{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T23:20:53Z","timestamp":1783984853987,"version":"3.55.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,1,22]],"date-time":"2022-01-22T00:00:00Z","timestamp":1642809600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,22]],"date-time":"2022-01-22T00:00:00Z","timestamp":1642809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s10489-021-02982-3","type":"journal-article","created":{"date-parts":[[2022,1,22]],"date-time":"2022-01-22T14:02:36Z","timestamp":1642860156000},"page":"11300-11323","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Modified group theory-based optimization algorithms for numerical optimization"],"prefix":"10.1007","volume":"52","author":[{"given":"Zewen","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qisheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichao","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,22]]},"reference":[{"key":"2982_CR1","unstructured":"Horst R, Tuy H (2013) Global optimization: deterministic approaches. Springer, Verlag"},{"issue":"6","key":"2982_CR2","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1109\/TEVC.2008.919004","volume":"12","author":"S Dan","year":"2008","unstructured":"Dan S (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702","journal-title":"IEEE Trans Evol Comput"},{"key":"2982_CR3","doi-asserted-by":"crossref","unstructured":"Pillay N, Engelbrecht AP, Abraham A et al (2016) Advances in nature and biologically inspired computing. Springer International Publishing","DOI":"10.1007\/978-3-319-27400-3"},{"key":"2982_CR4","doi-asserted-by":"publisher","unstructured":"Ashlock D (2006) Evolutionary computation for modeling and optimization. Springer, New York. https:\/\/doi.org\/10.1007\/0-387-31909-3","DOI":"10.1007\/0-387-31909-3"},{"key":"2982_CR5","doi-asserted-by":"publisher","unstructured":"He Y, Wang X (2018) Group theory-based optimization algorithm for solving knapsack problems. Knowl Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2018.07.045","DOI":"10.1016\/j.knosys.2018.07.045"},{"key":"2982_CR6","volume-title":"Genetic algorithms in search optimization and machine learning","author":"DE Goldberg","year":"1989","unstructured":"Goldberg D E (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, Boston"},{"issue":"2","key":"2982_CR7","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1016\/j.ejor.2013.03.005","volume":"229","author":"R Zamani","year":"2013","unstructured":"Zamani R (2013) A competitive magnet-based genetic algorithm for solving the resource-constrained project scheduling problem. Eur J Oper Res 229(2):552\u2013559","journal-title":"Eur J Oper Res"},{"key":"2982_CR8","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Procedings of IEEE int. Conf. Neural networks, Perth, pp 1942\u20131948","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"4","key":"2982_CR9","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. J Global Optim 11(4):341\u2013359","journal-title":"J Global Optim"},{"issue":"2","key":"2982_CR10","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1109\/TEVC.2008.927706","volume":"13","author":"AK Qin","year":"2009","unstructured":"Qin A K, Huang V L, Suganthan P N (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398\u2013417","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"2982_CR11","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TEVC.2010.2059031","volume":"15","author":"S Das","year":"2011","unstructured":"Das S, Suganthan P N (2011) Differential Evolution: A Survey of the State-of-the-Art. IEEE Trans Evol Comput 15(1):4\u201331","journal-title":"IEEE Trans Evol Comput"},{"key":"2982_CR12","unstructured":"Dorigo M, Birattari M (2002) Ant colony optimization. Encyclopediaof Machine Learning. Springer, Boston, pp 36\u201339"},{"issue":"3","key":"2982_CR13","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s10898-007-9149-x","volume":"39","author":"D Karaboga","year":"2007","unstructured":"Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459\u2013471","journal-title":"J Glob Optim"},{"issue":"3","key":"2982_CR14","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili S M, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69(3):46\u201361","journal-title":"Adv Eng Softw"},{"key":"2982_CR15","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/j.eswa.2018.07.022","volume":"113","author":"MA Al-Betar","year":"2018","unstructured":"Al-Betar M A, Awadallah M A, Faris H, et al. (2018) Natural selection methods for Grey Wolf Optimizer. Expert Syst Appl 113:481\u2013498","journal-title":"Expert Syst Appl"},{"key":"2982_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-1539-5","volume-title":"Estimation of distribution algorithms: a new tool for evolutionary computation","author":"P Larranga","year":"2002","unstructured":"Larranga P, Lozano J A (2002) Estimation of distribution algorithms: a new tool for evolutionary computation. Springer, New York"},{"issue":"1","key":"2982_CR17","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/TEVC.2015.2428616","volume":"20","author":"J Wang","year":"2016","unstructured":"Wang J, Tang K, Lozano J A, Yao X (2016) Estimation of the distribution algorithm with a stochastic local search for uncertain capacitated arc routing problems. IEEE Trans Evol Comput 20(1):96\u2013109","journal-title":"IEEE Trans Evol Comput"},{"key":"2982_CR18","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","journal-title":"Knowl Based Syst"},{"key":"2982_CR19","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1016\/j.asoc.2019.01.049","volume":"77","author":"Y He","year":"2019","unstructured":"He Y, Wang X, Gao S (2019) Ring Theory-Based evolutionary algorithm and its application to d0-1KP. Appl Soft Comput 77:714\u2013722","journal-title":"Appl Soft Comput"},{"key":"2982_CR20","doi-asserted-by":"publisher","unstructured":"Hossam F, Ala\u2019m A-Z, oubi Asghar AH, et al. (2019) An Intelligent System for Spam Detection and Identification of the most Relevant Features based on Evolutionary Random Weight Networks. Inform Fusion 48:67\u201383. https:\/\/doi.org\/10.1016\/j.inffus.2018.08.002","DOI":"10.1016\/j.inffus.2018.08.002"},{"key":"2982_CR21","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.jocs.2017.07.018","volume":"25","author":"LM Abualigah","year":"2018","unstructured":"Abualigah L M, Khader A T, Hanandeh E S (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456\u2013466","journal-title":"J Comput Sci"},{"key":"2982_CR22","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.ins.2018.12.086","volume":"481","author":"Y Marinakis","year":"2019","unstructured":"Marinakis Y, Marinaki M, Migdalas A (2019) A multi-adaptive particle swarm optimization for the vehicle routing problem with time windows. Inform Sci 481:311\u2013329","journal-title":"Inform Sci"},{"key":"2982_CR23","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.swevo.2019.02.009","volume":"46","author":"J Decerle","year":"2019","unstructured":"Decerle J, Grunder O, Hajjam A, et al. (2019) A hybrid memetic-ant colony optimization algorithm for the home health care problem with time window, synchronization and working time balancing. Swarm Evol Comput 46:171\u2013183","journal-title":"Swarm Evol Comput"},{"key":"2982_CR24","doi-asserted-by":"publisher","unstructured":"Sallam K M, Chakrabortty R K, Ryan M J (2020) A Two-stage multi-operator differential evolution algorithm for solving Resource Constrained Project Scheduling problems. Future Generation Computer Systems. https:\/\/doi.org\/10.1016\/j.future.2020.02.074","DOI":"10.1016\/j.future.2020.02.074"},{"key":"2982_CR25","doi-asserted-by":"crossref","unstructured":"Das K R, Das D, Das J (2015) Optimal tuning of PID controller using GWO algorithm for speed control in DC motor. In: International conference on soft computing techniques implementations. IEEE, Faridabad, pp 108\u2013112","DOI":"10.1109\/ICSCTI.2015.7489575"},{"key":"2982_CR26","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.compeleceng.2017.07.023","volume":"70","author":"K Srikanth","year":"2018","unstructured":"Srikanth K, Panwar L K, Panigrahi B (2018) Meta-heuristic framework: Quantum inspired binary grey wolf optimizer for unit commitment problem. Comput Electr Eng 70:243\u2013 260","journal-title":"Comput Electr Eng"},{"issue":"1","key":"2982_CR27","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1515\/jisys-2014-0137","volume":"26","author":"V Kumar","year":"2016","unstructured":"Kumar V, Chhabra J K, Kumar D (2016) Grey wolf Algorithm-Based clustering technique. J Intell Syst 26(1):153\u2013168","journal-title":"J Intell Syst"},{"key":"2982_CR28","doi-asserted-by":"publisher","unstructured":"Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51-67. https:\/\/doi.org\/10.1016\/j.advengsoft.2016.01.008","DOI":"10.1016\/j.advengsoft.2016.01.008"},{"key":"2982_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compstruc.2016.03.001","volume":"169","author":"A Askarzadeh","year":"2016","unstructured":"Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Compute Struct 169:1\u201312","journal-title":"Compute Struct"},{"key":"2982_CR30","doi-asserted-by":"crossref","unstructured":"Hayyolalam V, Kazem A (2020) Black Widow Optimization Algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Eng Appl Artif Intel 87:103249.1\u2013103249.28","DOI":"10.1016\/j.engappai.2019.103249"},{"key":"2982_CR31","doi-asserted-by":"publisher","unstructured":"Satnam K, Lalit K. A., Sangal A. L., Gaurav D (2020) Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Eng Appl Artif Intel 90:103541. https:\/\/doi.org\/10.1016\/j.engappai.2020.103541","DOI":"10.1016\/j.engappai.2020.103541"},{"key":"2982_CR32","doi-asserted-by":"publisher","unstructured":"Zhai Q, He Y, Wang G, et al. (2021) A general approach to solving hardware and software partitioning problem based on evolutionary algorithms. Adv Eng Softw. https:\/\/doi.org\/10.1016\/j.advengsoft.2021.102998","DOI":"10.1016\/j.advengsoft.2021.102998"},{"key":"2982_CR33","unstructured":"Awad N H, Ali M Z, Liang J J, et al. (2017) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained Real-Parameter numerical optimization, nanyang technological university, Technical. Report, Singapore"},{"key":"2982_CR34","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1007\/s10489-020-01893-z","volume":"51","author":"FA Hashim","year":"2021","unstructured":"Hashim F A, Hussain K, Houssein E H, et al. (2021) Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl Intell 51:1531\u20131551","journal-title":"Appl Intell"},{"key":"2982_CR35","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/s10489-017-0900-9","volume":"47","author":"B Sharma","year":"2017","unstructured":"Sharma B, Prakash R, Tiwari S, et al. (2017) A variant of environmental adaptation method with real parameter encoding and its application in economic load dispatch problem. Appl Intell 47:409\u2013429","journal-title":"Appl Intell"},{"key":"2982_CR36","doi-asserted-by":"publisher","unstructured":"Xu G, Zhang T, Lai Q (2021) A new firefly algorithm with mean condition partial attraction. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-021-02642-6","DOI":"10.1007\/s10489-021-02642-6"},{"issue":"260","key":"2982_CR37","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1080\/01621459.1952.10483441","volume":"47","author":"WH Kruskal","year":"1952","unstructured":"Kruskal W H, Allen W (1952) Use of ranks in one-criterion variance analysis. Publ Am Stat Assoc 47(260):583\u2013621","journal-title":"Publ Am Stat Assoc"},{"issue":"10","key":"2982_CR38","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1007\/s00500-008-0392-y","volume":"13","author":"S Garcia","year":"2009","unstructured":"Garcia S, Ferna\u030cndez A, Luengo J (2009) A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput 13(10):959\u2013977","journal-title":"Soft Comput"},{"issue":"1","key":"2982_CR39","first-page":"1","volume":"7","author":"J Demiar","year":"2006","unstructured":"Demiar J, Schuurmans D (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(1):1\u201330","journal-title":"J Mach Learn Res"},{"issue":"11","key":"2982_CR40","doi-asserted-by":"publisher","first-page":"7665","DOI":"10.1007\/s00521-018-3592-0","volume":"31","author":"K Hussain","year":"2018","unstructured":"Hussain K, Salleh M N M, Cheng S, Shi Y (2018) On the exploration and exploitation in popular swarm-based metaheuristic algorithms. Neural Comput Appl 31(11):7665\u2013 7683","journal-title":"Neural Comput Appl"},{"issue":"2","key":"2982_CR41","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s12293-018-0259-4","volume":"11","author":"X Yan","year":"2019","unstructured":"Yan X, Zhu Z, Wu Q, et al. (2019) Elastic parameter inversion problem based on brain storm optimization algorithm. Memet Comput 11(2):143\u2013153","journal-title":"Memet Comput"},{"key":"2982_CR42","unstructured":"Wang L (2015) Study on intelligent optimization algorithm with application to prestack AVO nonlinear inversion, Dissertation. China University of Geosciences"},{"key":"2982_CR43","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.ins.2019.12.083","volume":"517","author":"X Yan","year":"2020","unstructured":"Yan X, Li P, Tang K, et al. (2020) Clonal selection based intelligent parameter inversion algorithm for prestack seismic data. Inform Sci 517:86\u201399","journal-title":"Inform Sci"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02982-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02982-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02982-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T05:23:17Z","timestamp":1656393797000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02982-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,22]]},"references-count":43,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["2982"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02982-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,22]]},"assertion":[{"value":"5 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}