{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T17:24:26Z","timestamp":1743096266630,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811534249"},{"type":"electronic","value":"9789811534256"}],"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_43","type":"book-chapter","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T19:02:58Z","timestamp":1585767778000},"page":"549-557","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MEAPCA: A Multi-population Evolutionary Algorithm Based on PCA for Multi-objective Optimization"],"prefix":"10.1007","author":[{"given":"Nan-jiang","family":"Dong","sequence":"first","affiliation":[]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,2]]},"reference":[{"key":"43_CR1","volume-title":"Multi-Objective Evolutionary Optimization","author":"JH Zheng","year":"2017","unstructured":"Zheng, J.H., Juan, Z.: Multi-Objective Evolutionary Optimization. Science Press, Beijing (2017)"},{"key":"43_CR2","volume-title":"Multi-Objective Evolutionary Algorithm and Its Application","author":"XX Cui","year":"2006","unstructured":"Cui, X.X.: Multi-Objective Evolutionary Algorithm and Its Application. National Defence Industry Press, Beijing (2006)"},{"issue":"2","key":"43_CR3","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., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182\u2013197 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"6","key":"43_CR4","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2007","unstructured":"Zhang, Q., Li, H.: MOEA\/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712\u2013731 (2007)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"3","key":"43_CR5","first-page":"115","volume":"9","author":"K Deb","year":"1994","unstructured":"Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9(3), 115\u2013148 (1994)","journal-title":"Complex Syst."},{"key":"43_CR6","first-page":"431","volume":"9","author":"KDA Kumar","year":"1995","unstructured":"Kumar, K.D.A., Deb, K.: Real-coded genetic algorithms with simulated binary crossover: studies on multimodal and multiobjective problems. Complex Syst. 9, 431\u2013454 (1995)","journal-title":"Complex Syst."},{"issue":"4","key":"43_CR7","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn, R., Price, K.: Differential evolution \u2013 a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341\u2013359 (1997)","journal-title":"J. Global Optim."},{"issue":"1","key":"43_CR8","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)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"7","key":"43_CR9","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1162\/neco.1997.9.7.1493","volume":"9","author":"N Kambhatla","year":"1997","unstructured":"Kambhatla, N., Leen, T.K.: Dimension reduction by local principal component analysis. Neural Comput. 9(7), 1493\u20131516 (1997)","journal-title":"Neural Comput."},{"key":"43_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5563-6","volume-title":"Nonlinear Multiobjective Optimization","author":"K Miettinen","year":"2012","unstructured":"Miettinen, K.: Nonlinear Multiobjective Optimization. Springer, Boston (2012). \nhttps:\/\/doi.org\/10.1007\/978-1-4615-5563-6"},{"key":"43_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-0348-8280-4","volume-title":"Nonlinear Multiobjective Optimization: A Generalized Homotopy Approach","author":"C Hillermeier","year":"2001","unstructured":"Hillermeier, C.: Nonlinear Multiobjective Optimization: A Generalized Homotopy Approach. Birkhauser, Boston (2001)"},{"key":"43_CR12","doi-asserted-by":"crossref","unstructured":"Helbig, M., Engelbrecht, A.P.: Heterogeneous dynamic vector evaluated particle swarm optimisation for dynamic multi-objective optimisation. In: IEEE Congress on Evolutionary Computation, pp. 3151\u20133159. IEEE (2014)","DOI":"10.1109\/CEC.2014.6900303"},{"issue":"1","key":"43_CR13","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1109\/TEVC.2008.920671","volume":"13","author":"CK Goh","year":"2009","unstructured":"Goh, C.K., Tan, K.C.: A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Trans. Evol. Comput. 13(1), 103\u2013127 (2009)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"43_CR14","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/1-84628-137-7_6","volume-title":"Evolutionary Multiobjective Optimization","author":"K Deb","year":"2005","unstructured":"Deb, K., Thiele, L., Laumanns, M., et al.: Scalable test problems for evolutionary multiobjective optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization, pp. 105\u2013145. Springer, London (2005). \nhttps:\/\/doi.org\/10.1007\/1-84628-137-7_6"},{"key":"43_CR15","doi-asserted-by":"crossref","unstructured":"Deb, K., Sinha, A., Kukkonen, S.: Multi-objective test problems, linkages, and evolutionary methodologies. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, Seattle, Washington, USA, pp. 1141\u20131148 (2006)","DOI":"10.1145\/1143997.1144179"},{"issue":"1","key":"43_CR16","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.: A faster algorithm for calculating hypervolume. IEEE Trans. Evol. Comput. 10(1), 29\u201338 (2006)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"3","key":"43_CR17","doi-asserted-by":"publisher","first-page":"279","DOI":"10.3233\/ICA-170542","volume":"24","author":"L Pan","year":"2017","unstructured":"Pan, L., He, C., Tian, Y., Su, Y., Zhang, X.: A region division based diversity maintaining approach for many-objective optimization. Integr. Comput. Aided Eng. 24(3), 279\u2013296 (2017)","journal-title":"Integr. Comput. Aided Eng."},{"key":"43_CR18","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.asoc.2017.08.024","volume":"61","author":"C He","year":"2017","unstructured":"He, C., Tian, Y., Jin, Y., Zhang, X., Pan, L.: A radial space division based evolutionary algorithm for many-objective optimization. Appl. Soft Comput. 61, 603\u2013621 (2017)","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"43_CR19","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/TEVC.2018.2802784","volume":"23","author":"L Pan","year":"2018","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 (2018)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"43_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2906679","author":"L Pan","year":"2019","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. (2019). \nhttps:\/\/doi.org\/10.1109\/TCYB.2019.2906679","journal-title":"IEEE Trans. Cybern."},{"key":"43_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-030-12598-1_14","volume-title":"Evolutionary Multi-Criterion Optimization","author":"M Ming","year":"2019","unstructured":"Ming, M., Wang, R., Zhang, T.: Evolutionary many-constraint optimization: an exploratory analysis. In: Deb, K., et al. (eds.) EMO 2019. LNCS, vol. 11411, pp. 165\u2013176. Springer, Cham (2019). \nhttps:\/\/doi.org\/10.1007\/978-3-030-12598-1_14"},{"key":"43_CR22","doi-asserted-by":"publisher","first-page":"2288","DOI":"10.1016\/j.energy.2017.11.085","volume":"141","author":"R Wang","year":"2017","unstructured":"Wang, R., Li, G., Ming, M., et al.: An efficient multi-objective model and algorithm for sizing a stand-alone hybrid renewable energy system. Energy 141, 2288\u20132299 (2017)","journal-title":"Energy"}],"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_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T01:34:58Z","timestamp":1585791298000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-3425-6_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789811534249","9789811534256"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-3425-6_43","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"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)"}}]}}