolutionary multiobjective optimization in non-stationary environments. J Comput Sci Technol 5:133\u2013144","journal-title":"J Comput Sci Technol"},{"key":"3033_CR3","unstructured":"Azevedo CRB, Arajo AFR (2011) Generalized immigration schemes for dynamic evolutionary multiobjective optimization. In: 2011 IEEE congress of evolutionary computation (CEC), IEEE, pp 2033\u20132040"},{"key":"3033_CR4","volume-title":"Evolutionary optimization in dynamic environments","author":"J Branke","year":"2012","unstructured":"Branke J (2012) Evolutionary optimization in dynamic environments. Springer Science & Business Media, Berlin"},{"key":"3033_CR5","doi-asserted-by":"crossref","unstructured":"Branke J, Deb K, Dierolf H, et al (2004) Finding knees in multi-objective optimization. In: International conference on parallel problem solving from nature, Springer, Berlin, pp 722\u2013731","DOI":"10.1007\/978-3-540-30217-9_73"},{"issue":"1","key":"3033_CR6","first-page":"204","volume":"20","author":"L Chun\u2019an","year":"2009","unstructured":"Chun\u2019an L, Yuping W (2009) Multiobjective evolutionary algorithm for dynamic nonlinear constrained optimization problems. J Syst Eng Electron 20(1):204\u2013210","journal-title":"J Syst Eng Electron"},{"issue":"16","key":"3033_CR7","doi-asserted-by":"publisher","first-page":"3570","DOI":"10.1016\/j.neucom.2008.12.041","volume":"72","author":"M Cmara","year":"2009","unstructured":"Cmara M, Ortega J, de Toro F (2009) A single front genetic algorithm for parallel multi-objective optimization in dynamic environments. Neurocomputing 72(16):3570\u20133579","journal-title":"Neurocomputing"},{"key":"3033_CR8","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-642-11218-8_4","volume-title":"Approaching dynamic multi-objective optimization problems by using parallel evolutionary algorithms. Advances in multi-objective nature inspired computing","author":"M Cmara","year":"2010","unstructured":"Cmara M, Ortega J, de Toro F (2010) Approaching dynamic multi-objective optimization problems by using parallel evolutionary algorithms. Advances in multi-objective nature inspired computing. Springer, Berlin, pp 63\u201386"},{"issue":"3","key":"3033_CR9","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1109\/TEVC.2004.826067","volume":"8","author":"CAC Coello","year":"2004","unstructured":"Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256\u2013279","journal-title":"IEEE Trans Evol Comput"},{"key":"3033_CR10","volume-title":"Evolutionary algorithms for solving multi-objective problems","author":"CC Coello","year":"2007","unstructured":"Coello CC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems. Springer Science & Business Media, Berlin"},{"issue":"2\u20133","key":"3033_CR11","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/BF01195985","volume":"18","author":"I Das","year":"1999","unstructured":"Das I (1999) On characterizing the knee of the Pareto curve based on normal-boundary intersection. Struct Optim 18(2\u20133):107\u2013115","journal-title":"Struct Optim"},{"issue":"11","key":"3033_CR12","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1080\/0305215X.2010.548863","volume":"43","author":"K Deb","year":"2011","unstructured":"Deb K, Gupta S (2011) Understanding knee points in bicriteria problems and their implications as preferred solution principles. Eng Optim 43(11):1175\u20131204","journal-title":"Eng Optim"},{"key":"3033_CR13","doi-asserted-by":"crossref","unstructured":"Deb K, Agrawal S, Pratap A, et al (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: International conference on parallel problem solving from nature, Springer, Berlin pp 849\u2013858","DOI":"10.1007\/3-540-45356-3_83"},{"key":"3033_CR14","doi-asserted-by":"crossref","unstructured":"Deb K, Karthik S (2007) Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling. In: International conference on evolutionary multi-criterion optimization, Springer, Berlin, pp 803\u2013817","DOI":"10.1007\/978-3-540-70928-2_60"},{"issue":"5","key":"3033_CR15","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1109\/TEVC.2004.831456","volume":"8","author":"M Farina","year":"2004","unstructured":"Farina M, Deb K, Amato P (2004) Dynamic multiobjective optimization problems: test cases, approximations, and applications. IEEE Trans Evol Comput 8(5):425\u2013442","journal-title":"IEEE Trans Evol Comput"},{"key":"3033_CR16","first-page":"5","volume":"186","author":"CK Goh","year":"2009","unstructured":"Goh CK, Tan KC (2009) Evolutionary multi-objective optimization in uncertain environments. Stud Comput Intell Issues Algorithms 186:5\u201318","journal-title":"Stud Comput Intell Issues Algorithms"},{"issue":"1","key":"3033_CR17","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1109\/TEVC.2008.920671","volume":"13","author":"CK Goh","year":"2009","unstructured":"Goh CK, Tan KC (2009) A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Trans Evol Comput 13(1):103\u2013127","journal-title":"IEEE Trans Evol Comput"},{"key":"3033_CR18","doi-asserted-by":"crossref","unstructured":"Greeff M, Engelbrecht AP (2008) Solving dynamic multi-objective problems with vector evaluated particle swarm optimisation. In: 2008 IEEE congress on evolutionary computation (IEEE world congress on computational intelligence), IEEE, pp 2917\u20132924","DOI":"10.1109\/CEC.2008.4631190"},{"issue":"3","key":"3033_CR19","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/s10462-004-5900-6","volume":"23","author":"SU Guan","year":"2005","unstructured":"Guan SU, Chen Q, Mo W (2005) Evolving dynamic multi-objective optimization problems with objective replacement. Artif Intell Rev 23(3):267\u2013293","journal-title":"Artif Intell Rev"},{"key":"3033_CR20","doi-asserted-by":"crossref","unstructured":"Hatzakis I, Wallace D (2006) Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Proceedings of the 8th annual conference on genetic and evolutionary computation, ACM, pp 1201\u20131208","DOI":"10.1145\/1143997.1144187"},{"key":"3033_CR21","doi-asserted-by":"crossref","unstructured":"Hatzakis I, Wallace D (2006) Topology of anticipatory populations for evolutionary dynamic multi-objective optimization. In: 11th AIAA\/ISSMO multidisciplinary analysis and optimization conference","DOI":"10.2514\/6.2006-7071"},{"issue":"3","key":"3033_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2517649","volume":"46","author":"M Helbig","year":"2014","unstructured":"Helbig M, Engelbrecht AP (2014) Benchmarks for dynamic multi-objective optimisation algorithms. ACM Comput Surv 46(3):1\u201339","journal-title":"ACM Comput Surv"},{"key":"3033_CR23","doi-asserted-by":"crossref","unstructured":"Helbig M, Engelbrecht AP (2012) Analyses of guide update approaches for vector evaluated particle swarm optimisation on dynamic multi-objective optimisation problems. In: 2012 IEEE congress on evolutionary computation, IEEE, pp 1\u20138","DOI":"10.1109\/CEC.2012.6252882"},{"key":"3033_CR24","doi-asserted-by":"crossref","unstructured":"Isaacs A, Puttige V, Ray T, et al (2008) Development of a memetic algorithm for dynamic multi-objective optimization and its applications for online neural network modeling of UAVs. In: 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), IEEE, pp 548\u2013554","DOI":"10.1109\/IJCNN.2008.4633847"},{"key":"3033_CR25","first-page":"1","volume":"99","author":"S Jiang","year":"2016","unstructured":"Jiang S, Yang S (2016) Evolutionary dynamic multiobjective optimization: benchmarks and algorithm comparisons. IEEE Trans Cybernet 99:1\u201314","journal-title":"IEEE Trans Cybernet"},{"issue":"1","key":"3033_CR26","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1109\/TEVC.2016.2574621","volume":"21","author":"S Jiang","year":"2017","unstructured":"Jiang S, Yang S (2017) A steady-state and generational evolutionary algorithm for dynamic multiobjective optimization. IEEE Trans Evol Comput 21(1):65\u201382","journal-title":"IEEE Trans Evol Comput"},{"issue":"3","key":"3033_CR27","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1109\/TEVC.2005.846356","volume":"9","author":"Y Jin","year":"2005","unstructured":"Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments-a survey. IEEE Trans Evol Comput 9(3):303\u2013317","journal-title":"IEEE Trans Evol Comput"},{"key":"3033_CR28","unstructured":"Jin Y, Sendhoff B (2004) Constructing dynamic optimization test problems, using the multi-objective optimization concept. In: Applications of evolutionary computing, EvoWorkshops, (2004) EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC, Coimbra, Portugal, April 5\u20137. Proceedings. 2004, pp 525\u2013536"},{"key":"3033_CR29","doi-asserted-by":"crossref","unstructured":"Kim K, McKay RI, Moon BR (2010) Multiobjective evolutionary algorithms for dynamic social network clustering. In: Proceedings of the 12th annual conference on Genetic and evolutionary computation, ACM, pp 1179\u20131186","DOI":"10.1145\/1830483.1830699"},{"key":"3033_CR30","unstructured":"Li K, Deb K, Performance assessment for preference-based evolutionary multi-objective optimization using reference points"},{"key":"3033_CR31","doi-asserted-by":"crossref","unstructured":"Liu C, Wang Y (2006) New evolutionary algorithm for dynamic multiobjective optimization problems. In: International conference on natural computation, Springer Berlin Heidelberg, pp 889\u2013892","DOI":"10.1007\/11881070_117"},{"key":"3033_CR32","doi-asserted-by":"crossref","unstructured":"Liu R, Zhang W, Jiao L, et al (2010) A sphere-dominance based preference immune-inspired algorithm for dynamic multi-objective optimization. In: Proceedings of the 12th annual conference on genetic and evolutionary computation, ACM, pp 423\u2013430","DOI":"10.1145\/1830483.1830565"},{"key":"3033_CR33","doi-asserted-by":"crossref","unstructured":"Ma Y, Liu R, Shang R (2011) A hybrid dynamic multi-objective immune optimization algorithm using prediction strategy and improved differential evolution crossover operator. In: International conference on neural information processing. Springer, Berlin, pp 435\u2013444","DOI":"10.1007\/978-3-642-24958-7_51"},{"key":"3033_CR34","doi-asserted-by":"crossref","unstructured":"Martins FVC, Carrano EG, Wanner EF, et al (2009) A dynamic multiobjective hybrid approach for designing wireless sensor networks. In: 2009 IEEE congress on evolutionary computation, IEEE, pp 1145\u20131152","DOI":"10.1109\/CEC.2009.4983075"},{"issue":"2","key":"3033_CR35","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1016\/j.ejor.2008.07.015","volume":"197","author":"J Molina","year":"2009","unstructured":"Molina J, Santana LV, Hernndez-Daz AG et al (2009) g-dominance: Reference point based dominance for multiobjective metaheuristics. Eur J Oper Res 197(2):685\u2013692","journal-title":"Eur J Oper Res"},{"issue":"12","key":"3033_CR36","doi-asserted-by":"publisher","first-page":"2862","DOI":"10.1109\/TCYB.2015.2490738","volume":"46","author":"A Muruganantham","year":"2016","unstructured":"Muruganantham A, Tan KC, Vadakkepat P (2016) Evolutionary dynamic multiobjective optimization via kalman filter prediction. IEEE Trans Cybern 46(12):2862\u20132873","journal-title":"IEEE Trans Cybern"},{"key":"3033_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2012.05.001","volume":"6","author":"TT Nguyen","year":"2012","unstructured":"Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evolut Comput 6:1\u201324","journal-title":"Swarm Evolut Comput"},{"issue":"9","key":"3033_CR38","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.1007\/s00500-014-1433-3","volume":"19","author":"Z Peng","year":"2015","unstructured":"Peng Z, Zheng J, Zou J et al (2015) Novel prediction and memory strategies for dynamic multiobjective optimization. Soft Comput 19(9):2633\u20132653","journal-title":"Soft Comput"},{"key":"3033_CR39","doi-asserted-by":"crossref","unstructured":"Peng Z, Zheng J, Zou J (2014) A population diversity maintaining strategy based on dynamic environment evolutionary model for dynamic multiobjective optimization. In: 2014 IEEE congress on evolutionary computation (CEC), IEEE, pp 274\u2013281","DOI":"10.1109\/CEC.2014.6900268"},{"key":"3033_CR40","doi-asserted-by":"publisher","first-page":"3781","DOI":"10.1007\/s00500-016-2370-0","volume":"21.13","author":"S Qian","year":"2017","unstructured":"Qian S, Ye Y, Jiang B et al (2017) A micro-cloning dynamic multiobjective algorithm with an adaptive change reaction strategy. Soft Comput 21.13:3781\u20133801","journal-title":"Soft Comput"},{"key":"3033_CR41","unstructured":"Qian S, Ye Y, Jiang B, et al (2016) A micro-cloning dynamic multiobjective algorithm with an adaptive change reaction strategy. Soft Comput 1\u201321"},{"key":"3033_CR42","unstructured":"Rabil BS, Sabourin R, Granger E (2011) Watermarking stack of grayscale face images as dynamic multi-objective optimization problem. In: MDA, pp 63\u201377"},{"key":"3033_CR43","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1016\/j.asoc.2017.05.008","volume":"58","author":"G Ruan","year":"2017","unstructured":"Ruan G, Yu G, Zheng J, Zou J, Yang S (2017) The effect of diversity maintenance on prediction in dynamic multi-objective optimization. Appl Soft Comput 58:631\u2013647","journal-title":"Appl Soft Comput"},{"issue":"5","key":"3033_CR44","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1109\/TEVC.2010.2041060","volume":"14","author":"LB Said","year":"2010","unstructured":"Said LB, Bechikh S, Ghdira 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"},{"issue":"3","key":"3033_CR45","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1162\/evco.2009.17.3.411","volume":"17","author":"L Thiele","year":"2009","unstructured":"Thiele L, Miettinen K, Korhonen PJ et al (2009) A preference-based evolutionary algorithm for multi-objective optimization. Evol Comput 17(3):411\u2013436","journal-title":"Evol Comput"},{"key":"3033_CR46","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.procs.2011.04.018","volume":"4","author":"E Vinek","year":"2011","unstructured":"Vinek E, Beran PP, Schikuta E (2011) A dynamic multi-objective optimization framework for selecting distributed deployments in a heterogeneous environment. Proced Comput Sci 4:166\u2013175","journal-title":"Proced Comput Sci"},{"key":"3033_CR47","doi-asserted-by":"crossref","unstructured":"Wei J, Zhang M (2011) Simplex model based evolutionary algorithm for dynamic multi-objective optimization. In: Australasian joint conference on artificial intelligence, Springer, Berlin, pp 372\u2013381","DOI":"10.1007\/978-3-642-25832-9_38"},{"issue":"1","key":"3033_CR48","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/TEVC.2005.851275","volume":"10","author":"L While","year":"2006","unstructured":"While L, Hingston P, Barone L et al (2006) A faster algorithm for calculating hypervolume. IEEE Trans Evol Comput 10(1):29\u201338","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"3033_CR49","doi-asserted-by":"publisher","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80\u201383","journal-title":"Biom Bull"},{"issue":"5","key":"3033_CR50","first-page":"677","volume":"28","author":"Y Wu","year":"2013","unstructured":"Wu Y, Liu XX, Chi CZ (2013) Predictive multiobjective genetic algorithm for dynamic multiobjective optimization problems. Control Decis 28(5):677\u2013682","journal-title":"Control Decis"},{"issue":"11","key":"3033_CR51","doi-asserted-by":"publisher","first-page":"3221","DOI":"10.1007\/s00500-014-1477-4","volume":"19","author":"Y Wu","year":"2015","unstructured":"Wu Y, Jin Y, Liu X (2015) A directed search strategy for evolutionary dynamic multiobjective optimization. Soft Comput 19(11):3221\u20133235","journal-title":"Soft Comput"},{"key":"3033_CR52","unstructured":"Yu G, Zheng J, Shen R, et al (2015) Decomposing the user-preference in multiobjective optimization. Soft Comput pp 1\u201317"},{"key":"3033_CR53","doi-asserted-by":"crossref","unstructured":"Zeng S, Chen S, Zhao J, et al (2011) Dynamic constrained multi-objective model for solving constrained optimization problem. In: 2011 IEEE congress of evolutionary computation (CEC), IEEE, pp 2041\u20132046","DOI":"10.1109\/CEC.2011.5949866"},{"issue":"2","key":"3033_CR54","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1016\/j.asoc.2007.07.005","volume":"8","author":"Z Zhang","year":"2008","unstructured":"Zhang Z (2008) Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control. Appl Soft Comput 8(2):959\u2013971","journal-title":"Appl Soft Comput"},{"issue":"1","key":"3033_CR55","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/TEVC.2007.894202","volume":"12","author":"Q Zhang","year":"2008","unstructured":"Zhang Q, Zhou A, Jin Y (2008) RM-MEDA: a regularity model-based multiobjective estimation of distribution algorithm. IEEE Trans Evol Comput 12(1):41\u201363","journal-title":"IEEE Trans Evol Comput"},{"issue":"6","key":"3033_CR56","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1109\/TEVC.2014.2378512","volume":"19","author":"X Zhang","year":"2015","unstructured":"Zhang X, Tian Y, Jin Y (2015) A knee point-driven evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput 19(6):761\u2013776","journal-title":"IEEE Trans Evol Comput"},{"key":"3033_CR57","unstructured":"Zheng JH, Peng Z, Zou J, et al (2015) A prediction strategy based on guide-individual for dynamic multi-objective optimization"},{"key":"3033_CR58","doi-asserted-by":"crossref","unstructured":"Zheng J, Yu G, Zhu Q, et al. (2016) On decomposition methods in interactive user-preference based optimization. Appl Soft Comput","DOI":"10.1016\/j.asoc.2016.09.032"},{"issue":"1","key":"3033_CR59","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1109\/TCYB.2013.2245892","volume":"44","author":"A Zhou","year":"2014","unstructured":"Zhou A, Jin Y, Zhang Q (2014) A population prediction strategy for evolutionary dynamic multiobjective optimization. IEEE Trans Cybern 44(1):40\u201353","journal-title":"IEEE Trans Cybern"},{"key":"3033_CR60","doi-asserted-by":"crossref","unstructured":"Zhou A, Jin Y, Zhang Q, et al (2007) Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization. In: International conference on evolutionary multi-criterion optimization, Springer, Berlin, pp 832\u2013846","DOI":"10.1007\/978-3-540-70928-2_62"},{"key":"3033_CR61","unstructured":"Ziztler E, Laumanns M, Thiele L (2001) SPEA 2: improving the strength pareto evolutionary algorithm. Technical report 103, Computer Engineering and Networks Laboratory, ETH, Zurich, Switzerland, Prix de leau, redevance prleve sur lusager"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-018-3033-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-018-3033-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-018-3033-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,23]],"date-time":"2019-04-23T11:20:12Z","timestamp":1556018412000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-018-3033-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,27]]},"references-count":61,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2019,6]]}},"alternative-id":["3033"],"URL":"https:\/\/doi.org\/10.1007\/s00500-018-3033-0","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,27]]},"assertion":[{"value":"27 January 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}