{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T19:40:23Z","timestamp":1769629223117,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Singapore","isbn-type":[{"value":"9789811534249","type":"print"},{"value":"9789811534256","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-981-15-3425-6_28","type":"book-chapter","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T19:02:58Z","timestamp":1585767778000},"page":"355-369","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Clustering-Based Multiobjective Evolutionary Algorithm for Balancing Exploration and Exploitation"],"prefix":"10.1007","author":[{"given":"Wei","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Jianyu","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Chenghu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jianyong","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,2]]},"reference":[{"issue":"1","key":"28_CR1","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.: HypE: an algorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 19(1), 45\u201376 (2011). \nhttps:\/\/doi.org\/10.1162\/evco_a_00009","journal-title":"Evol. Comput."},{"issue":"3","key":"28_CR2","doi-asserted-by":"publisher","first-page":"1653","DOI":"10.1016\/j.ejor.2006.08.008","volume":"181","author":"N Beume","year":"2007","unstructured":"Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181(3), 1653\u20131669 (2007). \nhttps:\/\/doi.org\/10.1016\/j.ejor.2006.08.008","journal-title":"Eur. J. Oper. Res."},{"issue":"2","key":"28_CR3","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","volume":"10","author":"JC Bezdek","year":"1984","unstructured":"Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the Fuzzy C-Means clustering algorithm. Comput. Geosci. 10(2), 191\u2013203 (1984). \nhttps:\/\/doi.org\/10.1016\/0098-3004(84)90020-7","journal-title":"Comput. Geosci."},{"key":"28_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-7566-5","volume-title":"Pattern Recognition and Machine Learning","author":"C Bishop","year":"2006","unstructured":"Bishop, C.: Pattern Recognition and Machine Learning. Springer, New York (2006). \nhttps:\/\/doi.org\/10.1007\/978-1-4615-7566-5"},{"key":"28_CR5","doi-asserted-by":"publisher","unstructured":"Coello Coello, C.A., Lechuga, M.S.: MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002 (Cat. No. 02TH8600), vol. 2, pp. 1051\u20131056, May 2002. \nhttps:\/\/doi.org\/10.1109\/CEC.2002.1004388","DOI":"10.1109\/CEC.2002.1004388"},{"key":"28_CR6","unstructured":"Corne, D.W., Jerram, N.R., Knowles, J.D., Oates, M.J.: PESA-II: region-based selection in evolutionary multiobjective optimization. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, GECCO 2001, pp. 283\u2013290. Morgan Kaufmann Publishers Inc., San Francisco (2001). \nhttp:\/\/dl.acm.org\/citation.cfm?id=2955239.2955289"},{"issue":"2","key":"28_CR7","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.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182\u2013197 (2002). \nhttps:\/\/doi.org\/10.1109\/4235.996017","journal-title":"IEEE Trans. Evol. Comput."},{"key":"28_CR8","volume-title":"Multi-Objective Optimization Using Evolutionary Algorithms","author":"K Deb","year":"2001","unstructured":"Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)"},{"issue":"3","key":"28_CR9","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1145\/331499.331504","volume":"31","author":"AK Jain","year":"1999","unstructured":"Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264\u2013323 (1999). \nhttps:\/\/doi.org\/10.1145\/331499.331504","journal-title":"ACM Comput. Surv."},{"issue":"2","key":"28_CR10","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1109\/tevc.2008.925798","volume":"13","author":"H Li","year":"2009","unstructured":"Li, H., Zhang, Q.: Multiobjective optimization problems with complicated Pareto sets, MOEA\/D and NSGA-II. IEEE Trans. Evol. Comput. 13(2), 284\u2013302 (2009). \nhttps:\/\/doi.org\/10.1109\/tevc.2008.925798","journal-title":"IEEE Trans. Evol. Comput."},{"key":"28_CR11","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.swevo.2018.02.009","volume":"43","author":"X Li","year":"2018","unstructured":"Li, X., Zhang, H., Song, S.: A self-adaptive mating restriction strategy based on survival length for evolutionary multiobjective optimization. Swarm Evol. Comput. 43, 31\u201349 (2018). \nhttps:\/\/doi.org\/10.1016\/j.swevo.2018.02.009","journal-title":"Swarm Evol. Comput."},{"key":"28_CR12","doi-asserted-by":"publisher","unstructured":"Lin, X., Zhang, Q., Kwong, S.: A decomposition based multiobjective evolutionary algorithm with classification. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 3292\u20133299, July 2016. \nhttps:\/\/doi.org\/10.1109\/CEC.2016.7744206","DOI":"10.1109\/CEC.2016.7744206"},{"issue":"1","key":"28_CR13","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/TEVC.2018.2802784","volume":"23","author":"L Pan","year":"2019","unstructured":"Pan, L., He, C., Tian, Y., Wang, H., Zhang, X., Jin, Y.: A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization. IEEE Trans. Evol. Comput. 23(1), 74\u201388 (2019). \nhttps:\/\/doi.org\/10.1109\/TEVC.2018.2802784","journal-title":"IEEE Trans. Evol. Comput."},{"key":"28_CR14","doi-asserted-by":"publisher","unstructured":"Pan, L., Li, L., He, C., Tan, K.C.: A subregion division-based evolutionary algorithm with effective mating selection for many-objective optimization. IEEE Trans. Cybern. 1\u201314 (2019). \nhttps:\/\/doi.org\/10.1109\/TCYB.2019.2906679","DOI":"10.1109\/TCYB.2019.2906679"},{"key":"28_CR15","doi-asserted-by":"publisher","unstructured":"Shi, J., Zhang, Q., Sun, J.: PPLS\/D: Parallel Pareto local search based on decomposition. IEEE Trans. Cybern. 1\u201312 (2018). \nhttps:\/\/doi.org\/10.1109\/TCYB.2018.2880256","DOI":"10.1109\/TCYB.2018.2880256"},{"issue":"4","key":"28_CR16","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1109\/TEVC.2018.2865495","volume":"23","author":"J Sun","year":"2019","unstructured":"Sun, J., et al.: Learning from a stream of nonstationary and dependent data in multiobjective evolutionary optimization. IEEE Trans. Evol. Comput. 23(4), 541\u2013555 (2019). \nhttps:\/\/doi.org\/10.1109\/TEVC.2018.2865495","journal-title":"IEEE Trans. Evol. Comput."},{"key":"28_CR17","doi-asserted-by":"publisher","unstructured":"Sun, J., Zhang, H., Zhang, Q., Chen, H.: Balancing exploration and exploitation in multiobjective evolutionary optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2018, pp. 199\u2013200. ACM, New York (2018). \nhttps:\/\/doi.org\/10.1145\/3205651.3205708","DOI":"10.1145\/3205651.3205708"},{"key":"28_CR18","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.swevo.2018.04.009","volume":"44","author":"J Sun","year":"2019","unstructured":"Sun, J., Zhang, H., Zhou, A., Zhang, Q., Zhang, K.: A new learning-based adaptive multi-objective evolutionary algorithm. Swarm Evol. Comput. 44, 304\u2013319 (2019). \nhttps:\/\/doi.org\/10.1016\/j.swevo.2018.04.009","journal-title":"Swarm Evol. Comput."},{"issue":"5","key":"28_CR19","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1109\/TEVC.2018.2794319","volume":"22","author":"Y Sun","year":"2018","unstructured":"Sun, Y., Yen, G.G., Yi, Z.: Improved regularity model-based EDA for many-objective optimization. IEEE Trans. Evol. Comput. 22(5), 662\u2013678 (2018). \nhttps:\/\/doi.org\/10.1109\/TEVC.2018.2794319","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"4","key":"28_CR20","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.: PlatEMO: a MATLAB platform for evolutionary multi-objective optimization (educational forum). IEEE Comput. Intell. Mag. 12(4), 73\u201387 (2017). \nhttps:\/\/doi.org\/10.1109\/MCI.2017.2742868","journal-title":"IEEE Comput. Intell. Mag."},{"issue":"3","key":"28_CR21","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1109\/TEVC.2018.2865931","volume":"23","author":"M Wu","year":"2019","unstructured":"Wu, M., Li, K., Kwong, S., Zhang, Q., Zhang, J.: Learning to decompose: a paradigm for decomposition-based multiobjective optimization. IEEE Trans. Evol. Comput. 23(3), 376\u2013390 (2019). \nhttps:\/\/doi.org\/10.1109\/TEVC.2018.2865931","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"5","key":"28_CR22","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1109\/TEVC.2012.2227145","volume":"17","author":"S Yang","year":"2013","unstructured":"Yang, S., Li, M., Liu, X., Zheng, J.: A grid-based evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 17(5), 721\u2013736 (2013). \nhttps:\/\/doi.org\/10.1109\/TEVC.2012.2227145","journal-title":"IEEE Trans. Evol. Comput."},{"key":"28_CR23","doi-asserted-by":"publisher","unstructured":"Zhang, H., Song, S., Zhou, A., Gao, X.: A clustering based multiobjective evolutionary algorithm. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 723\u2013730, July 2014. \nhttps:\/\/doi.org\/10.1109\/CEC.2014.6900519","DOI":"10.1109\/CEC.2014.6900519"},{"key":"28_CR24","doi-asserted-by":"publisher","unstructured":"Zhang, J., Zhou, A., Zhang, G.: A classification and Pareto domination based multiobjective evolutionary algorithm. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 2883\u20132890, May 2015. \nhttps:\/\/doi.org\/10.1109\/CEC.2015.7257247","DOI":"10.1109\/CEC.2015.7257247"},{"key":"28_CR25","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.ins.2018.06.073","volume":"465","author":"J Zhang","year":"2018","unstructured":"Zhang, J., Zhou, A., Tang, K., Zhang, G.: Preselection via classification: a case study on evolutionary multiobjective optimization. Inf. Sci. 465, 388\u2013403 (2018). \nhttps:\/\/doi.org\/10.1016\/j.ins.2018.06.073","journal-title":"Inf. Sci."},{"key":"28_CR26","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1007\/978-3-662-49014-3_56","volume-title":"Bio-Inspired Computing \u2013 Theories and Applications","author":"J Zhang","year":"2015","unstructured":"Zhang, J., Zhou, A., Zhang, G.: A multiobjective evolutionary algorithm based on decomposition and preselection. In: Gong, M., Pan, L., Song, T., Tang, K., Zhang, X. (eds.) BIC-TA 2015. CCIS, vol. 562, pp. 631\u2013642. Springer, Heidelberg (2015). \nhttps:\/\/doi.org\/10.1007\/978-3-662-49014-3_56"},{"issue":"1","key":"28_CR27","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.: RM-MEDA: a regularity model-based multiobjective estimation of distribution algorithm. IEEE Trans. Evol. Comput. 12(1), 41\u201363 (2008). \nhttps:\/\/doi.org\/10.1109\/TEVC.2007.894202","journal-title":"IEEE Trans. Evol. Comput."},{"key":"28_CR28","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2008","unstructured":"Zhang, Q., Li, H.: MOEA\/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11, 712\u2013731 (2008). \nhttps:\/\/doi.org\/10.1109\/TEVC.2007.892759","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"28_CR29","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/TEVC.2016.2600642","volume":"22","author":"X Zhang","year":"2018","unstructured":"Zhang, X., Tian, Y., Cheng, R., Jin, Y.: A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization. IEEE Trans. Evol. Comput. 22(1), 97\u2013112 (2018). \nhttps:\/\/doi.org\/10.1109\/TEVC.2016.2600642","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"28_CR30","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.swevo.2011.03.001","volume":"1","author":"A Zhou","year":"2011","unstructured":"Zhou, A., Qu, B.Y., Li, H., Zhao, S.Z., Suganthan, P.N., Zhang, Q.: Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol. Comput. 1(1), 32\u201349 (2011). \nhttps:\/\/doi.org\/10.1016\/j.swevo.2011.03.001","journal-title":"Swarm Evol. Comput."},{"issue":"4","key":"28_CR31","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/4235.797969","volume":"3","author":"E Zitzler","year":"1999","unstructured":"Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257\u2013271 (1999). \nhttps:\/\/doi.org\/10.1109\/4235.797969","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"2","key":"28_CR32","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, C.M., Fonseca, V.G.D.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117\u2013132 (2003). \nhttps:\/\/doi.org\/10.1109\/TEVC.2003.810758","journal-title":"IEEE Trans. Evol. Comput."},{"key":"28_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/978-3-540-88908-3_14","volume-title":"Multiobjective Optimization","author":"E Zitzler","year":"2008","unstructured":"Zitzler, E., Knowles, J., Thiele, L.: Quality assessment of Pareto set approximations. In: Branke, J., Deb, K., Miettinen, K., S\u0142owi\u0144ski, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 373\u2013404. Springer, Heidelberg (2008). \nhttps:\/\/doi.org\/10.1007\/978-3-540-88908-3_14"}],"container-title":["Communications in Computer and Information Science","Bio-inspired Computing: Theories and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-3425-6_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T01:32:47Z","timestamp":1585791167000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-3425-6_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811534249","9789811534256"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-3425-6_28","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"2 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIC-TA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bio-Inspired Computing: Theories and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bicta2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2019.bicta.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"197","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"121","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"61% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}