{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T05:44:27Z","timestamp":1757310267633,"version":"3.37.3"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s00521-023-09110-x","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T02:01:41Z","timestamp":1699408901000},"page":"1381-1411","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multimodal multi-objective optimization via determinantal point process-assisted evolutionary algorithm"],"prefix":"10.1007","volume":"36","author":[{"given":"Xinyu","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1610-6865","authenticated-orcid":false,"given":"Wenyin","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Ming","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,8]]},"reference":[{"key":"9110_CR1","doi-asserted-by":"publisher","first-page":"2247","DOI":"10.1007\/s00521-021-06355-2","volume":"34","author":"T Zheng","year":"2022","unstructured":"Zheng T, Liu J, Liu Y, Tan S (2022) Hybridizing multi-objective, clustering and particle swarm optimization for multimodal optimization. Neural Comput Appl 34:2247\u20132274","journal-title":"Neural Comput Appl"},{"issue":"11","key":"9110_CR2","doi-asserted-by":"publisher","first-page":"2772","DOI":"10.1109\/TFUZZ.2020.2998174","volume":"28","author":"E Babaee Tirkolaee","year":"2020","unstructured":"Babaee Tirkolaee E, Goli A, Weber G-W (2020) Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm for just-in-time energy-aware flow shop scheduling problem with outsourcing option. IEEE Trans Fuzzy Syst 28(11):2772\u20132783","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"5","key":"9110_CR3","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1109\/TEVC.2017.2754271","volume":"22","author":"C Yue","year":"2018","unstructured":"Yue C, Qu B, Liang J (2018) A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems. IEEE Trans Evolut Computat 22(5):805\u2013817","journal-title":"IEEE Trans Evolut Computat"},{"key":"9110_CR4","unstructured":"Sebag M, Tarrisson N, Teytaud O, Lefevre J, Baillet S (2005) A multi-objective multi-modal optimization approach for mining stable spatio-temporal patterns. In: IJCAI-05, proceedings of the nineteenth international joint conference on artificial intelligence, Edinburgh, Scotland, UK, July 30-August 5, pp. 859\u2013864"},{"issue":"4","key":"9110_CR5","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1109\/TEVC.2002.802873","volume":"6","author":"A Jaszkiewicz","year":"2002","unstructured":"Jaszkiewicz A (2002) On the performance of multiple-objective genetic local search on the 0\/1 knapsack problem-a comparative experiment. IEEE Trans Evolut Computat 6(4):402\u2013412","journal-title":"IEEE Trans Evolut Computat"},{"key":"9110_CR6","doi-asserted-by":"crossref","unstructured":"Ishibuchi H, Yamane M, Akedo N, Nojima Y (2013) Many-objective and many-variable test problems for visual examination of multiobjective search. In: Evolutionary Computation","DOI":"10.1109\/CEC.2013.6557739"},{"issue":"4","key":"9110_CR7","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1109\/TCSS.2021.3061439","volume":"8","author":"S Han","year":"2021","unstructured":"Han S, Zhu K, Zhou M, Cai X (2021) Information-utilization-method-assisted multimodal multiobjective optimization and application to credit card fraud detection. IEEE Trans Computat Soc Syst 8(4):856\u2013869","journal-title":"IEEE Trans Computat Soc Syst"},{"issue":"5","key":"9110_CR8","doi-asserted-by":"publisher","first-page":"886","DOI":"10.1109\/TEVC.2021.3117702","volume":"26","author":"Y Peng","year":"2021","unstructured":"Peng Y, Ishibuchi H (2021) A diversity-enhanced subset selection framework for multi-modal multi-objective optimization. IEEE Trans Evolut Computat 26(5):886\u2013900","journal-title":"IEEE Trans Evolut Computat"},{"key":"9110_CR9","doi-asserted-by":"publisher","first-page":"13715","DOI":"10.1007\/s00521-020-04779-w","volume":"32","author":"AE Rizk-Allah","year":"2020","unstructured":"Rizk-Allah AE, Rizk M, Hassanien Slowik A (2020) Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization. Neural Comput Appl 32:13715\u201313746","journal-title":"Neural Comput Appl"},{"issue":"2","key":"9110_CR10","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 Evolut Computat 6(2):182\u2013197","journal-title":"IEEE Trans Evolut Computat"},{"key":"9110_CR11","first-page":"201","volume":"103","author":"E Zitzler","year":"2001","unstructured":"Zitzler E, Laumanns M, Thiele L (2001) Spea 2: improving the strength pareto evolutionary algorithm. TIK-Report 103:201","journal-title":"TIK-Report"},{"key":"9110_CR12","doi-asserted-by":"crossref","unstructured":"Menchaca-Mendez A, Coello CAC (2015) Gde-moea: A new moea based on the generational distance indicator and $$\\varepsilon $$-dominance. In: 2015 IEEE congress on evolutionary computation (CEC), pp 947\u2013955","DOI":"10.1109\/CEC.2015.7256992"},{"issue":"4","key":"9110_CR13","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1109\/TEVC.2014.2350987","volume":"19","author":"H Wang","year":"2015","unstructured":"Wang H, Jiao L, Yao X (2015) Two_arch2: an improved two-archive algorithm for many-objective optimization. IEEE Trans Evolut Computat 19(4):524\u2013541","journal-title":"IEEE Trans Evolut Computat"},{"issue":"6","key":"9110_CR14","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 (2007) Moea\/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 11(6):712\u2013731","journal-title":"IEEE Trans Evolut Comput"},{"issue":"5","key":"9110_CR15","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1109\/TEVC.2016.2519378","volume":"20","author":"R Cheng","year":"2016","unstructured":"Cheng R, Jin Y, Olhofer M, Sendhoff B (2016) A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans Evolut Computat 20(5):773\u2013791","journal-title":"IEEE Trans Evolut Computat"},{"key":"9110_CR16","doi-asserted-by":"crossref","unstructured":"Liang JJ, Yue CT, Qu BY (2016) Multimodal multi-objective optimization: A preliminary study. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 2454\u20132461","DOI":"10.1109\/CEC.2016.7744093"},{"issue":"1","key":"9110_CR17","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1109\/TEVC.2020.3008822","volume":"25","author":"Q Lin","year":"2021","unstructured":"Lin Q, Lin W, Zhu Z, Gong M, Li J, Coello CAC (2021) Multimodal multiobjective evolutionary optimization with dual clustering in decision and objective spaces. IEEE Trans Evolut Computat 25(1):130\u2013144","journal-title":"IEEE Trans Evolut Computat"},{"issue":"4","key":"9110_CR18","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1109\/TEVC.2021.3064508","volume":"25","author":"K Zhang","year":"2021","unstructured":"Zhang K, Shen C, Yen GG, Xu Z, He J (2021) Two-stage double niched evolution strategy for multimodal multiobjective optimization. IEEE Trans Evolut Computat 25(4):754\u2013768","journal-title":"IEEE Trans Evolut Computat"},{"issue":"6","key":"9110_CR19","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1109\/TEVC.2021.3078441","volume":"25","author":"W Li","year":"2021","unstructured":"Li W, Zhang T, Wang R, Ishibuchi H (2021) Weighted indicator-based evolutionary algorithm for multimodal multiobjective optimization. IEEE Trans Evolut Computat 25(6):1064\u20131078","journal-title":"IEEE Trans Evolut Computat"},{"issue":"1","key":"9110_CR20","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/TEVC.2022.3155757","volume":"27","author":"W Li","year":"2022","unstructured":"Li W, Yao X, Zhang T, Wang R, Wang L (2022) Hierarchy ranking method for multimodal multi-objective optimization with local pareto fronts. IEEE Trans Evolut Computat 27(1):98\u2013110","journal-title":"IEEE Trans Evolut Computat"},{"issue":"3","key":"9110_CR21","first-page":"551","volume":"24","author":"Y Liu","year":"2020","unstructured":"Liu Y, Ishibuchi H, Yen GG, Nojima Y, Masuyama N (2020) Handling imbalance between convergence and diversity in the decision space in evolutionary multimodal multiobjective optimization. IEEE Trans Evolut Computat 24(3):551\u2013565","journal-title":"IEEE Trans Evolut Computat"},{"key":"9110_CR22","unstructured":"Kulesza A, Taskar B (2011) k-dpps: fixed-size determinantal point processes. In: proceedings of the 28th international conference on machine learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011"},{"key":"9110_CR23","doi-asserted-by":"crossref","unstructured":"Gartrell M, Paquet U, Koenigstein N (2016) The bayesian low-rank determinantal point process mixture model. In: proceedings of the 10th ACM conference on recommender systems, pp. 349\u2013356","DOI":"10.1145\/2959100.2959178"},{"key":"9110_CR24","doi-asserted-by":"crossref","unstructured":"Tremblay N, Amblard P-O, Barthelm\u00e9 S (2017) Graph sampling with determinantal processes. In: 2017 25th European signal processing conference (EUSIPCO), pp. 1674\u20131678","DOI":"10.23919\/EUSIPCO.2017.8081494"},{"issue":"4","key":"9110_CR25","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1109\/TEVC.2018.2879406","volume":"23","author":"Y Liu","year":"2019","unstructured":"Liu Y, Yen GG, Gong D (2019) A multimodal multiobjective evolutionary algorithm using two-archive and recombination strategies. IEEE Trans Evolut Computat 23(4):660\u2013674","journal-title":"IEEE Trans Evolut Computat"},{"key":"9110_CR26","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/j.ins.2021.05.075","volume":"574","author":"Z Li","year":"2021","unstructured":"Li Z, Zou J, Yang S, Zheng J (2021) A two-archive algorithm with decomposition and fitness allocation for multi-modal multi-objective optimization. Inf Sci 574:413\u2013430","journal-title":"Inf Sci"},{"issue":"4","key":"9110_CR27","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1109\/TEVC.2019.2949841","volume":"24","author":"R Tanabe","year":"2020","unstructured":"Tanabe R, Ishibuchi H (2020) A framework to handle multimodal multiobjective optimization in decomposition-based evolutionary algorithms. IEEE Trans Evolut Computat 24(4):720\u2013734","journal-title":"IEEE Trans Evolut Computat"},{"key":"9110_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.100976","volume":"68","author":"W Wang","year":"2022","unstructured":"Wang W, Li G, Wang Y, Wu F, Zhang W, Li L (2022) Clearing-based multimodal multi-objective evolutionary optimization with layer-to-layer strategy. Swarm Evolut Computat 68:100976","journal-title":"Swarm Evolut Computat"},{"key":"9110_CR29","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.ins.2021.07.011","volume":"577","author":"G Li","year":"2021","unstructured":"Li G, Wang W, Zhang W, You W, Wu F, Tu H (2021) Handling multimodal multi-objective problems through self-organizing quantum-inspired particle swarm optimization. Inf Sci 577:510\u2013540","journal-title":"Inf Sci"},{"issue":"8","key":"9110_CR30","doi-asserted-by":"publisher","first-page":"4836","DOI":"10.1109\/TSMC.2019.2944338","volume":"51","author":"Q Fan","year":"2021","unstructured":"Fan Q, Yan X (2021) Solving multimodal multiobjective problems through zoning search. IEEE Trans Syst Man Cybern Syst 51(8):4836\u20134847","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"9110_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.100843","volume":"62","author":"G Li","year":"2021","unstructured":"Li G, Wang W, Zhang W, Wang Z, Tu H, You W (2021) Grid search based multi-population particle swarm optimization algorithm for multimodal multi-objective optimization. Swarm Evolut Computat 62:100843","journal-title":"Swarm Evolut Computat"},{"key":"9110_CR32","doi-asserted-by":"publisher","first-page":"2243","DOI":"10.1007\/s00521-019-04393-5","volume":"32","author":"G Maity","year":"2020","unstructured":"Maity G, Roy SK, Verdegay JL (2020) Analyzing multimodal transportation problem and its application to artificial intelligence. Neural Comput Appl 32:2243\u20132256","journal-title":"Neural Comput Appl"},{"issue":"2","key":"9110_CR33","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1109\/TEVC.2020.3035825","volume":"25","author":"P Zhang","year":"2021","unstructured":"Zhang P, Li J, Li T, Chen H (2021) A new many-objective evolutionary algorithm based on determinantal point processes. IEEE Trans Evolut Computat 25(2):334\u2013345","journal-title":"IEEE Trans Evolut Computat"},{"key":"9110_CR34","doi-asserted-by":"crossref","unstructured":"Liu Y, Ishibuchi H, Yen GG, Nojima Y, Masuyama N, Han Y (2020) On the normalization in evolutionary multi-modal multi-objective optimization. In: 2020 IEEE congress on evolutionary computation (CEC), pp. 1\u20138","DOI":"10.1109\/CEC48606.2020.9185899"},{"key":"9110_CR35","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 (2017) PlatEMO: a MATLAB platform for evolutionary multi-objective optimization. IEEE Computat Intell Magaz 12:73\u201387","journal-title":"IEEE Computat Intell Magaz"},{"key":"9110_CR36","first-page":"115","volume":"9","author":"R Agrawal","year":"2000","unstructured":"Agrawal R, Deb K, Agrawal R (2000) Simulated binary crossover for continuous search space. Complex Syst 9:115\u2013148","journal-title":"Complex Syst"},{"issue":"3","key":"9110_CR37","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1016\/j.ejor.2006.06.042","volume":"185","author":"K Deb","year":"2008","unstructured":"Deb K, Tiwari S (2008) Omni-optimizer: a generic evolutionary algorithm for single and multi-objective optimization. Europ J Operat Res 185(3):1062\u20131087","journal-title":"Europ J Operat Res"},{"issue":"5","key":"9110_CR38","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1109\/TEVC.2009.2021467","volume":"13","author":"A Zhou","year":"2009","unstructured":"Zhou A, Zhang Q, Jin Y (2009) Approximating the set of pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm. IEEE Trans Evolut Computat 13(5):1167\u20131189","journal-title":"IEEE Trans Evolut Computat"},{"issue":"4","key":"9110_CR39","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/4235.797969","volume":"3","author":"E Zitzler","year":"1999","unstructured":"Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evolut Computat 3(4):257\u2013271","journal-title":"IEEE Trans Evolut Computat"},{"issue":"3","key":"9110_CR40","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/s00500-008-0323-y","volume":"13","author":"J Alcal\u00e1-Fdez","year":"2009","unstructured":"Alcal\u00e1-Fdez J, S\u00e1nchez L, Garc\u00eda S, del Jesus MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, Fern\u00e1ndez JC, Herrera F (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307\u2013318","journal-title":"Soft Comput"},{"issue":"1","key":"9110_CR41","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TEVC.2020.3011829","volume":"25","author":"Y Zhou","year":"2021","unstructured":"Zhou Y, Xiang Y, He X (2021) Constrained multiobjective optimization: test problem construction and performance evaluations. IEEE Trans Evolut Computat 25(1):172\u2013186","journal-title":"IEEE Trans Evolut Computat"},{"issue":"1","key":"9110_CR42","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/MCI.2021.3129961","volume":"17","author":"H Ishibuchi","year":"2022","unstructured":"Ishibuchi H, Pang LM, Shang K (2022) Difficulties in fair performance comparison of multi-objective evolutionary algorithms [research frontier]. IEEE Computat Intell Magaz 17(1):86\u2013101","journal-title":"IEEE Computat Intell Magaz"},{"issue":"4","key":"9110_CR43","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1109\/TEVC.2022.3194253","volume":"27","author":"J Liang","year":"2023","unstructured":"Liang J, Lin H, Yue C, Yu K, Guo Y, Qiao K (2023) Multiobjective differential evolution with speciation for constrained multimodal multiobjective optimization. IEEE Trans Evolut Computat 27(4):1115\u20131129","journal-title":"IEEE Trans Evolut Computat"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09110-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09110-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09110-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T08:11:12Z","timestamp":1704442272000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09110-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,8]]},"references-count":43,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["9110"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09110-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2023,11,8]]},"assertion":[{"value":"8 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2023","order":3,"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 conflicts of interest to this work. The article contains no violation of any existing copyright or other third party right.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}