{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T22:21:39Z","timestamp":1787437299363,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032362223","type":"print"},{"value":"9783032362230","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:00:00Z","timestamp":1787443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:00:00Z","timestamp":1787443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-3-032-36223-0_14","type":"book-chapter","created":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T21:44:55Z","timestamp":1787435095000},"page":"216-232","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning-Augmented Cluster-Wise Prediction for\u00a0Dynamic Multi-Objective Optimization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7190-0829","authenticated-orcid":false,"given":"Lijing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6231-3721","authenticated-orcid":false,"given":"Yan","family":"Qiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8134-9076","authenticated-orcid":false,"given":"Juanjuan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,23]]},"reference":[{"issue":"4","key":"14_CR1","first-page":"76","volume":"55","author":"S Jiang","year":"2022","unstructured":"Jiang, S., Zou, J., Yang, S., Yao, X.: Evolutionary dynamic multi-objective optimisation: a survey. ACM Comput. Surv. 55(4), 76 (2022)","journal-title":"ACM Comput. Surv."},{"issue":"1","key":"14_CR2","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1109\/TCYB.2015.2510698","volume":"47","author":"S Jiang","year":"2017","unstructured":"Jiang, S., Yang, S.: Evolutionary dynamic multiobjective optimization: benchmarks and algorithm comparisons. IEEE Trans. Cybern. 47(1), 198\u2013211 (2017)","journal-title":"IEEE Trans. Cybern."},{"issue":"5","key":"14_CR3","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.: Dynamic multiobjective optimization problems: test cases, approximations, and applications. IEEE Trans. Evol. Comput. 8(5), 425\u2013442 (2004)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"14_CR4","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1109\/TEVC.2008.920671","volume":"13","author":"C-K 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":"14_CR5","doi-asserted-by":"publisher","first-page":"108447","DOI":"10.1016\/j.knosys.2022.108447","volume":"243","author":"J Li","year":"2022","unstructured":"Li, J., Liu, R., Wang, R.: A change type-based self-adaptive response strategy for dynamic multi-objective optimization. Knowl.-Based Syst. 243, 108447 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Liu, R., Li, J., Jin, Y., Jiao, L.: A self-adaptive response strategy for dynamic multiobjective evolutionary optimization based on objective space decomposition. Evol. Comput. 29(4), 491\u2013519 (2021)","DOI":"10.1162\/evco_a_00289"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Zhou, A., Jin, Y., Zhang, Q., Sendhoff, B., Tsang, E.: Prediction-based population re-initialization for evolutionary dynamic multiobjective optimization. In: Evolutionary Multi-Criterion Optimization (EMO 2007), LNCS, vol. 4403, pp. 832\u2013846. Springer, Berlin Heidelberg (2007)","DOI":"10.1007\/978-3-540-70928-2_62"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 1201\u20131208 (2006)","DOI":"10.1145\/1143997.1144187"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Zhou, A., Jin, Y., Zhang, Q.: A population prediction strategy for evolutionary dynamic multiobjective optimization. IEEE Trans. Cybern. 44(1), 40\u201353 (2014)","DOI":"10.1109\/TCYB.2013.2245892"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Rong, M., Gong, D., Zhang, Y., Jin, Y., Pedrycz, W.: Multidirectional prediction approach for dynamic multiobjective optimization problems. IEEE Trans. Cybern. 49(9), 3362\u20133374 (2019)","DOI":"10.1109\/TCYB.2018.2842158"},{"issue":"2","key":"14_CR11","first-page":"313","volume":"43","author":"J Ding","year":"2017","unstructured":"Ding, J., Yang, C., Chen, L., Chai, T.: Dynamic multi-objective optimization algorithm based on reference point prediction. Acta Automatica Sinica 43(2), 313\u2013320 (2017)","journal-title":"Acta Automatica Sinica"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Chen, D., Zou, F., Lu, R., Wang, X.: A hybrid fuzzy inference prediction strategy for dynamic multi-objective optimization. Swarm Evol. Comput. 43, 147\u2013165 (2018)","DOI":"10.1016\/j.swevo.2018.05.001"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Wang, F., Liao, F., Li, Y., Wang, H.: A new prediction strategy for dynamic multi-objective optimization using Gaussian mixture model. Inf. Sci. 580, 331\u2013351 (2021)","DOI":"10.1016\/j.ins.2021.08.065"},{"key":"14_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111317","volume":"154","author":"Z Wang","year":"2024","unstructured":"Wang, Z., Xue, L., Guo, Y., Han, M., Liang, S.: Solving dynamic multi-objective optimization problems via quantifying intensity of environment changes and ensemble learning-based prediction strategies. Appl. Soft Comput. 154, 111317 (2024)","journal-title":"Appl. Soft Comput."},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Wei, L., Guo, Z., Fan, R., Sun, H., Zhao, Z.: A prediction strategy based on special points and multiregion knee points for evolutionary dynamic multiobjective optimization. Appl. Intell. 50, 4357\u20134377 (2020)","DOI":"10.1007\/s10489-020-01772-7"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Ma, X., Yang, J., Sun, H., Hu, Z., Wei, L.: Multiregional co-evolutionary algorithm for dynamic multiobjective optimization. Inf. Sci. 545, 1\u201324 (2021)","DOI":"10.1016\/j.ins.2020.07.009"},{"issue":"4","key":"14_CR17","doi-asserted-by":"publisher","first-page":"948","DOI":"10.1109\/TEVC.2024.3393151","volume":"29","author":"Y Ye","year":"2025","unstructured":"Ye, Y., Liu, S., Zhou, J., Lin, Q., Jiang, M., Tan, K.C.: Learning-based directional improvement prediction for dynamic multiobjective optimization. IEEE Trans. Evol. Comput. 29(4), 948\u2013962 (2025)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"14_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2020.100829","volume":"62","author":"S Li","year":"2021","unstructured":"Li, S., Yang, S., Wang, Y., Yue, W., Qiao, J.: A modular neural network-based population prediction strategy for evolutionary dynamic multi-objective optimization. Swarm Evol. Comput. 62, 100829 (2021)","journal-title":"Swarm Evol. Comput."},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Hu, Y., et al.: The IGD-based prediction strategy for dynamic multi-objective optimization. Swarm Evol. Comput. 91, 101713 (2024)","DOI":"10.1016\/j.swevo.2024.101713"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Xu, K., et al.: A cluster prediction strategy with the induced mutation for dynamic multi-objective optimization. Inf. Sci. 661, 120193 (2024)","DOI":"10.1016\/j.ins.2024.120193"},{"key":"14_CR21","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1016\/j.future.2024.07.028","volume":"161","author":"H Peng","year":"2024","unstructured":"Peng, H., Pi, C., Xiong, J., Fan, D., Shen, F.: A dynamic multi-objective evolutionary algorithm with variable stepsize and dual prediction strategies. Futur. Gener. Comput. Syst. 161, 390\u2013403 (2024)","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"5","key":"14_CR22","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.1109\/TEVC.2025.3551323","volume":"29","author":"F Wang","year":"2025","unstructured":"Wang, F., Xie, J., Zhou, A., Tang, K.: A new prediction strategy for dynamic multiobjective optimization using diffusion model. IEEE Trans. Evol. Comput. 29(5), 1575\u20131589 (2025)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"5","key":"14_CR23","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1109\/TEVC.2025.3596468","volume":"29","author":"Y Xie","year":"2025","unstructured":"Xie, Y., Zhao, Q., Zhou, W., Zhu, Z.: Evolutionary dynamic multiobjective optimization with learning across problems. IEEE Trans. Evol. Comput. 29(5), 1561\u20131574 (2025)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Ou, J., et al.: Dynamic multi-objective optimization algorithm via historical collaborative strategy and interval prediction strategy. Swarm Evol. Comput. 101, 102281 (2026)","DOI":"10.1016\/j.swevo.2026.102281"},{"key":"14_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.128915","volume":"296","author":"K Guan","year":"2026","unstructured":"Guan, K., Ouyang, H., Li, S., et al.: Dynamic multi-objective optimization using historical evolutionary learning with global alignment, local descriptor matching and collaborative guidance. Expert Syst. Appl. 296, 128915 (2026)","journal-title":"Expert Syst. Appl."},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Li, G., Liu, Y., Deng, X.: A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment. Complex Intell. Syst. 10, 4421\u20134444 (2024)","DOI":"10.1007\/s40747-024-01369-4"},{"key":"14_CR27","unstructured":"Sun, B., et al.: Artificial physics multi-objective optimization algorithm based on partition-guided population evolution. Appl. Res. Comput. 40(5), 1381\u20131388, 1409 (2023)"},{"key":"14_CR28","unstructured":"MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281\u2013297 (1967)"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merri\u00ebnboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. In: Proceedings of SSST-8, pp. 103\u2013111 (2014)","DOI":"10.3115\/v1\/W14-4012"},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Tian, Y., Cheng, R., Zhang, X., Jin, Y.: PlatEMO: a MATLAB platform for evolutionary multi-objective optimization. IEEE Comput. Intell. Mag. 12(4), 73\u201387 (2017)","DOI":"10.1109\/MCI.2017.2742868"},{"key":"14_CR31","unstructured":"Jiang, S., Yang, S., Yao, X., Tan, K.C., Kaiser, M., Krasnogor, N.: Benchmark Problems for CEC2018 Competition on Dynamic Multiobjective Optimisation. Technical Report, Newcastle University, U.K (2018)"},{"key":"14_CR32","doi-asserted-by":"crossref","unstructured":"Bezerra, L.C.T., L\u00f3pez-Ib\u00e1\u00f1ez, M., St\u00fctzle, T.: An empirical assessment of the properties of inverted generational distance on multi- and many-Objective optimization. In: Evolutionary Multi-Criterion Optimization (EMO 2017), pp. 31\u201345. Springer, Cham (2017)","DOI":"10.1007\/978-3-319-54157-0_3"},{"key":"14_CR33","doi-asserted-by":"crossref","unstructured":"Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bull. 1(6), 80\u201383 (1945)","DOI":"10.2307\/3001968"}],"container-title":["Lecture Notes in Computer Science","Parallel Problem Solving from Nature \u2013 PPSN XIX"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-36223-0_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T21:44:57Z","timestamp":1787435097000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-36223-0_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,23]]},"ISBN":["9783032362223","9783032362230"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-36223-0_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,23]]},"assertion":[{"value":"23 August 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PPSN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel Problem Solving from Nature","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Trento","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ppsn2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}