{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T18:57:58Z","timestamp":1770058678785,"version":"3.49.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Sanming University introduces high-level talents to start scientific research funding support project","award":["20YG14"],"award-info":[{"award-number":["20YG14"]}]},{"name":"Guiding science and technology projects in Sanming City","award":["2020-G-61"],"award-info":[{"award-number":["2020-G-61"]}]},{"name":"Educational research projects of young and middle-aged teachers in Fujian Province","award":["JAT200618"],"award-info":[{"award-number":["JAT200618"]}]},{"name":"Scientific research and development fund of Sanming University","award":["B202009"],"award-info":[{"award-number":["B202009"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2022,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Chimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to the more flexible and complex application fields, researchers have higher requirements for native algorithms. In this paper, an enhanced chimp optimization algorithm (EChOA) is proposed to improve the accuracy of solutions. First, the highly disruptive polynomial mutation is used to initialize the population, which provides the foundation for global search. Next, Spearman\u2019s rank correlation coefficient of the chimps with the lowest social status is calculated with respect to the leader chimp. To reduce the probability of falling into the local optimum, the beetle antennae operator is used to improve the less fit chimps while gaining visual capability. Three strategies enhance the exploration and exploitation of the native algorithm. To verify the function optimization performance, EChOA is comprehensively analyzed on 12 classical benchmark functions and 15 CEC2017 benchmark functions. Besides, the practicability of EChOA is also highlighted by three engineering design problems and training multilayer perceptron. Compared with ChOA and five state-of-the-art algorithms, the statistical results show that EChOA has strong competitive capabilities and promising prospects.<\/jats:p>","DOI":"10.1007\/s40747-021-00346-5","type":"journal-article","created":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T19:02:44Z","timestamp":1617822164000},"page":"65-82","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":92,"title":["An enhanced chimp optimization algorithm for continuous optimization domains"],"prefix":"10.1007","volume":"8","author":[{"given":"Heming","family":"Jia","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9205-4078","authenticated-orcid":false,"given":"Kangjian","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Wanying","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Leng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,7]]},"reference":[{"issue":"8","key":"346_CR1","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.1109\/TLA.2017.7994804","volume":"15","author":"LG Hubner","year":"2017","unstructured":"Hubner LG, Maletzke AG, de Nadai BL, Schaefer RL, Zalewski W, Ferrero CA (2017) FB-DT: an improvement in the Brute Force algorithm for motifs discovery. IEEE Lat Am Trans 15(8):1542\u20131546","journal-title":"IEEE Lat Am Trans"},{"key":"346_CR2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s10951-013-0352-y","volume":"17","author":"M Alzaqebah","year":"2014","unstructured":"Alzaqebah M, Abdullah S (2014) An adaptive artificial bee colony and late-acceptance hill-climbing algorithm for examination timetabling. J Sched 17:249\u2013262","journal-title":"J Sched"},{"key":"346_CR3","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s10479-009-0565-9","volume":"189","author":"A Eshragh","year":"2011","unstructured":"Eshragh A, Filar JA, Haythorpe M (2011) A hybrid simulation-optimization algorithm for the Hamiltonian cycle problem. Ann Oper Res 189:103\u2013125","journal-title":"Ann Oper Res"},{"key":"346_CR4","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1007\/s00521-017-2880-4","volume":"30","author":"MM Alipour","year":"2018","unstructured":"Alipour MM, Razavi SN, Feizi Derakhshi MR, Balafar MA (2018) A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem. Neural Comput Applic 30:2935\u20132951","journal-title":"Neural Comput Applic"},{"key":"346_CR5","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1007\/s10288-011-0193-5","volume":"10","author":"DC Porumbel","year":"2012","unstructured":"Porumbel DC (2012) Heuristic algorithms and learning techniques: applications to the graph coloring problem. 4OR-Q J Oper Res 10:393\u2013394","journal-title":"4OR-Q J Oper Res"},{"key":"346_CR6","first-page":"1","volume-title":"Metaheuristic applications in structures and infrastructures","author":"AH Gandomi","year":"2013","unstructured":"Gandomi AH, Yang X-S, Talatahari S, Alavi AH (2013) Metaheuristic algorithms in modeling and optimization. Metaheuristic applications in structures and infrastructures. Elsevier, Oxford, pp 1\u201324"},{"issue":"1","key":"346_CR7","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.3233\/JIFS-201755","volume":"40","author":"K Sun","year":"2021","unstructured":"Sun K, Jia H, Li Y, Jiang Z (2021) Hybrid improved slime mould algorithm with adaptive \u03b2 hill climbing for numerical optimization. J Intell Fuzzy Sys 40(1):1667\u20131679","journal-title":"J Intell Fuzzy Sys"},{"key":"346_CR8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40747-019-0102-7","volume":"6","author":"A Hussain","year":"2020","unstructured":"Hussain A, Muhammad YS (2020) Trade-off between exploration and exploitation with genetic algorithm using a novel selection operator. Complex Intell Syst 6:1\u201314","journal-title":"Complex Intell Syst"},{"issue":"13","key":"346_CR9","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"},{"key":"346_CR10","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s00521-015-1870-7","volume":"27","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27:495\u2013513","journal-title":"Neural Comput Appl"},{"key":"346_CR11","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.advengsoft.2017.03.014","volume":"110","author":"A Kaveh","year":"2017","unstructured":"Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw 110:69\u201384","journal-title":"Adv Eng Softw"},{"key":"346_CR12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s40747-016-0010-z","volume":"2","author":"G Corriveau","year":"2016","unstructured":"Corriveau G, Guilbault R, Tahan A, Sabourin R (2016) Bayesian network as an adaptive parameter setting approach for genetic algorithms. Complex Intell Syst 2:1\u201322","journal-title":"Complex Intell Syst"},{"issue":"4","key":"346_CR13","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-a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341\u2013359","journal-title":"J Global Optim"},{"key":"346_CR14","doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN\u201995-International Conference on Neural Networks, pp. 1942\u20131948. IEEE, Perth","DOI":"10.1109\/ICNN.1995.488968"},{"key":"346_CR15","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.advengsoft.2015.01.010","volume":"83","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80\u201398","journal-title":"Adv Eng Softw"},{"key":"346_CR16","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":"346_CR17","doi-asserted-by":"crossref","first-page":"113377","DOI":"10.1016\/j.eswa.2020.113377","volume":"152","author":"A Faramarzi","year":"2020","unstructured":"Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377","journal-title":"Expert Syst Appl"},{"issue":"3","key":"346_CR18","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 DP (2011) Teaching-learning-based optimization: a novel method for con-strained mechanical design optimization problems. Comput-Aided Des 43(3):303\u2013315","journal-title":"Comput-Aided Des"},{"key":"346_CR19","first-page":"549","volume":"115","author":"YC Ho","year":"2020","unstructured":"Ho YC, Pepyne DL (2020) Simple explanation of the no-free-lunch theorem and its implications. J Optim Theory Appl 115:549\u2013570","journal-title":"J Optim Theory Appl"},{"issue":"1","key":"346_CR20","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1111\/itor.12001","volume":"22","author":"K S\u00f6rensen","year":"2015","unstructured":"S\u00f6rensen K (2015) Metaheuristics\u2014the metaphor exposed. Int Tran Oper Res 22(1):3\u201318","journal-title":"Int Tran Oper Res"},{"key":"346_CR21","doi-asserted-by":"crossref","unstructured":"Michalewicz Z (2012) Quo vadis, evolutionary computation?: on a growing gap between theory and practice. In: Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence, pp. 98\u2013121. Springer, Brisbane","DOI":"10.1007\/978-3-642-30687-7_6"},{"key":"346_CR22","doi-asserted-by":"crossref","first-page":"107005","DOI":"10.1016\/j.apacoust.2019.107005","volume":"157","author":"M Khishe","year":"2020","unstructured":"Khishe M, Mosavi MR (2020) Classification of underwater acoustical dataset using neural network trained by chimp optimization algorithm. Appl Acoust 157:107005","journal-title":"Appl Acoust"},{"key":"346_CR23","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.ins.2014.11.051","volume":"299","author":"A Arab","year":"2015","unstructured":"Arab A, Alfi A (2015) An adaptive gradient descent-based local search in memetic algorithm applied to optimal controller design. Inf Sci 299:117\u2013142","journal-title":"Inf Sci"},{"issue":"22","key":"346_CR24","doi-asserted-by":"crossref","first-page":"8881","DOI":"10.1016\/j.eswa.2015.07.043","volume":"42","author":"Z Li","year":"2015","unstructured":"Li Z, Wang W, Yan Y, Li Z (2015) PS-ABC: a hybrid algorithm based on particle swarm and artificial bee colony for high-dimensional optimization problems. Expert Syst Appl 42(22):8881\u20138895","journal-title":"Expert Syst Appl"},{"key":"346_CR25","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.asoc.2018.02.025","volume":"66","author":"\u0130 Berkan Aydilek","year":"2018","unstructured":"Berkan Aydilek \u0130 (2018) A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems. Appl Softw Comput 66:232\u2013249","journal-title":"Appl Softw Comput"},{"key":"346_CR26","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.asoc.2017.01.008","volume":"53","author":"S Mirjalili","year":"2017","unstructured":"Mirjalili S, Gandomi AH (2017) Chaotic gravitational constants for the gravitational search algorithm. Appl Softw Comput 53:407\u2013419","journal-title":"Appl Softw Comput"},{"key":"346_CR27","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.apacoust.2018.03.012","volume":"137","author":"M Khishe","year":"2018","unstructured":"Khishe M, Mosavi MR, Moridi A (2018) Chaotic fractal walk trainer for sonar data set classification using multi-layer perceptron neural network and its hardware implementation. Appl Acoust 137:121\u2013139","journal-title":"Appl Acoust"},{"key":"346_CR28","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s10470-018-1366-3","volume":"100","author":"M Kaveh","year":"2019","unstructured":"Kaveh M, Khishe M, Mosavi MR (2019) Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network. Analog Integr Circ Sig Process 100:405\u2013428","journal-title":"Analog Integr Circ Sig Process"},{"key":"346_CR29","doi-asserted-by":"crossref","first-page":"134448","DOI":"10.1109\/ACCESS.2019.2942064","volume":"7","author":"H Jia","year":"2019","unstructured":"Jia H, Sun K, Song W, Peng X, Lang C, Li Y (2019) Multi-strategy emperor penguin optimizer for RGB histogram-based color satellite image segmentation using Masi entropy. IEEE Access 7:134448\u2013134474","journal-title":"IEEE Access"},{"key":"346_CR30","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.apacoust.2019.05.006","volume":"154","author":"M Khishe","year":"2019","unstructured":"Khishe M, Mosavi MR (2019) Improved whale trainer for sonar datasets classification using neural network. Appl Acoust 154:176\u2013192","journal-title":"Appl Acoust"},{"key":"346_CR31","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.oceaneng.2019.04.013","volume":"181","author":"M Khishe","year":"2019","unstructured":"Khishe M, Mohammadi H (2019) Passive sonar target classification using multi-layer perceptron trained by salp swarm algorithm. Ocean Eng 181:98\u2013108","journal-title":"Ocean Eng"},{"key":"346_CR32","doi-asserted-by":"crossref","first-page":"140862","DOI":"10.1109\/ACCESS.2020.3012686","volume":"8","author":"Y Mousavi","year":"2020","unstructured":"Mousavi Y, Alfi A, Kucukdemiral IB (2020) Enhanced fractional chaotic whale optimization algorithm for parameter identification of isolated wind-diesel power systems. IEEE Access 8:140862\u2013140875","journal-title":"IEEE Access"},{"key":"346_CR33","doi-asserted-by":"crossref","first-page":"107963","DOI":"10.1016\/j.measurement.2020.107963","volume":"164","author":"H Shokri-Ghaleh","year":"2020","unstructured":"Shokri-Ghaleh H, Alfi A, Ebadollahi S, Shahri AM, Ranjbaran S (2020) Unequal limit cuckoo optimization algorithm applied for optimal design of nonlinear field calibration problem of a triaxial accelerometer. Meas 164:107963","journal-title":"Meas"},{"key":"346_CR34","doi-asserted-by":"crossref","unstructured":"Ma S, Li D, Hu T, Xing Y, Yang Z, Nai W (2020) Huber loss function based on variable step beetle antennae search algorithm with Gaussian direction. In: 12th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), pp. 248\u2013251. IEEE, Hangzhou","DOI":"10.1109\/IHMSC49165.2020.00062"},{"key":"346_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-020-00171-2","author":"H Wang","year":"2020","unstructured":"Wang H, Wang W, Zhou X, Zhao J, Wang Y, Xiao S, Xu M (2020) Artificial bee colony algorithm based on knowledge fusion. Complex Intell Syst. https:\/\/doi.org\/10.1007\/s40747-020-00171-2","journal-title":"Complex Intell Syst"},{"key":"346_CR36","doi-asserted-by":"crossref","first-page":"113917","DOI":"10.1016\/j.eswa.2020.113917","volume":"166","author":"MH Nadimi-Shahraki","year":"2021","unstructured":"Nadimi-Shahraki MH, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166:113917","journal-title":"Expert Syst Appl"},{"key":"346_CR37","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"},{"issue":"1","key":"346_CR38","first-page":"57","volume":"17","author":"B Abed-Alguni","year":"2019","unstructured":"Abed-Alguni B (2019) Island-based cuckoo search with highly disruptive polynomial mutation. Int J Artif Intell 17(1):57\u201382","journal-title":"Int J Artif Intell"},{"issue":"3","key":"346_CR39","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1016\/j.ejor.2006.06.042","volume":"185","author":"K Deb","year":"2008","unstructured":"Deb K, Tiwari S (2008) Omni-optimizer: a generic evolutionary algorithm for single and multi-objective optimization. Eur J Oper Res 185(3):1062\u20131087","journal-title":"Eur J Oper Res"},{"key":"346_CR40","doi-asserted-by":"crossref","first-page":"113389","DOI":"10.1016\/j.eswa.2020.113389","volume":"151","author":"S Dhargupta","year":"2020","unstructured":"Dhargupta S, Ghosh M, Mirjalili S, Sarkar R (2020) Selective opposition based grey wolf optimization. Expert Syst Appl 151:113389","journal-title":"Expert Syst Appl"},{"issue":"1","key":"346_CR41","first-page":"1","volume":"1","author":"X Jiang","year":"2017","unstructured":"Jiang X, Li S (2017) BAS: beetle antennae search algorithm for optimization problems. Int J Rob Control 1(1):1","journal-title":"Int J Rob Control"},{"issue":"7","key":"346_CR42","doi-asserted-by":"crossref","first-page":"4670","DOI":"10.1109\/TII.2019.2941916","volume":"16","author":"AH Khan","year":"2020","unstructured":"Khan AH, Li S, Luo X (2020) Obstacle avoidance and tracking control of redundant robotic manipulator: an RNN-based metaheuristic approach. IEEE Trans Ind Inf 16(7):4670\u20134680","journal-title":"IEEE Trans Ind Inf"},{"key":"346_CR43","unstructured":"Available: https:\/\/wikimili.com\/en\/Antenna_(biology)#Rosalia_alpina_side.JPG"},{"key":"346_CR44","doi-asserted-by":"crossref","first-page":"105190","DOI":"10.1016\/j.knosys.2019.105190","volume":"191","author":"A Faramarzi","year":"2020","unstructured":"Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl Based Syst 191:105190","journal-title":"Knowl Based Syst"},{"key":"346_CR45","doi-asserted-by":"crossref","first-page":"103731","DOI":"10.1016\/j.engappai.2020.103731","volume":"94","author":"EH Houssein","year":"2020","unstructured":"Houssein EH, Saad MR, Hashim FA, Shaban H, Hassaballah M (2020) L\u00e9vy flight distribution: a new metaheuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 94:103731","journal-title":"Eng Appl Artif Intell"},{"issue":"4","key":"346_CR46","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1080\/00207160108805080","volume":"77","author":"JG Digalakis","year":"2001","unstructured":"Digalakis JG, Margaritis KG (2001) On benchmarking functions for genetic algorithms. Int J Comput Math 77(4):481\u2013506","journal-title":"Int J Comput Math"},{"key":"346_CR47","doi-asserted-by":"crossref","unstructured":"Awad NH, Ali MZ, Suganthan PN (2017) Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 372\u2013379. IEEE, San Sebastian","DOI":"10.1109\/CEC.2017.7969336"},{"key":"346_CR48","doi-asserted-by":"crossref","first-page":"100665","DOI":"10.1016\/j.swevo.2020.100665","volume":"54","author":"J Carrasco","year":"2020","unstructured":"Carrasco J, Garc\u00eda S, Rueda MM, Das S, Herrera F (2020) Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: practical guidelines and a critical review. Swarm Evol Comput 54:100665","journal-title":"Swarm Evol Comput"},{"key":"346_CR49","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s00366-011-0241-y","volume":"29","author":"AH Gandomi","year":"2013","unstructured":"Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng comput 29:17\u201335","journal-title":"Eng comput"},{"key":"346_CR50","unstructured":"Dua D, Graff C (2019) UCI machine learning repository. Available: http:\/\/archive.ics.uci.edu\/ml"},{"issue":"1","key":"346_CR51","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1007\/s10489-014-0645-7","volume":"43","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili S (2015) How effective is the grey wolf optimizer in training multi-layer perceptrons. Appl Intell 43(1):150\u2013161","journal-title":"Appl Intell"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-021-00346-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-021-00346-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-021-00346-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T20:42:28Z","timestamp":1724791348000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-021-00346-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,7]]},"references-count":51,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["346"],"URL":"https:\/\/doi.org\/10.1007\/s40747-021-00346-5","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,7]]},"assertion":[{"value":"26 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}