{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T22:09:37Z","timestamp":1740175777532,"version":"3.37.3"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T00:00:00Z","timestamp":1715385600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T00:00:00Z","timestamp":1715385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2023MF109"],"award-info":[{"award-number":["ZR2023MF109"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deploying static wireless sensor nodes is prone to network coverage gaps, resulting in poor network coverage. In this paper, an attempt is made to improve the network coverage by moving the locations of the nodes. A surrogate-assisted sine Phasmatodea population evolution algorithm (SASPPE) is used to evaluate the network coverage. A<jats:inline-formula><jats:alternatives><jats:tex-math>$$50 \\times 50$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mn>50<\/mml:mn><mml:mo>\u00d7<\/mml:mo><mml:mn>50<\/mml:mn><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>hill simulation environment was tested for the number of nodes of 30 and 40 and radii of 3, 5 and 7, respectively. The results show that the SASPPE algorithm has the highest coverage, which can be up to 23.624% higher than the PPE algorithm, and up to 5.196% higher than the PPE algorithm, ceteris paribus. The SASPPE algorithm mixes the GSAM with LSAMs, which balances the computational cost of the algorithm and the algorithm\u2019s ability to find optimal results. The use of hierarchical clustering enhances the stable type of the LSAMs. In addition, LSAMs are easy to fall into local optimality when they are modeled with local data, and the use of sine Phasmatodea population evolution algorithm (Sine-PPE) for searching in LSAMs alleviates the time for the algorithm to fall into local optimality. On 30D, 50D, and 100D, the proposed algorithm was tested by 7 test functions. The results show that the algorithm has significant advantages on most functions.<\/jats:p>","DOI":"10.1007\/s40747-024-01460-w","type":"journal-article","created":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T16:01:36Z","timestamp":1715443296000},"page":"5545-5568","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Surrogate-assisted sine Phasmatodea population evolution algorithm applied to 3D coverage of mobile nodes"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2117-0618","authenticated-orcid":false,"given":"Shu-Chuan","family":"Chu","sequence":"first","affiliation":[]},{"given":"LuLu","family":"Liang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3128-9025","authenticated-orcid":false,"given":"Jeng-Shyang","family":"Pan","sequence":"additional","affiliation":[]},{"given":"LingPing","family":"Kong","sequence":"additional","affiliation":[]},{"given":"Jia","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,11]]},"reference":[{"issue":"3","key":"1460_CR1","doi-asserted-by":"crossref","first-page":"5791","DOI":"10.1109\/JIOT.2019.2905743","volume":"6","author":"Y Sun","year":"2019","unstructured":"Sun Y, Zhang L, Feng G et al (2019) Ant colony optimization. IEEE Internet Things J 6(3):5791\u20135802","journal-title":"IEEE Internet Things J"},{"doi-asserted-by":"crossref","unstructured":"Jaiswal SK, Dwivedi AK (2023) A security and application of wireless sensor network: a comprehensive study. In: 2023 International conference on IoT, communication and automation technology (ICICAT), pp 1\u20135","key":"1460_CR2","DOI":"10.1109\/ICICAT57735.2023.10263644"},{"issue":"5","key":"1460_CR3","first-page":"2723","volume":"16","author":"A Boubrima","year":"2017","unstructured":"Boubrima A, Bechkit W, Rivano H (2017) Optimal WSN deployment models for air pollution monitoring. IEEE Internet Things J 16(5):2723\u20132735","journal-title":"IEEE Internet Things J"},{"key":"1460_CR4","doi-asserted-by":"crossref","first-page":"18424","DOI":"10.1109\/ACCESS.2021.3053594","volume":"9","author":"L Cao","year":"2021","unstructured":"Cao L, Yue Y, Cai Y, Zhang Y (2021) A novel coverage optimization strategy for heterogeneous wireless sensor networks based on connectivity and reliability. IEEE Access 9:18424\u201318442","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Ayadi H, Zouinkhi A, Boussaid B et\u00a0al (2016) Energy efficiency in WSN: IEEE 802.15.4. In: 2016 17th International conference on sciences and techniques of automatic control and computer engineering (STA), Sousse, pp 766\u2013771","key":"1460_CR5","DOI":"10.1109\/STA.2016.7952060"},{"doi-asserted-by":"crossref","unstructured":"Narayan Vipul, Daniel AK, Chaturvedi Pooja (2023) E-FEERP: enhanced fuzzy based energy efficient routing protocol for wireless sensor network. Wirel Pers Commun 131:371\u2013398","key":"1460_CR6","DOI":"10.1007\/s11277-023-10434-z"},{"key":"1460_CR7","doi-asserted-by":"crossref","first-page":"26971","DOI":"10.1109\/ACCESS.2018.2833632","volume":"6","author":"A Tripathi","year":"2018","unstructured":"Tripathi A, Gupta HP, Dutta T et al (2018) Coverage and connectivity in WSNS: a survey, research issues and challenges. IEEE Access 6:26971\u201326992","journal-title":"IEEE Access"},{"key":"1460_CR8","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1007\/s11277-019-06903-z","volume":"111","author":"J Amutha","year":"2020","unstructured":"Amutha J, Sharma S, Nagar J (2020) WSN strategies based on sensors, deployment, sensing models, coverage and energy efficiency: review, approaches and open issues. Wirel Pers Commun 111:1089\u20131115","journal-title":"Wirel Pers Commun"},{"issue":"1","key":"1460_CR9","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/JCN.2019.000005","volume":"21","author":"R Elhabyan","year":"2019","unstructured":"Elhabyan R, Shi W, St-Hilaire M (2019) Coverage protocols for wireless sensor networks: review and future directions. J Commun Netw 21(1):45\u201360","journal-title":"J Commun Netw"},{"issue":"5","key":"1460_CR10","doi-asserted-by":"crossref","first-page":"5941","DOI":"10.1007\/s12652-020-02195-5","volume":"14","author":"R Yarinezhad","year":"2023","unstructured":"Yarinezhad R, Hashemi SN (2023) A sensor deployment approach for target coverage problem in wireless sensor networks. J Ambient Intell Humaniz Comput 14(5):5941\u20135956","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"3","key":"1460_CR11","doi-asserted-by":"crossref","first-page":"569","DOI":"10.2298\/CSIS180103023W","volume":"15","author":"L Wang","year":"2018","unstructured":"Wang L, Wu W, Qi J, Jia Z (2018) Wireless sensor network coverage optimization based on whale group algorithm. Comput Sci Inf Syst 15(3):569\u2013583","journal-title":"Comput Sci Inf Syst"},{"key":"1460_CR12","doi-asserted-by":"crossref","first-page":"28940","DOI":"10.1109\/ACCESS.2019.2902072","volume":"7","author":"M Farsi","year":"2019","unstructured":"Farsi M, Elhosseini MA, Badawy M et al (2019) Deployment techniques in wireless sensor networks, coverage and connectivity: a survey. IEEE Access 7:28940\u201328954","journal-title":"IEEE Access"},{"key":"1460_CR13","doi-asserted-by":"crossref","first-page":"180258","DOI":"10.1109\/ACCESS.2019.2952644","volume":"7","author":"AK Sangaiah","year":"2019","unstructured":"Sangaiah AK, Sadeghilalimi M, Hosseinabadi AAR, Zhang W (2019) Energy consumption in point-coverage wireless sensor networks via bat algorithm. IEEE Access 7:180258\u2013180269","journal-title":"IEEE Access"},{"key":"1460_CR14","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.eswa.2017.09.008","volume":"92","author":"M Elhoseny","year":"2018","unstructured":"Elhoseny M, Tharwat A, Yuan X, Hassanien AE (2018) Optimizing k-coverage of mobile WSNs. Expert Syst Appl 92:142\u2013153","journal-title":"Expert Syst Appl"},{"key":"1460_CR15","doi-asserted-by":"crossref","first-page":"887","DOI":"10.3934\/dcdss.2019059","volume":"124 &5","author":"M Chen","year":"2019","unstructured":"Chen M, Xu A, Wang X (2019) Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm. Discrete Contin Dyn Syst S 124 &5:887\u2013900","journal-title":"Discrete Contin Dyn Syst S"},{"key":"1460_CR16","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.comcom.2017.06.010","volume":"110","author":"SM Mohamed","year":"2017","unstructured":"Mohamed SM, Hamza HS, Saroit IA (2017) Coverage in mobile wireless sensor networks (M-WSN): a survey. Comput Commun 110:133\u2013150","journal-title":"Comput Commun"},{"issue":"10","key":"1460_CR17","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/JAS.2021.1004129","volume":"8","author":"J Tang","year":"2021","unstructured":"Tang J, Liu G, Pan Q (2021) A review on representative swarm intelligence algorithms for solving optimization problems: applications and trends. IEEE\/CAA J Autom Sin 8(10):1627\u20131643","journal-title":"IEEE\/CAA J Autom Sin"},{"issue":"9","key":"1460_CR18","doi-asserted-by":"crossref","first-page":"2812","DOI":"10.1109\/JSEN.2016.2523061","volume":"16","author":"CW Tsai","year":"2016","unstructured":"Tsai CW, Hong TP, Shiu GN (2016) Metaheuristics for the lifetime of WSN: a review. IEEE Sens J 16(9):2812\u20132831","journal-title":"IEEE Sens J"},{"issue":"3","key":"1460_CR19","first-page":"145","volume":"16","author":"S Kaur","year":"2018","unstructured":"Kaur S, Mahajan R (2018) Hybrid meta-heuristic optimization based energy efficient protocol for wireless sensor networks. Egypt Inf J 16(3):145\u2013150","journal-title":"Egypt Inf J"},{"key":"1460_CR20","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":"Grey wolf optimizer. Adv Eng Softw"},{"key":"1460_CR21","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"},{"doi-asserted-by":"crossref","unstructured":"Song PC, Chu SC, Pan JS, Yang H (2020) Phasmatodea population evolution algorithm and its application in length-changeable incremental extreme learning machine. In: 2020 2nd International conference on industrial artificial intelligence (IAI), Shenyang, pp 1\u20135","key":"1460_CR22","DOI":"10.1109\/IAI50351.2020.9262236"},{"doi-asserted-by":"crossref","unstructured":"Wu TY, Li HN, Chu SC (2023) CPPE: an improved Phasmatodea population evolution algorithm with chaotic maps. Mathematics 11(9):1977","key":"1460_CR23","DOI":"10.3390\/math11091977"},{"issue":"6","key":"1460_CR24","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.53106\/160792642021112206001","volume":"22","author":"PC Song","year":"2021","unstructured":"Song PC, Chu SC, Pan JS, Yang H (2021) The Phasmatodea population evolution algorithm and its application in 5G heterogeneous network downlink power allocation problem. J Internet Technol 22(6):1199\u20131213","journal-title":"J Internet Technol"},{"issue":"1","key":"1460_CR25","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","volume":"267","author":"JH Holland","year":"1992","unstructured":"Holland JH (1992) Genetic algorithms. Sci Am 267(1):66\u201373","journal-title":"Sci Am"},{"doi-asserted-by":"crossref","unstructured":"Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN\u201995-international conference on neural networks. IEEE, pp 1942\u20131948","key":"1460_CR26","DOI":"10.1109\/ICNN.1995.488968"},{"key":"1460_CR27","volume":"135","author":"RJ Kuo","year":"2023","unstructured":"Kuo RJ, Li SS (2023) Applying particle swarm optimization algorithm-based collaborative filtering recommender system considering rating and review. Appl Soft Comput 135:110038","journal-title":"Appl Soft Comput"},{"issue":"3","key":"1460_CR28","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1007\/s40747-021-00292-2","volume":"7","author":"S Dereli","year":"2021","unstructured":"Dereli S, K\u00f6ker R (2021) Strengthening the PSO algorithm with a new technique inspired by the golf game and solving the complex engineering problem. Complex Intell Syst 7(3):1515\u20131526","journal-title":"Complex Intell Syst"},{"doi-asserted-by":"crossref","unstructured":"Price KV (2013) Differential evolution. Handbook of optimization: from classical to modern approach. Springer, Berlin, pp 187\u2013214","key":"1460_CR29","DOI":"10.1007\/978-3-642-30504-7_8"},{"key":"1460_CR30","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110697","volume":"275","author":"EH Houssein","year":"2023","unstructured":"Houssein EH, Hosney ME, Oliva D et al (2023) An efficient discrete rat swarm optimizer for global optimization and feature selection in chemoinformatics. Knowl Based Syst 275:110697","journal-title":"Knowl Based Syst"},{"doi-asserted-by":"crossref","unstructured":"Liang JH, Oh C, Mathew M et\u00a0al (2018) Machine learning-based restart policy for CDCL sat solvers. In: Theory and applications of satisfiability testing\u2013SAT 2018: 21st international conference, SAT 2018, Held as part of the federated logic conference, FloC 2018, Oxford, UK, July 9\u201312, 2018, Proceedings 21, pp 94\u2013110","key":"1460_CR31","DOI":"10.1007\/978-3-319-94144-8_6"},{"issue":"11","key":"1460_CR32","doi-asserted-by":"crossref","first-page":"6723","DOI":"10.1109\/TSMC.2020.2963943","volume":"51","author":"L Ma","year":"2020","unstructured":"Ma L, Cheng S, Shi Y (2020) Enhancing learning efficiency of brain storm optimization via orthogonal learning design. IEEE Trans Syst Man Cybern Syst 51(11):6723\u20136742","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"1460_CR33","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.knosys.2015.01.010","volume":"80","author":"J Lu","year":"2015","unstructured":"Lu J, Behbood V, Hao P et al (2015) Transfer learning using computational intelligence: a survey. Knowl Based Syst 80:14\u201323","journal-title":"Knowl Based Syst"},{"doi-asserted-by":"crossref","unstructured":"Liang LL, Du ZG, Shieh CS et\u00a0al (2022) A new PPE algorithm based on parallel communication strategy. In: Advances in intelligent information hiding and multimedia signal processing: proceeding of the IIH-MSP 2021 and FITAT 2021, Kaohsiung, pp 289\u2013298","key":"1460_CR34","DOI":"10.1007\/978-981-19-1057-9_28"},{"doi-asserted-by":"crossref","unstructured":"Zhu Y, Yan F, Pan JS et\u00a0al (2022) Mutigroup-based Phasmatodea population evolution algorithm with multistrategy for IoT electric bus scheduling. In: Wireless communications and mobile computing 2022","key":"1460_CR35","DOI":"10.1155\/2022\/1500646"},{"issue":"6","key":"1460_CR36","doi-asserted-by":"crossref","first-page":"2322","DOI":"10.1109\/TCSI.2018.2888688","volume":"66","author":"C Li","year":"2019","unstructured":"Li C, Feng B, Li S et al (2019) Dynamic analysis of digital chaotic maps via state-mapping networks. IEEE Trans Circ Syst I Regul Pap 66(6):2322\u20132335","journal-title":"IEEE Trans Circ Syst I Regul Pap"},{"issue":"6","key":"1460_CR37","doi-asserted-by":"crossref","first-page":"3713","DOI":"10.1109\/TSMC.2019.2932616","volume":"51","author":"Z Hua","year":"2019","unstructured":"Hua Z, Zhou Y (2019) Exponential chaotic model for generating robust chaos. IEEE Trans Syst Man Cybern Syst 51(6):3713\u20133724","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"1460_CR38","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1007\/s10462-019-09704-9","volume":"53","author":"E Varol Altay","year":"2019","unstructured":"Varol Altay E, Alatas B (2019) Bird swarm algorithms with chaotic mapping. Artif Intell Rev 53:1373\u20131414","journal-title":"Artif Intell Rev"},{"issue":"9","key":"1460_CR39","doi-asserted-by":"crossref","first-page":"1885","DOI":"10.2514\/1.J051354","volume":"50","author":"ZH Han","year":"2012","unstructured":"Han ZH (2012) Hierarchical kriging model for variable-fidelity surrogate modeling. AIAA J 50(9):1885\u20131896","journal-title":"AIAA J"},{"key":"1460_CR40","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.ins.2018.04.062","volume":"454","author":"H Yu","year":"2018","unstructured":"Yu H, Tan Y, Zeng J et al (2018) Surrogate-assisted hierarchical particle swarm optimization. Inf Sci 454:59\u201372","journal-title":"Inf Sci"},{"key":"1460_CR41","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1007\/s00158-019-02413-5","volume":"61","author":"K Cheng","year":"2014","unstructured":"Cheng K, Lu Z, Ling C, Zhou S (2014) Surrogate-assisted global sensitivity analysis: an overview. Struct Multidiscip Optim 61:1187\u20131213","journal-title":"Struct Multidiscip Optim"},{"key":"1460_CR42","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.compchemeng.2014.05.021","volume":"68","author":"J Eason","year":"2014","unstructured":"Eason J, Cremaschi S (2014) Adaptive sequential sampling for surrogate model generation with artificial neural networks. Comput Chem Eng 68:220\u2013232","journal-title":"Comput Chem Eng"},{"key":"1460_CR43","doi-asserted-by":"crossref","first-page":"220027","DOI":"10.1109\/ACCESS.2020.3042834","volume":"8","author":"M Tahkola","year":"2023","unstructured":"Tahkola M, Ker\u00e4nen J, Sedov D et al (2023) Surrogate modeling of electrical machine torque using artificial neural networks. IEEE Access 8:220027\u2013220045","journal-title":"IEEE Access"},{"issue":"2","key":"1460_CR44","doi-asserted-by":"crossref","first-page":"317","DOI":"10.3390\/e25020317","volume":"25","author":"JS Pan","year":"2023","unstructured":"Pan JS, Zhang LG, Chu SC et al (2023) Surrogate-assisted hybrid meta-heuristic algorithm with an add-point strategy for a wireless sensor network. Entropy 25(2):317\u20132023","journal-title":"Entropy"},{"doi-asserted-by":"crossref","unstructured":"Razavi S, Tolson BA, Burn DH (2012) Review of surrogate modeling in water resources. Water Resour Res 48(7):2012","key":"1460_CR45","DOI":"10.1029\/2011WR011527"},{"issue":"4","key":"1460_CR46","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1109\/TEVC.2017.2675628","volume":"21","author":"C Sun","year":"2017","unstructured":"Sun C, Jin Y, Cheng R et al (2017) Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems. IEEE Trans Evol Comput 21(4):644\u2013660","journal-title":"IEEE Trans Evol Comput"},{"key":"1460_CR47","volume":"75","author":"M Wu","year":"2022","unstructured":"Wu M, Wang L, Xu J et al (2022) Adaptive surrogate-assisted multi-objective evolutionary algorithm using an efficient infill technique. Swarm Evol Comput 75:101170","journal-title":"Swarm Evol Comput"},{"issue":"1","key":"1460_CR48","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jocs.2013.07.004","volume":"5","author":"RG Regis","year":"2014","unstructured":"Regis RG (2014) Particle swarm with radial basis function surrogates for expensive black-box optimization. J Comput Sci 5(1):12\u201323","journal-title":"J Comput Sci"},{"issue":"2","key":"1460_CR49","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s11276-022-03168-6","volume":"29","author":"LL Liang","year":"2023","unstructured":"Liang LL, Chu SC, Du ZG, Pan JS (2023) Surrogate-assisted Phasmatodea population evolution algorithm applied to wireless sensor networks. Wirel Netw 29(2):673-675","journal-title":"Wirel Netw"},{"key":"1460_CR50","volume":"223","author":"Q Gu","year":"2021","unstructured":"Gu Q, Wang Q, Li X, Li X (2021) A surrogate-assisted multi-objective particle swarm optimization of expensive constrained combinatorial optimization problems. Knowl Based Syst 223:107049","journal-title":"Knowl Based Syst"},{"issue":"4","key":"1460_CR51","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1109\/TEVC.2021.3064835","volume":"25","author":"X Ji","year":"2021","unstructured":"Ji X, Zhang Y, Gong D, Sun X (2021) Dual-surrogate-assisted cooperative particle swarm optimization for expensive multimodal problems. IEEE Trans Evol Comput 25(4):794\u2013808","journal-title":"IEEE Trans Evol Comput"},{"key":"1460_CR52","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.chaos.2019.07.011","volume":"126","author":"A Altan","year":"2019","unstructured":"Altan A, Karasu S, Bekiros S (2019) Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques. Chaos Solitons Fractals 126:325\u2013336","journal-title":"Chaos Solitons Fractals"},{"key":"1460_CR53","doi-asserted-by":"crossref","first-page":"10124","DOI":"10.1016\/j.rineng.2023.101274","volume":"19","author":"S Syama","year":"2023","unstructured":"Syama S, Ramprabhakar J, Anand R et al (2023) A hybrid extreme learning machine model with L\u00e9vy flight chaotic whale optimization algorithm for wind speed forecasting. Results Eng 19:10124","journal-title":"Results Eng"},{"issue":"8","key":"1460_CR54","doi-asserted-by":"crossref","first-page":"8219","DOI":"10.1007\/s10462-022-10366-3","volume":"56","author":"XC Ran","year":"2023","unstructured":"Ran XC, Xi Y, Lu YG et al (2023) Comprehensive survey on hierarchical clustering algorithms and the recent developments. Artif Intell Rev 56(8):8219\u20138264","journal-title":"Artif Intell Rev"},{"unstructured":"Brzozowski \u0141, Siudem G, Gagolewski M (2023) Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering. arXiv preprint 56(8):8219\u20138264. arXiv:2303.12212","key":"1460_CR55"},{"key":"1460_CR56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10489-021-02624-8","volume":"52","author":"A Dogan","year":"2022","unstructured":"Dogan A, Birant D (2022) K-centroid link: a novel hierarchical clustering linkage method. Appl Intell 52:1\u201324","journal-title":"Appl Intell"},{"issue":"2005","key":"1460_CR57","first-page":"2005","volume":"2005005","author":"PN Suganthan","year":"2005","unstructured":"Suganthan PN, Hansen N, Liang JJ et al (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. KanGAL Rep 2005005(2005):2005\u20132005","journal-title":"KanGAL Rep"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01460-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01460-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01460-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T19:34:17Z","timestamp":1731958457000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01460-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,11]]},"references-count":57,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["1460"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01460-w","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2024,5,11]]},"assertion":[{"value":"29 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2024","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"}}]}}