{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T04:41:50Z","timestamp":1784954510272,"version":"3.55.0"},"reference-count":141,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005046","name":"Heilongjiang Natural Science Foundation","doi-asserted-by":"crossref","award":["LH2019F051"],"award-info":[{"award-number":["LH2019F051"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s13042-024-02197-1","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T09:02:13Z","timestamp":1727341333000},"page":"189-231","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Multi-strategy dung beetle optimizer for global optimization and feature selection"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7077-2124","authenticated-orcid":false,"given":"Huangzhi","family":"Xia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Limin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,26]]},"reference":[{"key":"2197_CR1","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.1007\/s10462-020-09933-3","volume":"54","author":"M Abd Elaziz","year":"2021","unstructured":"Abd Elaziz M, Attiya I (2021) An improved henry gas solubility optimization algorithm for task scheduling in cloud computing. Artif Intell Rev 54:3599\u20133637","journal-title":"Artif Intell Rev"},{"key":"2197_CR2","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.eswa.2017.07.043","volume":"90","author":"M Abd Elaziz","year":"2017","unstructured":"Abd Elaziz M, Oliva D, Xiong S (2017) An improved opposition-based sine cosine algorithm for global optimization. Expert Syst Appl 90:484\u2013500","journal-title":"Expert Syst Appl"},{"issue":"9","key":"2197_CR3","doi-asserted-by":"publisher","first-page":"9329","DOI":"10.1007\/s10462-023-10403-9","volume":"56","author":"M Abdel-Basset","year":"2023","unstructured":"Abdel-Basset M, El-Shahat D, Jameel M et al (2023) Exponential distribution optimizer (EDO): a novel math-inspired algorithm for global optimization and engineering problems. Artif Intell Rev 56(9):9329\u20139400. https:\/\/doi.org\/10.1007\/s10462-023-10403-9","journal-title":"Artif Intell Rev"},{"key":"2197_CR4","doi-asserted-by":"crossref","first-page":"110454","DOI":"10.1016\/j.knosys.2023.110454","volume":"268","author":"M Abdel-Basset","year":"2023","unstructured":"Abdel-Basset M, Mohamed R, Azeem SAA et al (2023) Kepler optimization algorithm: a new metaheuristic algorithm inspired by Kepler\u2019s laws of planetary motion. Knowl Based Syst 268:110454","journal-title":"Knowl Based Syst"},{"issue":"10","key":"2197_CR5","doi-asserted-by":"crossref","first-page":"11675","DOI":"10.1007\/s10462-023-10446-y","volume":"56","author":"M Abdel-Basset","year":"2023","unstructured":"Abdel-Basset M, Mohamed R, Jameel M et al (2023) Spider wasp optimizer: a novel meta-heuristic optimization algorithm. Artif Intell Rev 56(10):11675\u201311738","journal-title":"Artif Intell Rev"},{"issue":"11","key":"2197_CR6","doi-asserted-by":"crossref","first-page":"13224","DOI":"10.1007\/s10489-022-04201-z","volume":"53","author":"BH Abed-Alguni","year":"2022","unstructured":"Abed-Alguni BH, Alawad NA, Al-Betar MA et al (2022) Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection. Appl Intell 53(11):13224\u201313260","journal-title":"Appl Intell"},{"key":"2197_CR7","doi-asserted-by":"crossref","first-page":"113609","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","journal-title":"Comput Methods Appl Mech Eng"},{"issue":"1","key":"2197_CR8","first-page":"99","volume":"24","author":"A AbuNaser","year":"2015","unstructured":"AbuNaser A, Doush IA, Mansour N et al (2015) Underwater image enhancement using particle swarm optimization. J Intell Syst 24(1):99\u2013115","journal-title":"J Intell Syst"},{"key":"2197_CR9","doi-asserted-by":"crossref","first-page":"2237","DOI":"10.1007\/s10462-019-09732-5","volume":"53","author":"HA Alsattar","year":"2020","unstructured":"Alsattar HA, Zaidan A, Zaidan B (2020) Novel meta-heuristic bald eagle search optimisation algorithm. Artif Intell Rev 53:2237\u20132264","journal-title":"Artif Intell Rev"},{"issue":"5","key":"2197_CR10","doi-asserted-by":"crossref","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","journal-title":"Artif Intell Rev"},{"key":"2197_CR11","volume-title":"Introduction to optimum design","author":"J Arora","year":"2004","unstructured":"Arora J (2004) Introduction to optimum design. Elsevier, Amsterdam"},{"key":"2197_CR12","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1007\/s00500-018-3102-4","volume":"23","author":"S Arora","year":"2019","unstructured":"Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23:715\u2013734","journal-title":"Soft Comput"},{"issue":"1","key":"2197_CR13","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1038\/s41598-022-27344-y","volume":"13","author":"M Azizi","year":"2023","unstructured":"Azizi M, Aickelin U, Khorshidi HA et al (2023) Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization. Sci Rep 13(1):226","journal-title":"Sci Rep"},{"issue":"1","key":"2197_CR14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1162\/evco.1993.1.1.1","volume":"1","author":"T B\u00e4ck","year":"1993","unstructured":"B\u00e4ck T, Schwefel HP (1993) An overview of evolutionary algorithms for parameter optimization. Evol Comput 1(1):1\u201323","journal-title":"Evol Comput"},{"key":"2197_CR15","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.swevo.2019.04.010","volume":"48","author":"SK Baliarsingh","year":"2019","unstructured":"Baliarsingh SK, Vipsita S, Muhammad K et al (2019) Analysis of high-dimensional biomedical data using an evolutionary multi-objective emperor penguin optimizer. Swarm Evol Comput 48:262\u2013273","journal-title":"Swarm Evol Comput"},{"key":"2197_CR16","doi-asserted-by":"publisher","unstructured":"Beni G, Wang J (1993) Swarm intelligence in cellular robotic systems. In: Dario P, Sandini G., Aebischer P (eds) Robots and biological systems: towards a new bionics? NATO ASI Series, vol 102. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-642-58069-7_38","DOI":"10.1007\/978-3-642-58069-7_38"},{"issue":"4","key":"2197_CR17","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1038\/s41596-019-0139-5","volume":"14","author":"MG Best","year":"2019","unstructured":"Best MG, In\u2019t Veld SG, Sol N et al (2019) RNA sequencing and swarm intelligence-enhanced classification algorithm development for blood-based disease diagnostics using spliced blood platelet RNA. Nat Protoc 14(4):1206\u20131234","journal-title":"Nat Protoc"},{"issue":"1","key":"2197_CR18","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s10462-022-10164-x","volume":"56","author":"M Braik","year":"2023","unstructured":"Braik M, Al-Zoubi H, Ryalat M et al (2023) Memory based hybrid crow search algorithm for solving numerical and constrained global optimization problems. Artif Intell Rev 56(1):27\u201399","journal-title":"Artif Intell Rev"},{"issue":"1","key":"2197_CR19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"XN Bui","year":"2019","unstructured":"Bui XN, 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):1\u201314","journal-title":"Sci Rep"},{"key":"2197_CR20","doi-asserted-by":"crossref","unstructured":"Cameron C, Hartford J, Lundy T et\u00a0al (2022) The perils of learning before optimizing. In: Proceedings of the AAAI conference on artificial intelligence, vol 36(4). AAAI Press, Palo Alto, California,  USA, pp 3708\u20133715","DOI":"10.1609\/aaai.v36i4.20284"},{"issue":"1","key":"2197_CR21","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1002\/int.22617","volume":"37","author":"S Chakraborty","year":"2022","unstructured":"Chakraborty S, Saha AK, 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","journal-title":"Int J Intell Syst"},{"key":"2197_CR22","doi-asserted-by":"crossref","unstructured":"Chaudhary S, Kalathil D (2022) Safe online convex optimization with unknown linear safety constraints. In: Proceedings of the AAAI conference on artificial intelligence, vol 36(6). AAAI Press, Palo Alto, California USA, pp 6175\u20136182","DOI":"10.1609\/aaai.v36i6.20566"},{"key":"2197_CR23","doi-asserted-by":"crossref","first-page":"120027","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","journal-title":"Expert Syst Appl"},{"key":"2197_CR24","doi-asserted-by":"crossref","unstructured":"Chitty DM (2021) Partial-ACO as a GA mutation operator applied to TSP instances. In: Proceedings of the genetic and evolutionary computation conference companion, Association for Computing Machinery, New York, NY, USA, pp 69\u201370","DOI":"10.1145\/3449726.3459424"},{"key":"2197_CR25","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/BF02471151","volume":"2","author":"C Choi","year":"1998","unstructured":"Choi C, Lee JJ (1998) Chaotic local search algorithm. Artif Life Robot 2:41\u201347","journal-title":"Artif Life Robot"},{"key":"2197_CR26","doi-asserted-by":"crossref","unstructured":"Chu SC, Tsai PW, Pan JS (2006) Cat swarm optimization. In: PRICAI 2006: trends in artificial intelligence: 9th Pacific rim international conference on artificial intelligence Guilin, China, August 7\u201311, 2006 proceedings 9. Springer, Berlin, pp 854\u2013858","DOI":"10.1007\/978-3-540-36668-3_94"},{"issue":"2","key":"2197_CR27","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/S0166-3615(99)00046-9","volume":"41","author":"CAC Coello","year":"2000","unstructured":"Coello CAC (2000) Use of a self-adaptive penalty approach for engineering optimization problems. Comput Ind 41(2):113\u2013127","journal-title":"Comput Ind"},{"key":"2197_CR28","unstructured":"Colorni A, Dorigo M, Maniezzo V et\u00a0al (1991) Distributed optimization by ant colonies. In: Proceedings of the first European conference on artificial life, vol 142. Elsevier Publishing, Paris, France, pp 134\u2013142"},{"issue":"3","key":"2197_CR29","doi-asserted-by":"crossref","first-page":"2942","DOI":"10.1007\/s10489-021-02444-w","volume":"52","author":"AK Das","year":"2022","unstructured":"Das AK, Pratihar DK (2022) Bonobo optimizer (BO): an intelligent heuristic with self-adjusting parameters over continuous spaces and its applications to engineering problems. Appl Intell 52(3):2942\u20132974","journal-title":"Appl Intell"},{"key":"2197_CR30","doi-asserted-by":"crossref","first-page":"110011","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","journal-title":"Knowl Based Syst"},{"issue":"1","key":"2197_CR31","doi-asserted-by":"crossref","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","journal-title":"Swarm Evol Comput"},{"key":"2197_CR32","doi-asserted-by":"crossref","first-page":"108733","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","journal-title":"Appl Soft Comput"},{"key":"2197_CR33","doi-asserted-by":"crossref","first-page":"119017","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","journal-title":"Expert Syst Appl"},{"key":"2197_CR34","doi-asserted-by":"crossref","first-page":"106734","DOI":"10.1016\/j.asoc.2020.106734","volume":"98","author":"Feng Zk","year":"2021","unstructured":"Zk Feng, Wj Niu, Liu S (2021) Cooperation search algorithm: a novel metaheuristic evolutionary intelligence algorithm for numerical optimization and engineering optimization problems. Appl Soft Comput 98:106734","journal-title":"Appl Soft Comput"},{"key":"2197_CR35","doi-asserted-by":"crossref","first-page":"108562","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","journal-title":"Appl Soft Comput"},{"key":"2197_CR36","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-38778-3","author":"S Ferahtia","year":"2023","unstructured":"Ferahtia S, Houari A, Rezk H et al (2023) Red-tailed hawk algorithm for numerical optimization and real-world problems. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-023-38778-3","journal-title":"Sci Rep"},{"issue":"6","key":"2197_CR37","doi-asserted-by":"crossref","first-page":"3954","DOI":"10.1109\/TSMC.2019.2956121","volume":"51","author":"S Gao","year":"2019","unstructured":"Gao S, Yu Y, Wang Y et al (2019) Chaotic local search-based differential evolution algorithms for optimization. IEEE Trans Syst Man Cybern: Syst 51(6):3954\u20133967","journal-title":"IEEE Trans Syst Man Cybern: Syst"},{"issue":"3","key":"2197_CR38","doi-asserted-by":"crossref","first-page":"1981","DOI":"10.1007\/s10586-021-03304-5","volume":"25","author":"FS Gharehchopogh","year":"2022","unstructured":"Gharehchopogh FS, Abdollahzadeh B (2022) An efficient Harris Hawk Optimization algorithm for solving the travelling salesman problem. Clust Comput 25(3):1981\u20132005","journal-title":"Clust Comput"},{"issue":"1","key":"2197_CR39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-79139-8","volume":"11","author":"B Gong","year":"2021","unstructured":"Gong B (2021) Study of PLSR-BP model for stability assessment of loess slope based on particle swarm optimization. Sci Rep 11(1):1\u201310","journal-title":"Sci Rep"},{"key":"2197_CR40","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s00500-016-2385-6","volume":"22","author":"S Gu","year":"2018","unstructured":"Gu S, Cheng R, Jin Y (2018) Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Comput 22:811\u2013822","journal-title":"Soft Comput"},{"issue":"8","key":"2197_CR41","first-page":"1654","volume":"34","author":"WY Guo","year":"2019","unstructured":"Guo WY, Wang Y, Dai F et al (2019) Alternating sine cosine algorithm based on elite chaotic search strategy. Control Decis 34(8):1654\u20131662","journal-title":"Control Decis"},{"key":"2197_CR42","doi-asserted-by":"crossref","first-page":"106542","DOI":"10.1016\/j.asoc.2020.106542","volume":"96","author":"S Gupta","year":"2020","unstructured":"Gupta S, Deep K, Mirjalili S (2020) An efficient equilibrium optimizer with mutation strategy for numerical optimization. Appl Soft Comput 96:106542","journal-title":"Appl Soft Comput"},{"issue":"Mar","key":"2197_CR43","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3(Mar):1157\u20131182","journal-title":"J Mach Learn Res"},{"issue":"8","key":"2197_CR44","first-page":"2317","volume":"36","author":"FF Han","year":"2019","unstructured":"Han FF, Zhao QH, Du ZH et al (2019) Enhanced chicken swarm algorithm for global optimization. Appl Res Comput 36(8):2317\u20132319","journal-title":"Appl Res Comput"},{"key":"2197_CR45","doi-asserted-by":"crossref","first-page":"108320","DOI":"10.1016\/j.knosys.2022.108320","volume":"242","author":"FA Hashim","year":"2022","unstructured":"Hashim FA, Hussien AG (2022) Snake optimizer: a novel meta-heuristic optimization algorithm. Knowl Based Syst 242:108320","journal-title":"Knowl Based Syst"},{"key":"2197_CR46","unstructured":"Holland J (1975) Adaptation in natural and artificial systems : an introductory analysis with application to biology, control, and artificial intelligence, MIT press, pp 126\u2013153"},{"issue":"1","key":"2197_CR47","doi-asserted-by":"crossref","first-page":"5512","DOI":"10.1038\/s41598-023-32189-0","volume":"13","author":"D Hu","year":"2023","unstructured":"Hu D, Hu Y, Yi S et al (2023) Prediction method of surface settlement of rectangular pipe jacking tunnel based on improved PSO-BP neural network. Sci Rep 13(1):5512","journal-title":"Sci Rep"},{"key":"2197_CR48","doi-asserted-by":"crossref","first-page":"7665","DOI":"10.1007\/s00521-018-3592-0","volume":"31","author":"K Hussain","year":"2019","unstructured":"Hussain K, Salleh MNM, Cheng S et al (2019) On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Comput Appl 31:7665\u20137683","journal-title":"Neural Comput Appl"},{"issue":"2","key":"2197_CR49","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s13042-021-01326-4","volume":"13","author":"AG Hussien","year":"2022","unstructured":"Hussien AG, 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 Cybern 13(2):309\u2013336","journal-title":"Int J Mach Learn Cybern"},{"issue":"15","key":"2197_CR50","doi-asserted-by":"crossref","first-page":"3175","DOI":"10.1016\/j.ins.2011.03.018","volume":"181","author":"D Jia","year":"2011","unstructured":"Jia D, Zheng G, Khan MK (2011) An effective memetic differential evolution algorithm based on chaotic local search. Inf Sci 181(15):3175\u20133187","journal-title":"Inf Sci"},{"key":"2197_CR51","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/s10489-019-01507-3","volume":"50","author":"A Kaur","year":"2020","unstructured":"Kaur A, Jain S, Goel S (2020) Sandpiper optimization algorithm: a novel approach for solving real-life engineering problems. Appl Intell 50:582\u2013619","journal-title":"Appl Intell"},{"key":"2197_CR52","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN\u201995-international conference on neural networks, vol 4. IEEE, Perth, WA, Australia, pp 1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"key":"2197_CR53","doi-asserted-by":"crossref","first-page":"113338","DOI":"10.1016\/j.eswa.2020.113338","volume":"149","author":"M Khishe","year":"2020","unstructured":"Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338","journal-title":"Expert Syst Appl"},{"key":"2197_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106938","author":"MS Kiran","year":"2021","unstructured":"Kiran MS, Hakli H (2021) A tree-seed algorithm based on intelligent search mechanisms for continuous optimization. Appl Soft Comput. https:\/\/doi.org\/10.1016\/j.asoc.2020.106938","journal-title":"Appl Soft Comput"},{"issue":"7","key":"2197_CR55","doi-asserted-by":"crossref","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","journal-title":"Int J Intell Syst"},{"key":"2197_CR56","doi-asserted-by":"crossref","first-page":"108164","DOI":"10.1016\/j.cie.2022.108164","volume":"169","author":"T Kuo","year":"2022","unstructured":"Kuo T, Wang KJ (2022) A hybrid $$k$$-prototypes clustering approach with improved sine-cosine algorithm for mixed-data classification. Comput Ind Eng 169:108164","journal-title":"Comput Ind Eng"},{"issue":"1","key":"2197_CR57","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1038\/s41598-021-04506-y","volume":"12","author":"FF Li","year":"2022","unstructured":"Li FF, Du Y, Jia KJ (2022) Path planning and smoothing of mobile robot based on improved artificial fish swarm algorithm. Sci Rep 12(1):659","journal-title":"Sci Rep"},{"key":"2197_CR58","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.ins.2021.05.064","volume":"574","author":"G Li","year":"2021","unstructured":"Li G, Wang GG, Dong J et al (2021) DLEA: a dynamic learning evolution algorithm for many-objective optimization. Inf Sci 574:567\u2013589","journal-title":"Inf Sci"},{"key":"2197_CR59","doi-asserted-by":"crossref","first-page":"6090","DOI":"10.1007\/s11227-021-04116-5","volume":"78","author":"M Li","year":"2022","unstructured":"Li M, Xu G, Fu B et al (2022) Whale optimization algorithm based on dynamic pinhole imaging and adaptive strategy. J Supercomput 78:6090\u20136120","journal-title":"J Supercomput"},{"key":"2197_CR60","doi-asserted-by":"crossref","first-page":"3781","DOI":"10.1007\/s11831-021-09562-1","volume":"28","author":"W Li","year":"2021","unstructured":"Li W, Wang GG, Gandomi AH (2021) A survey of learning-based intelligent optimization algorithms. Arch Comput Methods Eng 28:3781\u20133799","journal-title":"Arch Comput Methods Eng"},{"issue":"6","key":"2197_CR61","doi-asserted-by":"crossref","first-page":"6133","DOI":"10.1007\/s10489-022-03743-6","volume":"53","author":"W Li","year":"2023","unstructured":"Li W, Shi R, Dong J (2023) Harris hawks optimizer based on the novice protection tournament for numerical and engineering optimization problems. Appl Intell 53(6):6133\u20136158","journal-title":"Appl Intell"},{"key":"2197_CR62","doi-asserted-by":"crossref","first-page":"106933","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","journal-title":"Appl Soft Comput"},{"key":"2197_CR63","doi-asserted-by":"crossref","first-page":"101282","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","journal-title":"Swarm Evol Comput"},{"issue":"4","key":"2197_CR64","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TKDE.2005.66","volume":"17","author":"H Liu","year":"2005","unstructured":"Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491\u2013502","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2197_CR65","doi-asserted-by":"crossref","first-page":"119898","DOI":"10.1016\/j.eswa.2023.119898","volume":"224","author":"H Liu","year":"2023","unstructured":"Liu H, Zhang X, Zhang H et al (2023) A reinforcement learning-based hybrid Aquila Optimizer and improved Arithmetic Optimization Algorithm for global optimization. Expert Syst Appl 224:119898","journal-title":"Expert Syst Appl"},{"issue":"5","key":"2197_CR66","first-page":"431","volume":"31","author":"SJ Liu","year":"2018","unstructured":"Liu SJ, Yang Y, Zhou YQ (2018) A swarm intelligence algorithm-lion swarm optimization. Pattern Recognit Artif Intell 31(5):431\u2013441","journal-title":"Pattern Recognit Artif Intell"},{"key":"2197_CR67","doi-asserted-by":"crossref","unstructured":"Lou A (2019) A fusion algorithm of gravitational search and tabu search. In: Proceedings of the 2019 international conference on artificial intelligence and computer science, Association for Computing Machinery, New York, NY, USA, pp 150\u2013157","DOI":"10.1145\/3349341.3349394"},{"key":"2197_CR68","doi-asserted-by":"crossref","first-page":"100851","DOI":"10.1016\/j.swevo.2021.100851","volume":"62","author":"M Lozano","year":"2021","unstructured":"Lozano M, Rodriguez FJ (2021) Network reconstruction from betweenness centrality by artificial bee colony. Swarm Evol Comput 62:100851","journal-title":"Swarm Evol Comput"},{"key":"2197_CR69","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2016.12.005","volume":"33","author":"M Mavrovouniotis","year":"2017","unstructured":"Mavrovouniotis M, Li C, Yang S (2017) A survey of swarm intelligence for dynamic optimization: algorithms and applications. Swarm Evol Comput 33:1\u201317","journal-title":"Swarm Evol Comput"},{"issue":"6","key":"2197_CR70","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1063\/1.1699114","volume":"21","author":"N Metropolis","year":"1953","unstructured":"Metropolis N, Rosenbluth AW, Rosenbluth MN et al (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087\u20131092","journal-title":"J Chem Phys"},{"key":"2197_CR71","doi-asserted-by":"crossref","first-page":"119211","DOI":"10.1016\/j.eswa.2022.119211","volume":"213","author":"HL Minh","year":"2023","unstructured":"Minh HL, Sang-To T, Theraulaz G et al (2023) Termite life cycle optimizer. Expert Syst Appl 213:119211","journal-title":"Expert Syst Appl"},{"key":"2197_CR72","doi-asserted-by":"crossref","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","journal-title":"Knowl Based Syst"},{"key":"2197_CR73","doi-asserted-by":"crossref","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","journal-title":"Adv Eng Softw"},{"key":"2197_CR74","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"issue":"1","key":"2197_CR75","doi-asserted-by":"crossref","first-page":"5211","DOI":"10.1038\/s41598-023-31876-2","volume":"13","author":"S Mohapatra","year":"2023","unstructured":"Mohapatra S, Mohapatra P (2023) American zebra optimization algorithm for global optimization problems. Sci Rep 13(1):5211","journal-title":"Sci Rep"},{"key":"2197_CR76","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1016\/j.asoc.2018.06.022","volume":"70","author":"MZ bin Mohd Zain","year":"2018","unstructured":"bin Mohd Zain MZ, Kanesan J, Chuah JH et al (2018) A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization. Appl Soft Comput 70:680\u2013700","journal-title":"Appl Soft Comput"},{"key":"2197_CR77","doi-asserted-by":"crossref","first-page":"100671","DOI":"10.1016\/j.swevo.2020.100671","volume":"54","author":"B Morales-Casta\u00f1eda","year":"2020","unstructured":"Morales-Casta\u00f1eda B, Zaldivar D, Cuevas E et al (2020) A better balance in metaheuristic algorithms: does it exist? Swarm Evol Comput 54:100671","journal-title":"Swarm Evol Comput"},{"key":"2197_CR78","doi-asserted-by":"crossref","first-page":"114607","DOI":"10.1016\/j.eswa.2021.114607","volume":"172","author":"H Muthusamy","year":"2021","unstructured":"Muthusamy H, Ravindran S, Yaacob S et al (2021) An improved elephant herding optimization using sine-cosine mechanism and opposition based learning for global optimization problems. Expert Syst Appl 172:114607","journal-title":"Expert Syst Appl"},{"key":"2197_CR79","doi-asserted-by":"crossref","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","journal-title":"Expert Syst Appl"},{"key":"2197_CR80","doi-asserted-by":"crossref","first-page":"115352","DOI":"10.1016\/j.eswa.2021.115352","volume":"183","author":"I Naruei","year":"2021","unstructured":"Naruei I, Keynia F (2021) A new optimization method based on coot bird natural life model. Expert Syst Appl 183:115352","journal-title":"Expert Syst Appl"},{"key":"2197_CR81","doi-asserted-by":"crossref","first-page":"115408","DOI":"10.1016\/j.eswa.2021.115408","volume":"1","author":"MM Noel","year":"2021","unstructured":"Noel MM, Muthiah-Nakarajan V, Amali GB et al (2021) A new biologically inspired global optimization algorithm based on firebug reproductive swarming behaviour. Expert Syst Appl 1:115408","journal-title":"Expert Syst Appl"},{"key":"2197_CR82","volume":"135","author":"H Pan","year":"2023","unstructured":"Pan H, Chen S, Xiong H (2023) A high-dimensional feature selection method based on modified gray wolf optimization. Appl Soft Comput 135:110031","journal-title":"Appl Soft Comput"},{"issue":"22","key":"2197_CR83","first-page":"92","volume":"59","author":"JC Pan","year":"2023","unstructured":"Pan JC, Li SB, Zhou P et al (2023) Dung beetle optimization algorithm guided by improved sine algorithm. Comput Eng Appl 59(22):92\u2013110","journal-title":"Comput Eng Appl"},{"issue":"8","key":"2197_CR84","doi-asserted-by":"crossref","first-page":"6427","DOI":"10.1007\/s00521-021-06775-0","volume":"34","author":"E Pashaei","year":"2022","unstructured":"Pashaei E, Pashaei E (2022) An efficient binary chimp optimization algorithm for feature selection in biomedical data classification. Neural Comput Appl 34(8):6427\u20136451","journal-title":"Neural Comput Appl"},{"issue":"3","key":"2197_CR85","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MCS.2002.1004010","volume":"22","author":"KM Passino","year":"2002","unstructured":"Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52\u201367","journal-title":"IEEE Control Syst Mag"},{"issue":"11","key":"2197_CR86","doi-asserted-by":"crossref","first-page":"13187","DOI":"10.1007\/s10462-023-10470-y","volume":"56","author":"K Rajwar","year":"2023","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","journal-title":"Artif Intell Rev"},{"key":"2197_CR87","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s11721-012-0071-6","volume":"6","author":"AS Rakitianskaia","year":"2012","unstructured":"Rakitianskaia AS, Engelbrecht AP (2012) Training feedforward neural networks with dynamic particle swarm optimisation. Swarm Intell 6:233\u2013270","journal-title":"Swarm Intell"},{"issue":"3","key":"2197_CR88","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.cad.2010.12.015","volume":"43","author":"RV Rao","year":"2011","unstructured":"Rao RV, Savsani VJ, Vakharia D (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303\u2013315","journal-title":"Comput Aided Des"},{"issue":"13","key":"2197_CR89","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232\u20132248","journal-title":"Inf Sci"},{"issue":"1","key":"2197_CR90","doi-asserted-by":"crossref","first-page":"2536","DOI":"10.1038\/s41598-020-59215-9","volume":"10","author":"AT Sahlol","year":"2020","unstructured":"Sahlol AT, Kollmannsberger P, Ewees AA (2020) Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Sci Rep 10(1):2536","journal-title":"Sci Rep"},{"issue":"4","key":"2197_CR91","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1007\/s10462-022-10218-0","volume":"56","author":"SK Sahoo","year":"2023","unstructured":"Sahoo SK, Saha AK, Nama S et al (2023) An improved moth flame optimization algorithm based on modified dynamic opposite learning strategy. Artif Intell Rev 56(4):2811\u20132869","journal-title":"Artif Intell Rev"},{"key":"2197_CR92","doi-asserted-by":"crossref","first-page":"104712","DOI":"10.1016\/j.compbiomed.2021.104712","volume":"136","author":"GI Sayed","year":"2021","unstructured":"Sayed GI, Soliman MM, Hassanien AE (2021) A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization. Comput Biol Med 136:104712","journal-title":"Comput Biol Med"},{"issue":"1","key":"2197_CR93","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-18940-4","volume":"8","author":"YD Sergeyev","year":"2018","unstructured":"Sergeyev YD, Kvasov D, Mukhametzhanov M (2018) On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget. Sci Rep 8(1):1\u20139","journal-title":"Sci Rep"},{"key":"2197_CR94","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.engappai.2019.01.001","volume":"80","author":"S Shadravan","year":"2019","unstructured":"Shadravan S, Naji HR, Bardsiri VK (2019) The sailfish optimizer: a novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems. Eng Appl Artif Intell 80:20\u201334","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"2197_CR95","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2006.04.001","volume":"33","author":"W Shang","year":"2007","unstructured":"Shang W, Huang H, Zhu H et al (2007) A novel feature selection algorithm for text categorization. Expert Syst Appl 33(1):1\u20135","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2197_CR96","first-page":"36","volume":"25","author":"D Singh","year":"2022","unstructured":"Singh D, Shukla A (2022) Manifold optimization with MMSE hybrid precoder for Mm-Wave massive MIMO communication. Rom J Inf Sci Technol 25(1):36\u201346","journal-title":"Rom J Inf Sci Technol"},{"key":"2197_CR97","doi-asserted-by":"crossref","unstructured":"de\u00a0Souza M (2021) Automatic design of heuristic algorithms for binary optimization problems. In: IJCAI, Thirtieth International Joint Conference on Artificial Intelligence, Virtual, Montreal, pp 4881\u20134882","DOI":"10.24963\/ijcai.2021\/672"},{"key":"2197_CR98","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.eswa.2019.05.028","volume":"134","author":"JL Speiser","year":"2019","unstructured":"Speiser JL, Miller ME, Tooze J et al (2019) A comparison of random forest variable selection methods for classification prediction modeling. Expert Syst Appl 134:93\u2013101","journal-title":"Expert Syst Appl"},{"issue":"4","key":"2197_CR99","doi-asserted-by":"crossref","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 Glob Optim 11(4):341","journal-title":"J Glob Optim"},{"key":"2197_CR100","doi-asserted-by":"crossref","unstructured":"Tanabe R, Fukunaga AS (2014) Improving the search performance of SHADE using linear population sizereduction[C]\/\/2014 IEEE congress on evolutionary computation (CEC). IEEE, pp 1658\u20131665","DOI":"10.1109\/CEC.2014.6900380"},{"key":"2197_CR101","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1007\/s00366-021-01286-x","volume":"38","author":"C Tang","year":"2021","unstructured":"Tang C, Zhou Y, Luo Q et al (2021) An enhanced pathfinder algorithm for engineering optimization problems. Eng Comput 38:1481\u20131503","journal-title":"Eng Comput"},{"key":"2197_CR102","doi-asserted-by":"crossref","first-page":"116621","DOI":"10.1016\/j.eswa.2022.116621","volume":"196","author":"A Tiwari","year":"2022","unstructured":"Tiwari A, Chaturvedi A (2022) A hybrid feature selection approach based on information theory and dynamic butterfly optimization algorithm for data classification. Expert Syst Appl 196:116621","journal-title":"Expert Syst Appl"},{"issue":"11","key":"2197_CR103","doi-asserted-by":"crossref","first-page":"7259","DOI":"10.1007\/s00500-023-07930-6","volume":"27","author":"D Wang","year":"2023","unstructured":"Wang D, Liu H, Tu L et al (2023) An orthogonal electric fish optimization algorithm with quantization for global numerical optimization. Soft Comput 27(11):7259\u20137283","journal-title":"Soft Comput"},{"issue":"7","key":"2197_CR104","doi-asserted-by":"crossref","first-page":"7237","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):7237\u20137262","journal-title":"Artif Intell Rev"},{"key":"2197_CR105","doi-asserted-by":"crossref","first-page":"105082","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","journal-title":"Eng Appl Artif Intell"},{"key":"2197_CR106","doi-asserted-by":"crossref","first-page":"109917","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","journal-title":"Appl Soft Comput"},{"issue":"8","key":"2197_CR107","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1038\/s42256-021-00366-3","volume":"3","author":"M Weiel","year":"2021","unstructured":"Weiel M, G\u00f6tz M, Klein A et al (2021) Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions. Nat Mach Intell 3(8):727\u2013734","journal-title":"Nat Mach Intell"},{"issue":"1","key":"2197_CR108","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67\u201382","journal-title":"IEEE Trans Evol Comput"},{"key":"2197_CR109","doi-asserted-by":"crossref","unstructured":"Woodworth BE, Bullins B, Shamir O et\u00a0al (2021) The min-max complexity of distributed stochastic convex optimization with intermittent communication. In: Conference on learning theory. PMLR, Boulder, Colorado, pp 4386\u20134437","DOI":"10.24963\/ijcai.2022\/751"},{"key":"2197_CR110","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1016\/j.ins.2015.09.051","volume":"329","author":"G Wu","year":"2016","unstructured":"Wu G (2016) Across neighborhood search for numerical optimization. Inf Sci 329:597\u2013618","journal-title":"Inf Sci"},{"issue":"4","key":"2197_CR111","first-page":"1","volume":"53","author":"CH Xu","year":"2017","unstructured":"Xu CH, Li CX, Yu X et al (2017) Improved grey wolf optimization algorithm based on chaotic cat mapping and gaussian mutation. Comput Eng Appl 53(4):1\u20139","journal-title":"Comput Eng Appl"},{"key":"2197_CR112","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.ins.2019.04.022","volume":"492","author":"Y Xu","year":"2019","unstructured":"Xu Y, Chen H, Luo J et al (2019) Enhanced moth-flame optimizer with mutation strategy for global optimization. Inf Sci 492:181\u2013203","journal-title":"Inf Sci"},{"issue":"7","key":"2197_CR113","doi-asserted-by":"crossref","first-page":"7305","DOI":"10.1007\/s11227-022-04959-6","volume":"79","author":"J Xue","year":"2022","unstructured":"Xue J, Shen B (2022) Dung beetle optimizer: a new meta-heuristic algorithm for global optimization. J Supercomput 79(7):7305\u20137336","journal-title":"J Supercomput"},{"key":"2197_CR114","doi-asserted-by":"crossref","unstructured":"Xue K, Qian C, Xu L et\u00a0al (2021) Evolutionary gradient descent for non-convex optimization. In: IJCAI, pp 3221\u20133227","DOI":"10.24963\/ijcai.2021\/443"},{"key":"2197_CR115","doi-asserted-by":"crossref","first-page":"136614","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","journal-title":"Chemosphere"},{"key":"2197_CR116","doi-asserted-by":"crossref","first-page":"106198","DOI":"10.1016\/j.engappai.2023.106198","volume":"123","author":"H Yang","year":"2023","unstructured":"Yang H, Gao S, Lei Z et al (2023) An improved spherical evolution with enhanced exploration capabilities to address wind farm layout optimization problem. Eng Appl Artif Intell 123:106198","journal-title":"Eng Appl Artif Intell"},{"issue":"12","key":"2197_CR117","doi-asserted-by":"crossref","first-page":"6075","DOI":"10.1007\/s00170-024-13369-2","volume":"131","author":"T Yang","year":"2024","unstructured":"Yang T, Sun X, Yang H et al (2024) Integrated thermal error modeling and compensation of machine tool feed system using subtraction-average-based optimizer-based CNN-GRU neural network. Int J Adv Manuf Technol 131(12):6075\u20136089","journal-title":"Int J Adv Manuf Technol"},{"key":"2197_CR118","doi-asserted-by":"crossref","unstructured":"Yang XS (2009) Firefly algorithms for multimodal optimization. In: Stochastic algorithms: foundations and applications: 5th international symposium, SAGA 2009, Sapporo, Japan, October 26\u201328, 2009. Proceedings 5. Springer, Berlin, pp 169\u2013178","DOI":"10.1007\/978-3-642-04944-6_14"},{"issue":"2","key":"2197_CR119","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1504\/IJBIC.2010.032124","volume":"2","author":"XS Yang","year":"2010","unstructured":"Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-inspir Comput 2(2):78\u201384","journal-title":"Int J Bio-inspir Comput"},{"key":"2197_CR120","doi-asserted-by":"crossref","unstructured":"Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), Springer, Berlin, Heidelberg, pp 65\u201374","DOI":"10.1007\/978-3-642-12538-6_6"},{"key":"2197_CR121","doi-asserted-by":"crossref","unstructured":"Yang XS, Deb S (2009) Cuckoo search via l\u00e9vy flights. In: 2009 World congress on nature and biologically inspired computing (NaBIC). IEEE, Coimbatore, India, pp 210\u2013214","DOI":"10.1109\/NABIC.2009.5393690"},{"key":"2197_CR122","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107483","volume":"232","author":"Z Yang","year":"2021","unstructured":"Yang Z, Deng LB, Wang Y et al (2021) Aptenodytes forsteri optimization: algorithm and applications. Knowl Based Syst 232:107483","journal-title":"Knowl Based Syst"},{"issue":"8","key":"2197_CR123","doi-asserted-by":"crossref","first-page":"4669","DOI":"10.1007\/s00500-022-07593-9","volume":"27","author":"K Yao","year":"2023","unstructured":"Yao K, Sun J, Chen C et al (2023) An information entropy-based grey wolf optimizer. Soft Comput 27(8):4669\u20134684","journal-title":"Soft Comput"},{"key":"2197_CR124","doi-asserted-by":"crossref","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","journal-title":"Appl Soft Comput"},{"key":"2197_CR125","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1016\/j.asoc.2017.08.051","volume":"61","author":"W Ye","year":"2017","unstructured":"Ye W, Feng W, Fan S (2017) A novel multi-swarm particle swarm optimization with dynamic learning strategy. Appl Soft Comput 61:832\u2013843","journal-title":"Appl Soft Comput"},{"issue":"15","key":"2197_CR126","doi-asserted-by":"crossref","first-page":"11543","DOI":"10.1007\/s00521-019-04641-8","volume":"32","author":"S Yilmaz","year":"2020","unstructured":"Yilmaz S, Sen S (2020) Electric fish optimization: a new heuristic algorithm inspired by electrolocation. Neural Comput Appl 32(15):11543\u201311578","journal-title":"Neural Comput Appl"},{"key":"2197_CR127","doi-asserted-by":"crossref","first-page":"103662","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","journal-title":"Eng Appl Artif Intell"},{"key":"2197_CR128","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s12293-017-0247-0","volume":"10","author":"Y Yu","year":"2018","unstructured":"Yu Y, Gao S, Cheng S et al (2018) CBSO: a memetic brain storm optimization with chaotic local search. Memet Comput 10:353\u2013367","journal-title":"Memet Comput"},{"key":"2197_CR129","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120112","author":"IA Zamfirache","year":"2023","unstructured":"Zamfirache IA, Precup RE, Roman RC et al (2023) Neural network-based control using actor-critic reinforcement learning and grey wolf optimizer with experimental servo system validation. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2023.120112","journal-title":"Expert Syst Appl"},{"key":"2197_CR130","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.swevo.2015.06.002","volume":"25","author":"I Zelinka","year":"2015","unstructured":"Zelinka I (2015) A survey on evolutionary algorithms dynamics and its complexity-mutual relations, past, present and future. Swarm Evol Comput 25:2\u201314","journal-title":"Swarm Evol Comput"},{"key":"2197_CR131","doi-asserted-by":"crossref","unstructured":"Zhang J, Member S, IEEE et al (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945\u2013958","DOI":"10.1109\/TEVC.2009.2014613"},{"issue":"15","key":"2197_CR132","doi-asserted-by":"crossref","first-page":"3043","DOI":"10.1016\/j.ins.2008.02.014","volume":"178","author":"M Zhang","year":"2008","unstructured":"Zhang M, Luo W, Wang X (2008) Differential evolution with dynamic stochastic selection for constrained optimization. Inf Sci 178(15):3043\u20133074","journal-title":"Inf Sci"},{"key":"2197_CR133","doi-asserted-by":"crossref","first-page":"112976","DOI":"10.1016\/j.eswa.2019.112976","volume":"141","author":"X Zhang","year":"2020","unstructured":"Zhang X, Xu Y, Yu C et al (2020) Gaussian mutational chaotic fruit fly-built optimization and feature selection. Expert Syst Appl 141:112976","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2197_CR134","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s10462-022-10182-9","volume":"56","author":"Y Zhang","year":"2023","unstructured":"Zhang Y (2023) An improved generalized normal distribution optimization and its applications in numerical problems and engineering design problems. Artif Intell Rev 56(1):685\u2013747","journal-title":"Artif Intell Rev"},{"key":"2197_CR135","doi-asserted-by":"crossref","first-page":"113246","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","journal-title":"Expert Syst Appl"},{"key":"2197_CR136","doi-asserted-by":"crossref","first-page":"103918","DOI":"10.1016\/j.artint.2023.103918","volume":"319","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Bliek L, da Costa P et al (2023) The first AI4TSP competition: learning to solve stochastic routing problems. Artif Intell 319:103918","journal-title":"Artif Intell"},{"key":"2197_CR137","doi-asserted-by":"crossref","first-page":"105075","DOI":"10.1016\/j.engappai.2022.105075","volume":"114","author":"S Zhao","year":"2022","unstructured":"Zhao S, Zhang T, Ma S et al (2022) Dandelion optimizer: a nature-inspired metaheuristic algorithm for engineering applications. Eng Appl Artif Intell 114:105075","journal-title":"Eng Appl Artif Intell"},{"issue":"10","key":"2197_CR138","doi-asserted-by":"crossref","first-page":"11833","DOI":"10.1007\/s10489-022-03994-3","volume":"53","author":"S Zhao","year":"2022","unstructured":"Zhao S, Zhang T, Ma S et al (2022) Sea-horse optimizer: a novel nature-inspired meta-heuristic for global optimization problems. Appl Intell 53(10):11833\u201311860","journal-title":"Appl Intell"},{"key":"2197_CR139","doi-asserted-by":"crossref","first-page":"2862","DOI":"10.1007\/s10489-019-01409-4","volume":"49","author":"X Zhao","year":"2019","unstructured":"Zhao X, Zhou Y, Xiang Y (2019) A grouping particle swarm optimizer. Appl Intell 49:2862\u20132873","journal-title":"Appl Intell"},{"key":"2197_CR140","doi-asserted-by":"crossref","first-page":"117562","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","journal-title":"Expert Syst Appl"},{"key":"2197_CR141","doi-asserted-by":"crossref","first-page":"109215","DOI":"10.1016\/j.knosys.2022.109215","volume":"251","author":"C Zhong","year":"2022","unstructured":"Zhong C, Li G, Meng Z (2022) Beluga whale optimization: a novel nature-inspired metaheuristic algorithm. Knowl Based Syst 251:109215","journal-title":"Knowl Based Syst"}],"updated-by":[{"DOI":"10.1007\/s13042-024-02458-z","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T00:00:00Z","timestamp":1732579200000}}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02197-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02197-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02197-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T07:39:39Z","timestamp":1737531579000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02197-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,26]]},"references-count":141,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["2197"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02197-1","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s13042-024-02458-z","asserted-by":"object"}]},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,26]]},"assertion":[{"value":"19 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2024","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s13042-024-02458-z","URL":"https:\/\/doi.org\/10.1007\/s13042-024-02458-z","order":7,"name":"change_details","label":"Change Details","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 this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate\/Consent for publication"}}]}}