{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T06:59:30Z","timestamp":1777618770502,"version":"3.51.4"},"reference-count":103,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T00:00:00Z","timestamp":1752278400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T00:00:00Z","timestamp":1752278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62105064"],"award-info":[{"award-number":["No. 62105064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62105064"],"award-info":[{"award-number":["No. 62105064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. U2239205"],"award-info":[{"award-number":["No. U2239205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"DOI":"10.1007\/s10462-025-11239-1","type":"journal-article","created":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T03:55:02Z","timestamp":1752292502000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Fractional order dung beetle optimizer with reduction factor for global optimization and industrial engineering optimization problems"],"prefix":"10.1007","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8712-1982","authenticated-orcid":false,"given":"Huangzhi","family":"Xia","sequence":"first","affiliation":[]},{"given":"Yifen","family":"Ke","sequence":"additional","affiliation":[]},{"given":"Riwei","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Huai","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,12]]},"reference":[{"key":"11239_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110454","volume":"268","author":"M Abdel","year":"2023","unstructured":"Abdel M, Mohamed R, Azeem S et al (2023) Kepler optimization algorithm: a new metaheuristic algorithm inspired by Kepler\u2019s laws of planetary motion. Knowl-Based Syst 268:110454. https:\/\/doi.org\/10.1016\/j.knosys.2023.110454","journal-title":"Knowl-Based Syst"},{"issue":"11","key":"11239_CR2","doi-asserted-by":"publisher","first-page":"3827","DOI":"10.3390\/app10113827","volume":"10","author":"L Abualigah","year":"2020","unstructured":"Abualigah L, Diabat A, Geem Z (2020) A comprehensive survey of the harmony search algorithm in clustering applications. Appl Sci 10(11):3827. https:\/\/doi.org\/10.3390\/app10113827","journal-title":"Appl Sci"},{"key":"11239_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2020.113609","volume":"376","author":"L Abualigah","year":"2021","unstructured":"Abualigah L, Diabat A, Mirjalili S et al (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609. https:\/\/doi.org\/10.1016\/j.cma.2020.113609","journal-title":"Comput Methods Appl Mech Eng"},{"issue":"11","key":"11239_CR4","doi-asserted-by":"publisher","first-page":"8823","DOI":"10.1007\/s00521-022-06906-1","volume":"34","author":"L Abualigah","year":"2022","unstructured":"Abualigah L, Ewees A, Al-qaness M et al (2022) Boosting arithmetic optimization algorithm by sine cosine algorithm and levy flight distribution for solving engineering optimization problems. Neural Comput Appl 34(11):8823\u20138852. https:\/\/doi.org\/10.1007\/s00521-022-06906-1","journal-title":"Neural Comput Appl"},{"issue":"6","key":"11239_CR5","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1007\/s10845-022-01921-4","volume":"34","author":"L Abualigah","year":"2023","unstructured":"Abualigah L, Diabat A, Svetinovic D et al (2023) Boosted harris hawks gravitational force algorithm for global optimization and industrial engineering problems. J Intell Manuf 34(6):2693\u20132728. https:\/\/doi.org\/10.1007\/s10845-022-01921-4","journal-title":"J Intell Manuf"},{"issue":"2","key":"11239_CR6","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.1016\/j.aej.2021.06.019","volume":"61","author":"W Ahmed","year":"2022","unstructured":"Ahmed W, Mageed H, Mohamed S et al (2022) Fractional order darwinian particle swarm optimization for parameters identification of solar pv cells and modules. Alex Eng J 61(2):1249\u20131263. https:\/\/doi.org\/10.1016\/j.aej.2021.06.019","journal-title":"Alexandria Eng J"},{"issue":"5","key":"11239_CR7","doi-asserted-by":"publisher","first-page":"3979","DOI":"10.1007\/s10462-021-10100-5","volume":"55","author":"O Altay","year":"2022","unstructured":"Altay O (2022) Chaotic slime mould optimization algorithm for global optimization. Artif Intell Rev 55(5):3979\u20134040. https:\/\/doi.org\/10.1007\/s10462-021-10100-5","journal-title":"Artif Intell Rev"},{"issue":"3","key":"11239_CR8","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1061\/(ASCE)0733-9445(1989)115:3(626)","volume":"115","author":"H Amir","year":"1989","unstructured":"Amir H, Hasegawa T (1989) Nonlinear mixed-discrete structural optimization. J Struct Eng 115(3):626\u2013646. https:\/\/doi.org\/10.1061\/(ASCE)0733-9445(1989)115:3(626)","journal-title":"J Struct Eng"},{"key":"11239_CR9","doi-asserted-by":"crossref","unstructured":"Arora J (2004) Introduction to optimum design. Elsevier. https:\/\/books.google.ru\/books?id=9FbwVe577xwC","DOI":"10.1016\/B978-012064155-0\/50012-4"},{"issue":"3","key":"11239_CR10","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1007\/s00500-018-3102-4","volume":"23","author":"S Arora","year":"2018","unstructured":"Arora S, Singh S (2018) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23(3):715\u2013734. https:\/\/doi.org\/10.1007\/s00500-018-3102-4","journal-title":"Soft Comput"},{"key":"11239_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122335","volume":"241","author":"S Barua","year":"2024","unstructured":"Barua S, Merabet A (2024) L\u00e9vy arithmetic algorithm: an enhanced metaheuristic algorithm and its application to engineering optimization. Expert Syst Appl 241:122335. https:\/\/doi.org\/10.1016\/j.eswa.2023.122335","journal-title":"Expert Syst Appl"},{"key":"11239_CR12","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1007\/978-3-642-58069-7_38","volume":"102","author":"G Beni","year":"1993","unstructured":"Beni G, Wang J (1993) Swarm intelligence in cellular robotic systems. Robots Biol Syst: Towards New Bionics? 102:703\u2013712. https:\/\/doi.org\/10.1007\/978-3-642-58069-7_38","journal-title":"Robots Biol Syst: Towards New Bionics?"},{"issue":"1","key":"11239_CR13","doi-asserted-by":"publisher","first-page":"13971","DOI":"10.1038\/s41598-019-50262-5","volume":"9","author":"X Bui","year":"2019","unstructured":"Bui X, Jaroonpattanapong P, Nguyen H et al (2019) A novel hybrid model for predicting blast-induced ground vibration based on $$k$$-nearest neighbors and particle swarm optimization. Sci Rep 9(1):13971. https:\/\/doi.org\/10.1038\/s41598-019-50262-5","journal-title":"Sci Rep"},{"key":"11239_CR14","doi-asserted-by":"publisher","unstructured":"Cameron C, Hartford J, Lundy T, et\u00a0al (2022) The perils of learning before optimizing. In: Proc of the AAAI Conference on Artificial Intelligence, pp 3708\u20133715. https:\/\/doi.org\/10.1609\/aaai.v36i4.20284","DOI":"10.1609\/aaai.v36i4.20284"},{"issue":"7","key":"11239_CR15","doi-asserted-by":"publisher","first-page":"7793","DOI":"10.1007\/s12652-020-02506-w","volume":"12","author":"R Chakraborty","year":"2020","unstructured":"Chakraborty R, Verma G, Namasudra S (2020) Ifodpso-based multi-level image segmentation scheme aided with masi entropy. J Ambient Intell Humaniz Comput 12(7):7793\u20137811. https:\/\/doi.org\/10.1007\/s12652-020-02506-w","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"1","key":"11239_CR16","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1002\/int.22617","volume":"37","author":"S Chakraborty","year":"2022","unstructured":"Chakraborty S, Saha A, Chakraborty R et al (2022) Hswoa: an ensemble of hunger games search and whale optimization algorithm for global optimization. Int J Intell Syst 37(1):52\u2013104. https:\/\/doi.org\/10.1002\/int.22617","journal-title":"Int J Intell Syst"},{"key":"11239_CR17","doi-asserted-by":"publisher","unstructured":"Chang C, Ding T, Ee C, et\u00a0al (2024) Nature-inspired heuristic frameworks trends in solving multi-objective engineering optimization problems. Arch Comput Methods Eng31:3551\u20133584. https:\/\/doi.org\/10.1007\/s11831-024-10090-x","DOI":"10.1007\/s11831-024-10090-x"},{"issue":"22","key":"11239_CR18","doi-asserted-by":"publisher","first-page":"26949","DOI":"10.1007\/s10489-023-04969-8","volume":"53","author":"L Chen","year":"2023","unstructured":"Chen L, Gao J, Lopes A et al (2023) Adaptive fractional-order genetic-particle swarm optimization otsu algorithm for image segmentation. Appl Intell 53(22):26949\u201326966. https:\/\/doi.org\/10.1007\/s10489-023-04969-8","journal-title":"Appl Intell"},{"key":"11239_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120027","volume":"224","author":"Z Cheng","year":"2023","unstructured":"Cheng Z, Song H, Zheng D et al (2023) Hybrid firefly algorithm with a new mechanism of gender distinguishing for global optimization. Expert Syst Appl 224:120027. https:\/\/doi.org\/10.1016\/j.eswa.2023.120027","journal-title":"Expert Syst Appl"},{"key":"11239_CR20","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neunet.2022.09.034","volume":"157","author":"J Ci","year":"2022","unstructured":"Ci J, Guo Z, Long H et al (2022) Multiple asymptotical\u2013periodicity of fractional-order delayed neural networks under state-dependent switching. Neural Netw: Off J Int Neural Netw Soc 157:11\u201325. https:\/\/doi.org\/10.1016\/j.neunet.2022.09.034","journal-title":"Neural Netw: Off J Int Neural Netw Soc"},{"issue":"2","key":"11239_CR21","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/S0166-3615(99)00046-9","volume":"41","author":"C Coello","year":"2000","unstructured":"Coello C (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113\u2013127. https:\/\/doi.org\/10.1016\/S0166-3615(99)00046-9","journal-title":"Comput Ind"},{"key":"11239_CR22","unstructured":"Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. In: Proc of the First European Conference on Artificial Life, pp 134\u2013142. https:\/\/www.researchgate.net\/publication\/216300484"},{"issue":"3","key":"11239_CR23","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/s11760-012-0316-2","volume":"6","author":"M Couceiro","year":"2012","unstructured":"Couceiro M, Rocha R, Fonseca N et al (2012) Introducing the fractional-order darwinian pso. SIViP 6(3):343\u2013350. https:\/\/doi.org\/10.1007\/s11760-012-0316-2","journal-title":"SIViP"},{"issue":"1","key":"11239_CR24","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ejor.2022.11.007","volume":"306","author":"Y Cui","year":"2023","unstructured":"Cui Y, Hu W, Rahmani A (2023) Fractional-order artificial bee colony algorithm with application in robot path planning. Eur J Oper Res 306(1):47\u201364. https:\/\/doi.org\/10.1016\/j.ejor.2022.11.007","journal-title":"Eur J Oper Res"},{"key":"11239_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110011","volume":"259","author":"M Dehghani","year":"2023","unstructured":"Dehghani M, Montazeri Z, Trojovsk\u00e1 E et al (2023) Coati optimization algorithm: a new bio-inspired metaheuristic algorithm for solving optimization problems. Knowl-Based Syst 259:110011. https:\/\/doi.org\/10.1016\/j.knosys.2022.110011","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"11239_CR26","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.swevo.2011.02.002","volume":"1","author":"J Derrac","year":"2011","unstructured":"Derrac J, Garc\u00eda S, Molina D et al (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3\u201318. https:\/\/doi.org\/10.1016\/j.swevo.2011.02.002","journal-title":"Swarm Evol Comput"},{"issue":"2","key":"11239_CR27","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.1007\/s00366-021-01487-4","volume":"39","author":"D Dhawale","year":"2023","unstructured":"Dhawale D, Kamboj V, Anand P (2023) An improved chaotic Harris Hawks optimizer for solving numerical and engineering optimization problems. Eng Comput 39(2):1183\u20131228. https:\/\/doi.org\/10.1007\/s00366-021-01487-4","journal-title":"Eng Comput"},{"key":"11239_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108733","volume":"121","author":"Y Dong","year":"2022","unstructured":"Dong Y, Zhang H, Wang C et al (2022) An adaptive state transition algorithm with local enhancement for global optimization. Appl Soft Comput 121:108733. https:\/\/doi.org\/10.1016\/j.asoc.2022.108733","journal-title":"Appl Soft Comput"},{"key":"11239_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119017","volume":"213","author":"Y Duan","year":"2023","unstructured":"Duan Y, Yu X (2023) A collaboration-based hybrid gwo-sca optimizer for engineering optimization problems. Expert Syst Appl 213:119017. https:\/\/doi.org\/10.1016\/j.eswa.2022.119017","journal-title":"Expert Syst Appl"},{"issue":"Part","key":"11239_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107976","volume":"113","author":"T Dutta","year":"2021","unstructured":"Dutta T, Dey S, Bhattacharyya S et al (2021) Quantum fractional order Darwinian particle swarm optimization for hyperspectral multi-level image thresholding. Appl Soft Comput 113:107976. https:\/\/doi.org\/10.1016\/j.asoc.2021.107976","journal-title":"Appl Soft Comput"},{"key":"11239_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122147","volume":"238","author":"E El-kenawy","year":"2024","unstructured":"El-kenawy E, Khodadadi N, Mirjalili S et al (2024) Greylag goose optimization: nature-inspired optimization algorithm. Expert Syst Appl 238:122147. https:\/\/doi.org\/10.1016\/j.eswa.2023.122147","journal-title":"Expert Syst Appl"},{"key":"11239_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106734","volume":"98","author":"Z Feng","year":"2021","unstructured":"Feng Z, Niu W, Liu S (2021) Cooperation search algorithm: a novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems. Appl Soft Comput 98:106734. https:\/\/doi.org\/10.1016\/j.asoc.2020.106734","journal-title":"Appl Soft Comput"},{"key":"11239_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108562","volume":"119","author":"Z Feng","year":"2022","unstructured":"Feng Z, Duan J, Niu W et al (2022) Enhanced sine cosine algorithm using opposition learning, adaptive evolution and neighborhood search strategies for multivariable parameter optimization problems. Appl Soft Comput 119:108562. https:\/\/doi.org\/10.1016\/j.asoc.2022.108562","journal-title":"Appl Soft Comput"},{"issue":"1","key":"11239_CR34","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1007\/s42235-023-00437-8","volume":"21","author":"M Ghasemi","year":"2024","unstructured":"Ghasemi M, Zare M, Zahedi A et al (2024) Geyser inspired algorithm: a new geological-inspired meta-heuristic for real-parameter and constrained engineering optimization. J Bionic Eng 21(1):374\u2013408. https:\/\/doi.org\/10.1007\/s42235-023-00437-8","journal-title":"J Bionic Eng"},{"key":"11239_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111850","volume":"295","author":"M Ghasemi","year":"2024","unstructured":"Ghasemi M, Zare M, Trojovsk\u1ef3 P et al (2024) Optimization based on the smart behavior of plants with its engineering applications: Ivy algorithm. Knowl-Based Syst 295:111850. https:\/\/doi.org\/10.1016\/j.knosys.2024.111850","journal-title":"Knowl-Based Syst"},{"key":"11239_CR36","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.matcom.2021.08.013","volume":"192","author":"F Hashim","year":"2022","unstructured":"Hashim F, Houssein E, Hussain K et al (2022) Honey badger algorithm: new metaheuristic algorithm for solving optimization problems. Math Comput Simul 192:84\u2013110. https:\/\/doi.org\/10.1016\/j.matcom.2021.08.013","journal-title":"Math Comput Simul"},{"key":"11239_CR37","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"A Heidari","year":"2019","unstructured":"Heidari A, Mirjalili S, Faris H et al (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849\u2013872. https:\/\/doi.org\/10.1016\/j.future.2019.02.028","journal-title":"Future Gener Comput Syst"},{"issue":"1","key":"11239_CR38","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"J Holland","year":"1992","unstructured":"Holland J (1992) Genetic algorithms. Sci Am 267(1):66\u201373","journal-title":"Sci Am"},{"key":"11239_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2022.114901","volume":"394","author":"G Hu","year":"2022","unstructured":"Hu G, Zhong J, Du B et al (2022) An enhanced hybrid arithmetic optimization algorithm for engineering applications. Comput Methods Appl Mech Eng 394:114901. https:\/\/doi.org\/10.1016\/j.cma.2022.114901","journal-title":"Comput Methods Appl Mech Eng"},{"key":"11239_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2022.115676","volume":"403","author":"G Hu","year":"2023","unstructured":"Hu G, Yang R, Qin X et al (2023) Mcsa: Multi-strategy boosted chameleon-inspired optimization algorithm for engineering applications. Comput Methods Appl Mech Eng 403:115676. https:\/\/doi.org\/10.1016\/j.cma.2022.115676","journal-title":"Comput Methods Appl Mech Eng"},{"key":"11239_CR41","doi-asserted-by":"publisher","unstructured":"Hussien A, Amin M (2022) A self-adaptive harris hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection. Int J Mach Learn Cybernet 13(2):309\u2013336. https:\/\/doi.org\/10.1007\/s13042-021-01326-4","DOI":"10.1007\/s13042-021-01326-4"},{"key":"11239_CR42","doi-asserted-by":"publisher","unstructured":"Jia H, Wen Q, Wang Y, et\u00a0al (2024) Catch fish optimization algorithm: a new human behavior algorithm for solving clustering problems. Clust Comput 27(9):13295\u201313332. https:\/\/doi.org\/10.1007\/s10586-024-04618-w","DOI":"10.1007\/s10586-024-04618-w"},{"issue":"2","key":"11239_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10915-023-02157-x","volume":"95","author":"Y Ke","year":"2023","unstructured":"Ke Y, Ma C, Jia Z et al (2023) Quasi non-negative quaternion matrix factorization with application to color face recognition. J Sci Comput 95(2):1\u201333. https:\/\/doi.org\/10.1007\/s10915-023-02157-x","journal-title":"J Sci Comput"},{"key":"11239_CR44","doi-asserted-by":"publisher","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proc of the ICNN95-International Conference on Neural Networks, pp 1942\u20131948. https:\/\/doi.org\/10.1109\/ICNN.1995.488968","DOI":"10.1109\/ICNN.1995.488968"},{"key":"11239_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113338","volume":"149","author":"M Khishe","year":"2020","unstructured":"Khishe M, Reza M (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338. https:\/\/doi.org\/10.1016\/j.eswa.2020.113338","journal-title":"Expert Syst Appl"},{"issue":"7","key":"11239_CR46","doi-asserted-by":"publisher","first-page":"3900","DOI":"10.1002\/int.22707","volume":"37","author":"T Kundu","year":"2022","unstructured":"Kundu T, Garg H (2022) A hybrid itlhho algorithm for numerical and engineering optimization problems. Int J Intell Syst 37(7):3900\u20133980. https:\/\/doi.org\/10.1002\/int.22707","journal-title":"Int J Intell Syst"},{"key":"11239_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106933","volume":"98","author":"Y Li","year":"2021","unstructured":"Li Y, Zhao Y, Liu J (2021) Dimension by dimension dynamic sine cosine algorithm for global optimization problems. Appl Soft Comput 98:106933. https:\/\/doi.org\/10.1016\/j.asoc.2020.106933","journal-title":"Appl Soft Comput"},{"issue":"13","key":"11239_CR48","doi-asserted-by":"publisher","first-page":"16663","DOI":"10.1007\/s10489-022-04132-9","volume":"53","author":"M Li","year":"2022","unstructured":"Li M, Xu G, Zeng L et al (2022) Hybrid whale optimization algorithm based on symbiosis strategy for global optimization. Appl Intell 53(13):16663\u201316705. https:\/\/doi.org\/10.1007\/s10489-022-04132-9","journal-title":"Appl Intell"},{"issue":"6","key":"11239_CR49","doi-asserted-by":"publisher","first-page":"6133","DOI":"10.1007\/s10489-022-03743-6","volume":"53","author":"W Li","year":"2022","unstructured":"Li W, Shi R, Dong J (2022) Harris hawks optimizer based on the novice protection tournament for numerical and engineering optimization problems. Appl Intell 53(6):6133\u20136158. https:\/\/doi.org\/10.1007\/s10489-022-03743-6","journal-title":"Appl Intell"},{"key":"11239_CR50","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1016\/j.matcom.2022.08.020","volume":"204","author":"Y Li","year":"2023","unstructured":"Li Y, Yu X, Liu J (2023) An opposition-based butterfly optimization algorithm with adaptive elite mutation in solving complex high-dimensional optimization problems. Math Comput Simul 204:498\u2013528. https:\/\/doi.org\/10.1016\/j.matcom.2022.08.020","journal-title":"Math Comput Simul"},{"key":"11239_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.129604","volume":"286","author":"Y Li","year":"2024","unstructured":"Li Y, Sun K, Yao Q et al (2024) A dual-optimization wind speed forecasting model based on deep learning and improved dung beetle optimization algorithm. Energy 286:129604. https:\/\/doi.org\/10.1016\/j.energy.2023.129604","journal-title":"Energy"},{"key":"11239_CR52","doi-asserted-by":"publisher","unstructured":"Lian J, Zhu T, Ma L, et\u00a0al (2024) The educational competition optimizer. Int J Syst Sci 55(15):3185\u20133222. https:\/\/doi.org\/10.1080\/00207721.2024.2367079","DOI":"10.1080\/00207721.2024.2367079"},{"key":"11239_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2023.101282","volume":"78","author":"D Liu","year":"2023","unstructured":"Liu D, He H, Yang Q et al (2023) Function value ranking aware differential evolution for global numerical optimization. Swarm Evol Comput 78:101282. https:\/\/doi.org\/10.1016\/j.swevo.2023.101282","journal-title":"Swarm Evol Comput"},{"key":"11239_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104960","volume":"113","author":"G Ma","year":"2022","unstructured":"Ma G, Yue X (2022) An improved whale optimization algorithm based on multilevel threshold image segmentation using the otsu method. Eng Appl Artif Intell 113:104960. https:\/\/doi.org\/10.1016\/j.engappai.2022.104960","journal-title":"Eng Appl Artif Intell"},{"key":"11239_CR56","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1007\/978-3-319-11857-4_10","volume":"8794","author":"X Meng","year":"2014","unstructured":"Meng X, Liu Y, Gao X et al (2014) A new bio-inspired algorithm: chicken swarm optimization. Lect Notes Comput Sci 8794:86\u201394. https:\/\/doi.org\/10.1007\/978-3-319-11857-4_10","journal-title":"Lect Notes Comput Sci"},{"issue":"6","key":"11239_CR57","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1063\/1.1699114","volume":"21","author":"N Metropolis","year":"1953","unstructured":"Metropolis N, Rosenbluth A, Rosenbluth M et al (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087\u20131092. https:\/\/doi.org\/10.1063\/1.1699114","journal-title":"J Chem Phys"},{"key":"11239_CR58","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.knosys.2015.12.022","volume":"96","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S (2016) Sca: A sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120\u2013133. https:\/\/doi.org\/10.1016\/j.knosys.2015.12.022","journal-title":"Knowl-Based Syst"},{"issue":"C","key":"11239_CR59","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367. https:\/\/doi.org\/10.1016\/j.advengsoft.2016.01.008","journal-title":"Adv Eng Softw"},{"issue":"C","key":"11239_CR60","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.eswa.2016.03.047","volume":"57","author":"E Nabil","year":"2016","unstructured":"Nabil E (2016) A modified flower pollination algorithm for global optimization. Expert Syst Appl 57:192\u2013203. https:\/\/doi.org\/10.1016\/j.eswa.2016.03.047","journal-title":"Expert Syst Appl"},{"issue":"7","key":"11239_CR61","doi-asserted-by":"publisher","first-page":"6101","DOI":"10.1007\/s10462-022-10328-9","volume":"56","author":"J Pan","year":"2023","unstructured":"Pan J, Hu P, Sn\u00e1\u0161el V et al (2023) A survey on binary metaheuristic algorithms and their engineering applications. Artif Intell Rev 56(7):6101\u20136167. https:\/\/doi.org\/10.1007\/s10462-022-10328-9","journal-title":"Artif Intell Rev"},{"issue":"1\u20132","key":"11239_CR62","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s11071-009-9649-y","volume":"61","author":"E Pires","year":"2010","unstructured":"Pires E, Machado J, Oliveira P et al (2010) Particle swarm optimization with fractional-order velocity. Nonlinear Dyn 61:295\u2013301. https:\/\/doi.org\/10.1007\/s11071-009-9649-y","journal-title":"Nonlinear Dyn"},{"key":"11239_CR63","doi-asserted-by":"publisher","unstructured":"Rajwar K, Deep K, Das S (2023) An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges. Artif Intell Rev 56(11):13187\u201313257. https:\/\/doi.org\/10.1007\/s10462-023-10470-y","DOI":"10.1007\/s10462-023-10470-y"},{"issue":"13","key":"11239_CR64","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi H, Saryazdi S (2009) Gsa: a gravitational search algorithm. Inf Sci 179(13):2232\u20132248. https:\/\/doi.org\/10.1016\/j.ins.2009.03.004","journal-title":"Inf Sci"},{"key":"11239_CR65","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.engappai.2019.01.001","volume":"80","author":"S Shadravan","year":"2019","unstructured":"Shadravan S, Naji H, Bardsiri V (2019) The sailfish optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell 80:20\u201334. https:\/\/doi.org\/10.1016\/j.engappai.2019.01.001","journal-title":"Eng Appl Artif Intell"},{"key":"11239_CR66","doi-asserted-by":"publisher","unstructured":"Souza M (2021) Automatic design of heuristic algorithms for binary optimization problems. In: Proc of the International Joint Conference on Artificial Intelligence, pp 4881\u20134882. https:\/\/doi.org\/10.24963\/ijcai.2021\/672","DOI":"10.24963\/ijcai.2021\/672"},{"key":"11239_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107532","volume":"128","author":"R Sowmya","year":"2024","unstructured":"Sowmya R, Premkumar M, Jangir P (2024) Newton-raphson-based optimizer: a new population-based metaheuristic algorithm for continuous optimization problems. Eng Appl Artif Intell 128:107532. https:\/\/doi.org\/10.1016\/j.engappai.2023.107532","journal-title":"Eng Appl Artif Intell"},{"issue":"4","key":"11239_CR68","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-a simple and efficient heuristic for global optimization over continuous spaces. Adv Bioinform 11(4):341\u2013359. https:\/\/doi.org\/10.1023\/A:1008202821328","journal-title":"Adv Bioinform"},{"key":"11239_CR69","doi-asserted-by":"publisher","unstructured":"Tang C, Zhou Y, Luo Q, et\u00a0al (2021) An enhanced pathfinder algorithm for engineering optimization problems. Eng Comput 38:1481\u20131503. https:\/\/doi.org\/10.1007\/s00366-021-01286-x","DOI":"10.1007\/s00366-021-01286-x"},{"key":"11239_CR70","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1016\/j.apm.2023.10.045","volume":"126","author":"A Tian","year":"2024","unstructured":"Tian A, Liu F, Lv H (2024) Snow geese algorithm: a novel migration-inspired meta-heuristic algorithm for constrained engineering optimization problems. Appl Math Model 126:327\u2013347. https:\/\/doi.org\/10.1016\/j.apm.2023.10.045","journal-title":"Appl Math Model"},{"issue":"2","key":"11239_CR71","doi-asserted-by":"publisher","first-page":"149","DOI":"10.3390\/biomimetics8020149","volume":"8","author":"P Trojovsky","year":"2023","unstructured":"Trojovsky P, Dehghani M (2023) Subtraction-average-based optimizer: a new swarm-inspired metaheuristic algorithm for solving optimization problems. Biomimetics 8(2):149. https:\/\/doi.org\/10.3390\/biomimetics8020149","journal-title":"Biomimetics (Basel, Switzerland)"},{"issue":"19","key":"11239_CR72","doi-asserted-by":"publisher","first-page":"14275","DOI":"10.1007\/s00521-023-08481-5","volume":"35","author":"O Turgut","year":"2023","unstructured":"Turgut O, Turgut M, Kirtepe E (2023) A systematic review of the emerging metaheuristic algorithms on solving complex optimization problems. Neural Comput Appl 35(19):14275\u201314378. https:\/\/doi.org\/10.1007\/s00521-023-08481-5","journal-title":"Neural Comput Appl"},{"issue":"7","key":"11239_CR73","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-022-10352-9","volume":"56","author":"J Wang","year":"2022","unstructured":"Wang J, Zhu S (2022) A multi-factor two-stage deep integration model for stock price prediction based on intelligent optimization and feature clustering. Artif Intell Rev 56(7):1\u201326. https:\/\/doi.org\/10.1007\/s10462-022-10352-9","journal-title":"Artif Intell Rev"},{"key":"11239_CR74","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ultras.2018.06.012","volume":"92","author":"Y Wang","year":"2019","unstructured":"Wang Y, Peng W, Qiu C et al (2019) Fractional-order darwinian pso-based feature selection for media-adventitia border detection in intravascular ultrasound images. Ultrasonics 92:1\u20137. https:\/\/doi.org\/10.1016\/j.ultras.2018.06.012","journal-title":"Ultrasonics"},{"key":"11239_CR75","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105082","volume":"114","author":"L Wang","year":"2022","unstructured":"Wang L, Cao Q, Zhang Z et al (2022) Artificial rabbits optimization: a new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 114:105082. https:\/\/doi.org\/10.1016\/j.engappai.2022.105082","journal-title":"Eng Appl Artif Intell"},{"key":"11239_CR76","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.aej.2023.09.042","volume":"81","author":"Z Wang","year":"2023","unstructured":"Wang Z, Huang L, Yang S et al (2023) A quasi-oppositional learning of updating quantum state and q-learning based on the dung beetle algorithm for global optimization. Alex Eng J 81:469\u2013488. https:\/\/doi.org\/10.1016\/j.aej.2023.09.042","journal-title":"Alex Eng J"},{"key":"11239_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2024.103694","volume":"195","author":"W Wang","year":"2024","unstructured":"Wang W, Tian W, Xu D et al (2024) Arctic puffin optimization: a bio-inspired metaheuristic algorithm for solving engineering design optimization. Adv Eng Softw 195:103694. https:\/\/doi.org\/10.1016\/j.advengsoft.2024.103694","journal-title":"Adv Eng Softw"},{"key":"11239_CR78","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109917","volume":"133","author":"D Wei","year":"2023","unstructured":"Wei D, Wang H, Dai J et al (2023) Dynamic chaotic gold-panning optimizer and its typical engineering applications. Appl Soft Comput 133:109917. https:\/\/doi.org\/10.1016\/j.asoc.2022.109917","journal-title":"Appl Soft Comput"},{"issue":"6","key":"11239_CR79","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1007\/978-1-4612-4380-9_16","volume":"1","author":"F Wilcoxon","year":"1944","unstructured":"Wilcoxon F (1944) Individual comparisons by ranking methods. Biometrics 1(6):196. https:\/\/doi.org\/10.1007\/978-1-4612-4380-9_16","journal-title":"Biometrics"},{"issue":"1","key":"11239_CR80","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"D Wolpert","year":"1997","unstructured":"Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67\u201382. https:\/\/doi.org\/10.1109\/4235.585893","journal-title":"IEEE Trans Evol Comput"},{"key":"11239_CR81","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1016\/j.ins.2015.09.051","volume":"329","author":"G Wu","year":"2016","unstructured":"Wu G (2016) Across neighbourhood search for numerical optimization. Inf Sci 329:597\u2013618. https:\/\/doi.org\/10.1016\/j.ins.2015.09.051","journal-title":"Inf Sci"},{"key":"11239_CR82","doi-asserted-by":"publisher","unstructured":"Wu X, Li S, Jiang X, et\u00a0al (2024) Information acquisition optimizer: a new efficient algorithm for solving numerical and constrained engineering optimization problems. J Supercomput 80(18):25736\u201325791. https:\/\/doi.org\/10.1007\/s11227-024-06384-3","DOI":"10.1007\/s11227-024-06384-3"},{"key":"11239_CR54","doi-asserted-by":"publisher","unstructured":"Xiao C, Cai Z, Wang Y (2007) A good nodes set evolution strategy for constrained optimization. In: 2007 IEEE congress on evolutionary computation. IEEE, pp 943\u2013950. https:\/\/doi.org\/10.1109\/CHICC.2006.4346961","DOI":"10.1109\/CHICC.2006.4346961"},{"issue":"1","key":"11239_CR83","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","volume":"8","author":"J Xue","year":"2020","unstructured":"Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control Eng 8(1):22\u201334. https:\/\/doi.org\/10.1080\/21642583.2019.1708830","journal-title":"Syst Sci Control Eng"},{"issue":"7","key":"11239_CR84","doi-asserted-by":"publisher","first-page":"7305","DOI":"10.1007\/s11227-022-04959-6","volume":"79","author":"J Xue","year":"2022","unstructured":"Xue J, Shen B (2023) Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. J Supercomput 79(7):7305\u20137336. https:\/\/doi.org\/10.1007\/s11227-022-04959-6","journal-title":"J Supercomput"},{"issue":"8","key":"11239_CR85","doi-asserted-by":"publisher","first-page":"3787","DOI":"10.1007\/s11665-023-08871-9","volume":"33","author":"H Xue","year":"2024","unstructured":"Xue H, Li T, Li J et al (2024) Multi-objective optimization for turning process of 304 stainless steel based on dung beetle optimizer-back propagation neural network and improved particle swarm optimization. J Mater Eng Perform 33(8):3787\u20133800. https:\/\/doi.org\/10.1007\/s11665-023-08871-9","journal-title":"J Mater Eng Perform"},{"key":"11239_CR103","doi-asserted-by":"publisher","unstructured":"Xia H, Chen L, Xu H (2025a) Multi-strategy dung beetle optimizer for global optimization and feature selection. Int J Mach Learn Cybernet 16(1):189\u2013231. https:\/\/doi.org\/10.1007\/s13042-024-02197-1","DOI":"10.1007\/s13042-024-02197-1"},{"key":"11239_CR102","doi-asserted-by":"publisher","unstructured":"Xia H, Ke Y, Liao R et al (2025b) Fractional order calculus enhanced dung beetle optimizer for function global optimization and multilevel threshold medical image segmentation. J Supercomput 81(1):90. https:\/\/doi.org\/10.1007\/s11227-024-06592-x","DOI":"10.1007\/s11227-024-06592-x"},{"issue":"Pt 2","key":"11239_CR86","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemosphere.2022.136614","volume":"309","author":"J Yan","year":"2022","unstructured":"Yan J, Li G, Qi G et al (2022) Improved feed forward with bald eagle search for conjunctive water management in deficit region. Chemosphere 309:136614. https:\/\/doi.org\/10.1016\/j.chemosphere.2022.136614","journal-title":"Chemosphere"},{"key":"11239_CR87","doi-asserted-by":"publisher","unstructured":"Yang X (2010) Firefly algorithms for multimodal optimization. In: Proc of the International Conference on Stochastic Algorithms: Foundations and Applications 5792:169\u2013178. https:\/\/doi.org\/10.1007\/978-3-642-04944-6_14","DOI":"10.1007\/978-3-642-04944-6_14"},{"key":"11239_CR88","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104558","volume":"108","author":"W Yang","year":"2022","unstructured":"Yang W, Xia K, Fan S et al (2022) A multi-strategy whale optimization algorithm and its application. Eng Appl Artif Intell 108:104558. https:\/\/doi.org\/10.1016\/j.engappai.2021.104558","journal-title":"Eng Appl Artif Intell"},{"key":"11239_CR89","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110701","volume":"146","author":"Q Yang","year":"2023","unstructured":"Yang Q, Liu J, Wu Z et al (2023) A fusion algorithm based on whale and grey wolf optimization algorithm for solving real-world optimization problems. Appl Soft Comput 146:110701. https:\/\/doi.org\/10.1016\/j.asoc.2023.110701","journal-title":"Appl Soft Comput"},{"key":"11239_CR90","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119041","volume":"213","author":"X Yang","year":"2023","unstructured":"Yang X, Wang R, Zhao D et al (2023) An adaptive quadratic interpolation and rounding mechanism sine cosine algorithm with application to constrained engineering optimization problems. Expert Syst Appl 213:119041. https:\/\/doi.org\/10.1016\/j.eswa.2022.119041","journal-title":"Expert Syst Appl"},{"key":"11239_CR91","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1016\/j.asoc.2019.03.012","volume":"78","author":"H Yapici","year":"2019","unstructured":"Yapici H, Cetinkaya N (2019) A new meta-heuristic optimizer: pathfinder algorithm. Appl Soft Comput 78:545\u2013568. https:\/\/doi.org\/10.1016\/j.asoc.2019.03.012","journal-title":"Appl Soft Comput"},{"key":"11239_CR92","doi-asserted-by":"publisher","unstructured":"Yazdani D, Toosi A, Meybodi M (2010) Fuzzy adaptive artificial fish swarm algorithm. In: Proc the Australasian Joint Conference on Artificial Intelligence pp 334\u2013343. https:\/\/doi.org\/10.1007\/978-3-642-17432-2_34","DOI":"10.1007\/978-3-642-17432-2_34"},{"key":"11239_CR93","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103662","volume":"92","author":"D Yousri","year":"2020","unstructured":"Yousri D, Mirjalili S (2020) Fractional-order cuckoo search algorithm for parameter identification of the fractional-order chaotic, chaotic with noise and hyper-chaotic financial systems. Eng Appl Artif Intell 92:103662. https:\/\/doi.org\/10.1016\/j.engappai.2020.103662","journal-title":"Eng Appl Artif Intell"},{"key":"11239_CR94","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105889","volume":"197","author":"D Yousri","year":"2020","unstructured":"Yousri D, Elaziz M, Mirjalili S (2020) Fractional-order calculus-based flower pollination algorithm with local search for global optimization and image segmentation. Knowl-Based Syst 197:105889. https:\/\/doi.org\/10.1016\/j.knosys.2020.105889","journal-title":"Knowl-Based Syst"},{"key":"11239_CR95","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104193","volume":"100","author":"D Yousri","year":"2021","unstructured":"Yousri D, Mirjalili S, Machado J et al (2021) Efficient fractional-order modified harris hawks optimizer for proton exchange membrane fuel cell modeling. Eng Appl Artif Intell 100:104193. https:\/\/doi.org\/10.1016\/j.engappai.2021.104193","journal-title":"Eng Appl Artif Intell"},{"issue":"6","key":"11239_CR96","doi-asserted-by":"publisher","first-page":"2806","DOI":"10.1109\/TNNLS.2021.3109565","volume":"34","author":"Y Zhang","year":"2021","unstructured":"Zhang Y (2021) Neural network algorithm with reinforcement learning for parameters extraction of photovoltaic models. IEEE Trans Neural Netw Learn Syst 34(6):2806\u20132816. https:\/\/doi.org\/10.1109\/TNNLS.2021.3109565","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"11239_CR97","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113246","volume":"148","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Jin Z (2020) Group teaching optimization algorithm: a novel metaheuristic method for solving global optimization problems. Expert Syst Appl 148:113246. https:\/\/doi.org\/10.1016\/j.eswa.2020.113246","journal-title":"Expert Syst Appl"},{"key":"11239_CR98","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117562","volume":"204","author":"X Zhao","year":"2022","unstructured":"Zhao X, Fang Y, Ma S et al (2022) Multi-swarm improved moth-flame optimization algorithm with chaotic grouping and gaussian mutation for solving engineering optimization problems. Expert Syst Appl 204:117562. https:\/\/doi.org\/10.1016\/j.eswa.2022.117562","journal-title":"Expert Syst Appl"},{"key":"11239_CR99","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122200","volume":"238","author":"W Zhao","year":"2024","unstructured":"Zhao W, Wang L, Zhang Z et al (2024) Electric eel foraging optimization: a new bio-inspired optimizer for engineering applications. Expert Syst Appl 238:122200. https:\/\/doi.org\/10.1016\/j.eswa.2023.122200","journal-title":"Expert Syst Appl"},{"key":"11239_CR100","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121219","volume":"236","author":"F Zhu","year":"2024","unstructured":"Zhu F, Li G, Tang H et al (2024) Dung beetle optimization algorithm based on quantum computing and multi-strategy fusion for solving engineering problems. Expert Syst Appl 236:121219. https:\/\/doi.org\/10.1016\/j.eswa.2023.121219","journal-title":"Expert Syst Appl"},{"key":"11239_CR101","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121597","volume":"237","author":"D Zhu","year":"2024","unstructured":"Zhu D, Wang S, Zhou C et al (2024) Human memory optimization algorithm: a memory-inspired optimizer for global optimization problems. Expert Syst Appl 237:121597. https:\/\/doi.org\/10.1016\/j.eswa.2023.121597","journal-title":"Expert Syst Appl"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11239-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-025-11239-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11239-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T18:09:31Z","timestamp":1757700571000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-025-11239-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,12]]},"references-count":103,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["11239"],"URL":"https:\/\/doi.org\/10.1007\/s10462-025-11239-1","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,12]]},"assertion":[{"value":"10 April 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2025","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 known competing financial interests or personal relationships that could have appeared to influence the work reported in the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This paper does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent is obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"308"}}