{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:10:36Z","timestamp":1742947836242,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030125974"},{"type":"electronic","value":"9783030125981"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-12598-1_5","type":"book-chapter","created":{"date-parts":[[2019,2,2]],"date-time":"2019-02-02T14:41:50Z","timestamp":1549118510000},"page":"53-65","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A New Hybrid Metaheuristic for Equality Constrained Bi-objective Optimization Problems"],"prefix":"10.1007","author":[{"given":"Oliver","family":"Cuate","sequence":"first","affiliation":[]},{"given":"Lourdes","family":"Uribe","sequence":"additional","affiliation":[]},{"given":"Antonin","family":"Ponsich","sequence":"additional","affiliation":[]},{"given":"Adriana","family":"Lara","sequence":"additional","affiliation":[]},{"given":"Fernanda","family":"Beltran","sequence":"additional","affiliation":[]},{"given":"Alberto Rodr\u00edguez","family":"S\u00e1nchez","sequence":"additional","affiliation":[]},{"given":"Oliver","family":"Sch\u00fctze","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,2,3]]},"reference":[{"issue":"3","key":"5_CR1","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)","journal-title":"Eur. J. Oper. Res."},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Bosman, P.A.N., de Jong, E.D.: Exploiting gradient information in numerical multi-objective evolutionary optimization. In: Genetic and Evolutionary Computation Conference - GECCO 2005. ACM (2005)","DOI":"10.1145\/1068009.1068138"},{"issue":"1","key":"5_CR3","first-page":"3","volume":"6","author":"M Brown","year":"2005","unstructured":"Brown, M., Smith, R.E.: Directed multi-objective optimization. Int. J. Comput. Syst. Sig. 6(1), 3\u201317 (2005)","journal-title":"Int. J. Comput. Syst. Sig."},{"key":"5_CR4","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":"2","key":"5_CR5","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)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Dilettoso, E., Rizzo, S.A., Salerno, N.: A weakly Pareto compliant quality indicator. Math. Comput. Appl. 22(1) (2017)","DOI":"10.3390\/mca22010025"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Dominguez-Isidro, S., Mezura-Montes, E.: The baldwin effect on a memetic differential evolution for constrained numerical optimization problems. In: Proceedings of the Genetic and Evolutionary Optimization Conference, pp. 1\u20138 (2017)","DOI":"10.1145\/3067695.3076096"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Fan, Z., et al.: An improved epsilon constraint handling method embedded in MOEA\/D for constrained multi-objective optimization problems. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1\u20138 (2016)","DOI":"10.1109\/SSCI.2016.7850224"},{"key":"5_CR9","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1137\/08071692X","volume":"20","author":"J Fliege","year":"2009","unstructured":"Fliege, J., Drummond, L.M.G., Svaiter, B.F.: Newton\u2019s method for multiobjective optimization. SIAM J. Optim. 20, 602\u2013626 (2009)","journal-title":"SIAM J. Optim."},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Gerstl, K., Rudolph, G., Sch\u00fctze, O., Trautmann, H.: Finding evenly spaced fronts for multiobjective control via averaging Hausdorff-measure. In: Proceedings of 8th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1\u20136. IEEE Press (2011)","DOI":"10.1109\/ICEEE.2011.6106656"},{"key":"5_CR11","unstructured":"Hern\u00e1ndez-Ocana, B., Mezura-Montes, E., del Pilar Pozos-Parra, M.: Evolutionary bacterial foraging algorithm to solve constrained numerical optimization problems. In: New Tendencies in Logic, Languages, Algorithms, and New Methods of Reasoning, pp. 29\u201342 (2016)"},{"key":"5_CR12","unstructured":"Hu, X., Huang, Z., Wang, Z.: Hybridization of the multi-objective evolutionary algorithms and the gradient-based algorithms. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 870\u2013877 (2003)"},{"key":"5_CR13","series-title":"Studies in Fuzziness and Soft Computing","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/3-540-32363-5_14","volume-title":"Recent Advances in Memetic Algorithms","author":"J Knowles","year":"2005","unstructured":"Knowles, J., Corne, D.: Memetic algorithms for multiojective optimization: issues, methods and prospects. In: Hart, W.E., Smith, J.E., Krasnogor, N. (eds.) Recent Advances in Memetic Algorithms. STUDFUZZ, vol. 166, pp. 313\u2013352. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/3-540-32363-5_14"},{"key":"5_CR14","unstructured":"Knowles, J.D., Corne, D.W.: M-PAES: a memetic algorithm for multiobjective optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, Piscataway, New Jersey, pp. 325\u2013332 (2000)"},{"key":"5_CR15","unstructured":"Kukkonen, S., Lampinen, J.: GDE3: the third evolution step of generalized differential evolution. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 1, pp. 443\u2013450. IEEE (2005)"},{"issue":"5","key":"5_CR16","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/TEVC.2014.2373386","volume":"19","author":"K Li","year":"2015","unstructured":"Li, K., Deb, K., Zhang, Q., Kwong, S.: An evolutionary many-objective optimization algorithm based on dominance and decomposition. IEEE Trans. Evol. Comput. 19(5), 694\u2013716 (2015)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"3","key":"5_CR17","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1080\/0305215X.2017.1327579","volume":"50","author":"A Mart\u00edn","year":"2018","unstructured":"Mart\u00edn, A., Sch\u00fctze, O.: Pareto tracer: a predictor-corrector method for multi-objective optimization problems. Eng. Optim. 50(3), 516\u2013536 (2018)","journal-title":"Eng. Optim."},{"issue":"2","key":"5_CR18","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1109\/TEVC.2003.819944","volume":"8","author":"YW Ong","year":"2004","unstructured":"Ong, Y.W., Keane, A.J.: Meta-Lamarckian learning in memetic algorithms. IEEE Trans. Evol. Comput. 8(2), 99\u2013110 (2004)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Peitz, S., Dellnitz, M.: A survey of recent trends in multiobjective optimal control-surrogate models, feedback control and objective reduction. Math. Comput. Appl. 23(2) (2018)","DOI":"10.3390\/mca23020030"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Saha, A., Ray, T.: Equality constrained multi-objective optimization. In: 2012 IEEE Congress on Evolutionary Computation, CEC 2012, pp. 1\u20137, June 2012","DOI":"10.1109\/CEC.2012.6256109"},{"issue":"5","key":"5_CR21","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1080\/03052150701821328","volume":"40","author":"O Sch\u00fctze","year":"2008","unstructured":"Sch\u00fctze, O., Coello Coello, C.A., Mostaghim, S., Talbi, E.-G., Dellnitz, M.: Hybridizing evolutionary strategies with continuation methods for solving multi-objective problems. Eng. Optim. 40(5), 383\u2013402 (2008)","journal-title":"Eng. Optim."},{"issue":"4","key":"5_CR22","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1109\/TEVC.2011.2161872","volume":"16","author":"O Sch\u00fctze","year":"2012","unstructured":"Sch\u00fctze, O., Esquivel, X., Lara, A., Coello Coello, C.A.: Using the averaged Hausdorff distance as a performance measure in evolutionary multiobjective optimization. IEEE Trans. Evol. Comput. 16(4), 504\u2013522 (2012)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"21","key":"5_CR23","doi-asserted-by":"publisher","first-page":"6331","DOI":"10.1007\/s00500-016-2187-x","volume":"21","author":"O Sch\u00fctze","year":"2017","unstructured":"Sch\u00fctze, O., Alvarado, S., Segura, C., Landa, R.: Gradient subspace approximation: a direct search method for memetic computing. Soft Comput. 21(21), 6331\u20136350 (2017)","journal-title":"Soft Comput."},{"key":"5_CR24","unstructured":"Takahama, T., Sakai, S.: Constrained optimization by the $$\\epsilon $$-constrained differential evolution with gradient-based mutation and feasible elites (2006)"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Takahama, T., Sakai, S., Iwane, N.: Solving nonlinear constrained optimization problems by the $$\\varepsilon $$ constrained differential evolution. In: IEEE 2006, vol. 3, pp. 2322\u20132327. IEEE (2006)","DOI":"10.1109\/ICSMC.2006.385209"},{"key":"5_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1007\/978-3-642-44973-4_8","volume-title":"Learning and Intelligent Optimization","author":"H Trautmann","year":"2013","unstructured":"Trautmann, H., Wagner, T., Brockhoff, D.: R2-EMOA: focused multiobjective search using R2-indicator-based selection. In: Nicosia, G., Pardalos, P. (eds.) LION 2013. LNCS, vol. 7997, pp. 70\u201374. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-44973-4_8"}],"container-title":["Lecture Notes in Computer Science","Evolutionary Multi-Criterion Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-12598-1_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:22:06Z","timestamp":1710264126000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-12598-1_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030125974","9783030125981"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-12598-1_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"3 February 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EMO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Evolutionary Multi-Criterion Optimization","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"East Lansing, MI","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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":"10 March 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 March 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"emo2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.emo2019.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":"76","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":"59","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":"78% - 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":"3.6","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":"4.1","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}