{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T23:12:03Z","timestamp":1771715523362,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:00:00Z","timestamp":1651795200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T00:00:00Z","timestamp":1651795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comb Optim"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s10878-022-00860-3","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T22:07:02Z","timestamp":1651874822000},"page":"794-849","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An efficient spread-based evolutionary algorithm for solving dynamic multi-objective optimization problems"],"prefix":"10.1007","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5620-8340","authenticated-orcid":false,"given":"Alireza","family":"Falahiazar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2441-9477","authenticated-orcid":false,"given":"Arash","family":"Sharifi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5702-2209","authenticated-orcid":false,"given":"Vahid","family":"Seydi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,6]]},"reference":[{"key":"860_CR1","doi-asserted-by":"crossref","unstructured":"Aboud A, Fdhila R, Alimi AM (2017) Dynamic multi objective particle swarm optimization based on a new environment change detection strategy. In: International conference on neural information processing. Springer, pp 258\u2013268","DOI":"10.1007\/978-3-319-70093-9_27"},{"key":"860_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-021-09693-5","author":"B Alsalibi","year":"2022","unstructured":"Alsalibi B, Mirjalili S, Abualigah L, Yahya RI, Gandomi AH (2022) A comprehensive survey on the recent variants and applications of membrane-inspired evolutionary algorithms. Arch Comput Methods Eng. https:\/\/doi.org\/10.1007\/s11831-021-09693-5","journal-title":"Arch Comput Methods Eng"},{"key":"860_CR3","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780195099713.001.0001","volume-title":"Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms","author":"T Back","year":"1996","unstructured":"Back T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Oxford"},{"key":"860_CR4","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1007\/s10878-019-00413-1","volume":"38","author":"M Barkaoui","year":"2019","unstructured":"Barkaoui M, Berger J, Boukhtouta A (2019) An evolutionary approach for the target search problem in uncertain environment. J Combin Optim 38:808\u2013835","journal-title":"J Combin Optim"},{"key":"860_CR5","first-page":"47","volume-title":"Emergence, analysis, and evolution of structures\u2014concepts and strategies across disciplines","author":"T Bartz-Beielstein","year":"2010","unstructured":"Bartz-Beielstein T, Preu\u00df M, Schwefel H-P (2010) Model optimization with evolutionary algorithms. In: Roosen P (ed) Emergence, analysis, and evolution of structures\u2014concepts and strategies across disciplines. Springer, Berlin, pp 47\u201362"},{"key":"860_CR6","unstructured":"Beyer H, Brucherseifer E, Jakob W, Pohlheim H, Sendhoff B, To TB (2002) Evolutionary algorithms-terms and definitions. VDI\/VDE-Richtlinie-3550, Blatt 3"},{"key":"860_CR7","volume-title":"Parallel processing for dynamic multi-objective optimization","author":"M C\u00e1mara Sola","year":"2010","unstructured":"C\u00e1mara Sola M (2010) Parallel processing for dynamic multi-objective optimization. Universidad de Granada, Granada"},{"key":"860_CR8","doi-asserted-by":"publisher","first-page":"3570","DOI":"10.1016\/j.neucom.2008.12.041","volume":"72","author":"M C\u00e1mara","year":"2009","unstructured":"C\u00e1mara M, Ortega J, de Toro F (2009) A single front genetic algorithm for parallel multi-objective optimization in dynamic environments. Neurocomputing 72:3570\u20133579. https:\/\/doi.org\/10.1016\/j.neucom.2008.12.041","journal-title":"Neurocomputing"},{"key":"860_CR9","doi-asserted-by":"publisher","unstructured":"C\u00e1mara M, Ortega J, de Toro F (2010) Approaching dynamic multi-objective optimization problems by using parallel evolutionary algorithms, vol 272, pp 63\u201386. https:\/\/doi.org\/10.1007\/978-3-642-11218-8_4","DOI":"10.1007\/978-3-642-11218-8_4"},{"key":"860_CR10","doi-asserted-by":"publisher","first-page":"105783","DOI":"10.1016\/j.ast.2020.105783","volume":"100","author":"P Champasak","year":"2020","unstructured":"Champasak P, Panagant N, Pholdee N, Bureerat S, Yildiz AR (2020) Self-adaptive many-objective meta-heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial vehicle. Aerosp Sci Technol 100:105783","journal-title":"Aerosp Sci Technol"},{"key":"860_CR11","unstructured":"Cheng R, Gen M (1996) Genetic algorithms for multi-row machine layout problem. In: Engineering design and automation, pp 876\u2013881"},{"key":"860_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRD.2020.2982471","author":"Y Chi","year":"2020","unstructured":"Chi Y, Xu Y, Zhang R (2020) Many-objective robust optimization for dynamic VAR planning to enhance voltage stability of a wind-energy power system. IEEE Tran Power Deliv. https:\/\/doi.org\/10.1109\/TPWRD.2020.2982471","journal-title":"IEEE Tran Power Deliv"},{"key":"860_CR13","doi-asserted-by":"publisher","first-page":"111142","DOI":"10.1016\/j.enpol.2019.111142","volume":"137","author":"TH Christensen","year":"2020","unstructured":"Christensen TH, Friis F, Bettin S, Throndsen W, Ornetzeder M, Skj\u00f8lsvold TM, Ryghaug M (2020) The role of competences, engagement, and devices in configuring the impact of prices in energy demand response: findings from three smart energy pilots with households. Energy Policy 137:111142","journal-title":"Energy Policy"},{"key":"860_CR14","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s10710-005-6164-x","volume":"6","author":"CAC Coello","year":"2005","unstructured":"Coello CAC, Cort\u00e9s NC (2005) Solving multiobjective optimization problems using an artificial immune system. Genet Program Evol Mach 6:163\u2013190. https:\/\/doi.org\/10.1007\/s10710-005-6164-x","journal-title":"Genet Program Evol Mach"},{"key":"860_CR15","volume-title":"Evolutionary algorithms for solving multi-objective problems","author":"CAC Coello","year":"2007","unstructured":"Coello CAC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, vol 5. Springer, Berlin"},{"key":"860_CR16","volume-title":"Introduction to algorithms","author":"TH Cormen","year":"2001","unstructured":"Cormen TH, Stein C, Rivest RL, Leiserson CE (2001) Introduction to algorithms. McGraw-Hill Higher Education, New York"},{"key":"860_CR17","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.ins.2020.01.018","volume":"518","author":"Z Cui","year":"2020","unstructured":"Cui Z, Zhang J, Wu D, Cai X, Wang H, Zhang W, Chen J (2020) Hybrid many-objective particle swarm optimization algorithm for green coal production problem. Inf Sci 518:256\u2013271","journal-title":"Inf Sci"},{"key":"860_CR18","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/978-3-030-62191-9_4","volume-title":"Numerical methods for energy applications","author":"L Czumbil","year":"2021","unstructured":"Czumbil L, Micu DD, Ceclan A (2021) Advanced numerical methods based on artificial intelligence. In: Mahdavi Tabatabaei N, Bizon N (eds) Numerical methods for energy applications. Springer, Cham, pp 93\u2013120. https:\/\/doi.org\/10.1007\/978-3-030-62191-9_4"},{"key":"860_CR19","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1162\/evco.1999.7.3.205","volume":"7","author":"K Deb","year":"1999","unstructured":"Deb K (1999) Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol Comput 7:205\u2013230. https:\/\/doi.org\/10.1162\/evco.1999.7.3.205","journal-title":"Evol Comput"},{"key":"860_CR20","volume-title":"Multi-objective optimization using evolutionary algorithms","author":"K Deb","year":"2001","unstructured":"Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Hoboken"},{"key":"860_CR21","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 (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182\u2013197. https:\/\/doi.org\/10.1109\/4235.996017","journal-title":"IEEE Trans Evol Comput"},{"key":"860_CR22","doi-asserted-by":"publisher","unstructured":"Deb K, Rao N UB, Karthik S (2007) Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling, vol 4403, pp 803\u2013817. https:\/\/doi.org\/10.1007\/978-3-540-70928-2_60","DOI":"10.1007\/978-3-540-70928-2_60"},{"key":"860_CR23","doi-asserted-by":"publisher","unstructured":"Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, 1995. MHS '95, 4\u20136 Oct 1995, pp 39\u201343. https:\/\/doi.org\/10.1109\/MHS.1995.494215","DOI":"10.1109\/MHS.1995.494215"},{"key":"860_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0020-0190(02)00204-1","volume":"82","author":"AE Eiben","year":"2002","unstructured":"Eiben AE, Schoenauer M (2002) Evolutionary computing. Inf Process Lett 82:1\u20136","journal-title":"Inf Process Lett"},{"key":"860_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-05094-1","volume-title":"Introduction to evolutionary computing","author":"AE Eiben","year":"2003","unstructured":"Eiben AE, Smith JE (2003) Introduction to evolutionary computing, vol 53. Springer, Berlin"},{"key":"860_CR26","volume-title":"Formal engineering design synthesis","author":"KA Erik","year":"2001","unstructured":"Erik KA, Jonathan C (2001) Formal engineering design synthesis. Cambridge University Press, Cambridge"},{"key":"860_CR27","volume-title":"Genetic algorithms and engineering design","author":"M Gen","year":"1997","unstructured":"Gen M, Cheng R (1997) Genetic algorithms and engineering design, 1st edn. Wiley-Interscience, Hoboken","edition":"1"},{"key":"860_CR28","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1007\/s10878-012-9564-x","volume":"28","author":"SF Ghannadpour","year":"2014","unstructured":"Ghannadpour SF, Noori S, Tavakkoli-Moghaddam R (2014) A multi-objective vehicle routing and scheduling problem with uncertainty in customers\u2019 request and priority. J Combin Optim 28:414\u2013446","journal-title":"J Combin Optim"},{"key":"860_CR29","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 KC (2009) A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Trans Evol Comput 13:103\u2013127. https:\/\/doi.org\/10.1109\/tevc.2008.920671","journal-title":"IEEE Trans Evol Comput"},{"key":"860_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/s0377-2217(01)00282-x","volume":"142","author":"RP H\u00e4m\u00e4l\u00e4inen","year":"2002","unstructured":"H\u00e4m\u00e4l\u00e4inen RP, M\u00e4ntysaari J (2002) Dynamic multi-objective heating optimization. Eur J Oper Res 142:1\u201315. https:\/\/doi.org\/10.1016\/s0377-2217(01)00282-x","journal-title":"Eur J Oper Res"},{"key":"860_CR31","doi-asserted-by":"crossref","unstructured":"Hatzakis I, Wallace D (2006) Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. Paper presented at the Proceedings of the 8th annual conference on genetic and evolutionary computation, Seattle, WA, USA","DOI":"10.1145\/1143997.1144187"},{"key":"860_CR32","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 46:1\u201339. https:\/\/doi.org\/10.1145\/2517649","journal-title":"ACM Comput Surv"},{"key":"860_CR33","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TEVC.2017.2771451","volume":"22","author":"M Jiang","year":"2018","unstructured":"Jiang M, Huang Z, Qiu L, Huang W, Yen GG (2018) Transfer learning-based dynamic multiobjective optimization algorithms. IEEE Trans Evol Comput 22:501\u2013514. https:\/\/doi.org\/10.1109\/TEVC.2017.2771451","journal-title":"IEEE Trans Evol Comput"},{"key":"860_CR34","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/TEVC.2020.3004027","volume":"25","author":"M Jiang","year":"2021","unstructured":"Jiang M, Wang Z, Hong H, Yen GG (2021) Knee point-based imbalanced transfer learning for dynamic multiobjective optimization. IEEE Trans Evol Comput 25:117\u2013129. https:\/\/doi.org\/10.1109\/TEVC.2020.3004027","journal-title":"IEEE Trans Evol Comput"},{"key":"860_CR35","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 (2017) Evolutionary dynamic multiobjective optimization: benchmarks and algorithm comparisons. IEEE Trans Cybern 47:198\u2013211","journal-title":"IEEE Trans Cybern"},{"key":"860_CR36","doi-asserted-by":"publisher","unstructured":"Liu M, Zeng W (2012) A fast evolutionary algorithm for dynamic bi-objective optimization problems, pp 130\u2013134. https:\/\/doi.org\/10.1109\/iccse.2012.6295042","DOI":"10.1109\/iccse.2012.6295042"},{"key":"860_CR37","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1146\/annurev.publhealth.23.100901.140546","volume":"23","author":"T Lumley","year":"2002","unstructured":"Lumley T, Diehr P, Emerson S, Chen L (2002) The importance of the normality assumption in large public health data sets. Annu Rev Public Health 23:151\u2013169. https:\/\/doi.org\/10.1146\/annurev.publhealth.23.100901.140546","journal-title":"Annu Rev Public Health"},{"key":"860_CR38","volume-title":"An introduction to statistical methods and data analysis","author":"R Lyman Ott","year":"2015","unstructured":"Lyman Ott R, Longnecker MT (2015) An introduction to statistical methods and data analysis, 7th edn. Brooks Cole, Belmont","edition":"7"},{"key":"860_CR39","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1109\/TEVC.2018.2868770","volume":"23","author":"X Ma","year":"2019","unstructured":"Ma X, Li X, Zhang Q, Tang K, Liang Z, Xie W, Zhu Z (2019) A survey on cooperative co-evolutionary algorithms. IEEE Trans Evol Comput 23:421\u2013441. https:\/\/doi.org\/10.1109\/TEVC.2018.2868770","journal-title":"IEEE Trans Evol Comput"},{"key":"860_CR40","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2011","unstructured":"Pan SJ, Tsang IW, Kwok JT, Yang Q (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22:199\u2013210. https:\/\/doi.org\/10.1109\/TNN.2010.2091281","journal-title":"IEEE Trans Neural Netw"},{"key":"860_CR41","doi-asserted-by":"crossref","unstructured":"Sharma L, Garg PK (2021) Knowledge representation in artificial intelligence: an overview. In: Artificial intelligence, pp 19\u201328","DOI":"10.1201\/9781003140351-3"},{"key":"860_CR42","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-540-75225-7_5","volume-title":"Algorithmic learning theory","author":"A Smola","year":"2007","unstructured":"Smola A, Gretton A, Song L, Sch\u00f6lkopf B (2007) A Hilbert space embedding for distributions. Algorithmic learning theory. Springer, Berlin, pp 13\u201331"},{"key":"860_CR43","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1162\/evco.1994.2.3.221","volume":"2","author":"N Srinivas","year":"1994","unstructured":"Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2:221\u2013248. https:\/\/doi.org\/10.1162\/evco.1994.2.3.221","journal-title":"Evol Comput"},{"key":"860_CR44","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/a:1008202821328","volume":"11","author":"R Storn","year":"1997","unstructured":"Storn R, Price K (1997) Differential evolution\u2014a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341\u2013359. https:\/\/doi.org\/10.1023\/a:1008202821328","journal-title":"J Global Optim"},{"key":"860_CR45","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-85729-652-8","volume-title":"Multi-objective evolutionary optimisation for product design and manufacturing","author":"L Wang","year":"2011","unstructured":"Wang L, Ng AH, Deb K (2011) Multi-objective evolutionary optimisation for product design and manufacturing. Springer, Berlin"},{"key":"860_CR46","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1109\/TEVC.2019.2922834","volume":"24","author":"Q Zhang","year":"2020","unstructured":"Zhang Q, Yang S, Jiang S, Wang R, Li X (2020) Novel prediction strategies for dynamic multiobjective optimization. IEEE Trans Evol Comput 24:260\u2013274. https:\/\/doi.org\/10.1109\/TEVC.2019.2922834","journal-title":"IEEE Trans Evol Comput"},{"key":"860_CR47","doi-asserted-by":"publisher","first-page":"38391","DOI":"10.1109\/ACCESS.2020.2974324","volume":"8","author":"Z Zhu","year":"2020","unstructured":"Zhu Z, Tian X, Xia C, Chen L, Cai Y (2020) A shift vector guided multiobjective evolutionary algorithm based on decomposition for dynamic optimization. IEEE Access 8:38391\u201338403. https:\/\/doi.org\/10.1109\/ACCESS.2020.2974324","journal-title":"IEEE Access"},{"key":"860_CR48","volume-title":"Evolutionary algorithms for multiobjective optimization: methods and applications","author":"E Zitzler","year":"1999","unstructured":"Zitzler E (1999) Evolutionary algorithms for multiobjective optimization: methods and applications. Swiss Federal Institute of Technology (ETH), Z\u00fcrich"},{"key":"860_CR49","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.ins.2019.09.016","volume":"509","author":"F Zou","year":"2020","unstructured":"Zou F, Yen GG, Tang L (2020) A knee-guided prediction approach for dynamic multi-objective optimization. Inf Sci 509:193\u2013209","journal-title":"Inf Sci"}],"container-title":["Journal of Combinatorial Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10878-022-00860-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10878-022-00860-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10878-022-00860-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T07:34:17Z","timestamp":1659080057000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10878-022-00860-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,6]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["860"],"URL":"https:\/\/doi.org\/10.1007\/s10878-022-00860-3","relation":{},"ISSN":["1382-6905","1573-2886"],"issn-type":[{"value":"1382-6905","type":"print"},{"value":"1573-2886","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,6]]},"assertion":[{"value":"4 April 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}