{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,21]],"date-time":"2025-06-21T14:02:53Z","timestamp":1750514573912,"version":"3.37.3"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Wireless Netw"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s11276-024-03677-6","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T19:04:39Z","timestamp":1709319879000},"page":"2675-2696","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An efficient surrogate-assisted Taguchi salp swarm algorithm and its application for intrusion detection"],"prefix":"10.1007","volume":"30","author":[{"given":"Shu-Chuan","family":"Chu","sequence":"first","affiliation":[]},{"given":"Xu","family":"Yuan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3128-9025","authenticated-orcid":false,"given":"Jeng-Shyang","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Tsu-Yang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Fengting","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"issue":"4","key":"3677_CR1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.3390\/fi9040077","volume":"9","author":"A Ali","year":"2017","unstructured":"Ali, A., Ming, Yu., Chakraborty, S., & Iram, S. (2017). A comprehensive survey on real-time applications of WSN. Future Internet, 9(4), 77.","journal-title":"Future Internet"},{"key":"3677_CR2","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s11277-020-07882-2","volume":"117","author":"S Prithi","year":"2021","unstructured":"Prithi, S., & Sumathi, S. (2021). Automata based hybrid PSO-GWO algorithm for secured energy efficient optimal routing in wireless sensor network. Wireless Personal Communications, 117, 545\u2013559.","journal-title":"Wireless Personal Communications"},{"issue":"2","key":"3677_CR3","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1109\/TSMCC.2010.2054080","volume":"41","author":"RV Kulkarni","year":"2010","unstructured":"Kulkarni, R. V., & Venayagamoorthy, G. K. (2010). Particle swarm optimization in wireless-sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics, 41(2), 262\u2013267.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics"},{"key":"3677_CR4","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.asoc.2015.10.011","volume":"38","author":"AA Aburomman","year":"2016","unstructured":"Aburomman, A. A., & Reaz, M. B. I. (2016). A novel SVM-kNN-PSO ensemble method for intrusion detection system. Applied Soft Computing, 38, 360\u2013372.","journal-title":"Applied Soft Computing"},{"issue":"5","key":"3677_CR5","doi-asserted-by":"crossref","first-page":"3719","DOI":"10.1007\/s40747-021-00498-4","volume":"8","author":"A Singh","year":"2022","unstructured":"Singh, A., Chatterjee, K., & Satapathy, S. C. (2022). An edge based hybrid intrusion detection framework for mobile edge computing. Complex & Intelligent Systems, 8(5), 3719\u20133746.","journal-title":"Complex & Intelligent Systems"},{"issue":"6","key":"3677_CR6","first-page":"155013292211043","volume":"18","author":"Z Li","year":"2022","unstructured":"Li, Z., Miao, Q., Chaudhry, S. A., & Chen, C. M. (2022). A provably secure and lightweight mutual authentication protocol in fog-enabled social Internet of vehicles. International Journal of Distributed Sensor, 18(6), 15501329221104332.","journal-title":"International Journal of Distributed Sensor"},{"key":"3677_CR7","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1007\/s11276-020-02529-3","volume":"27","author":"MR Ayyagari","year":"2021","unstructured":"Ayyagari, M. R., Kesswani, N., Kumar, M., & Kumar, K. (2021). Intrusion detection techniques in network environment: A systematic review. Wireless Networks, 27, 1269\u20131285.","journal-title":"Wireless Networks"},{"issue":"10","key":"3677_CR8","doi-asserted-by":"crossref","first-page":"3858","DOI":"10.3390\/s22103858","volume":"22","author":"TY Wu","year":"2022","unstructured":"Wu, T. Y., Meng, Q., Kumari, S., & Zhang, P. (2022). Rotating behind security: A lightweight authentication protocol based on IoT-enabled cloud computing environments. Sensors, 22(10), 3858.","journal-title":"Sensors"},{"key":"3677_CR9","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1007\/s11276-016-1197-z","volume":"23","author":"G Kumaresan","year":"2017","unstructured":"Kumaresan, G., & Adiline, M. T. (2017). Group key authentication scheme for vanet intrusion detection (GKAVIN). Wireless Networks, 23, 935\u2013945.","journal-title":"Wireless Networks"},{"issue":"5","key":"3677_CR10","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/S0167-4048(02)00514-X","volume":"21","author":"Y Liao","year":"2002","unstructured":"Liao, Y., & Vemuri, V. R. (2002). Use of k-nearest neighbor classifier for intrusion detection. Computers & Security, 21(5), 439\u2013448.","journal-title":"Computers & Security"},{"issue":"6","key":"3677_CR11","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.3390\/sym12061046","volume":"12","author":"O Almomani","year":"2020","unstructured":"Almomani, O. (2020). A feature selection model for network intrusion detection system based on PSO, GWO, FFA and GA algorithms. Symmetry, 12(6), 1046.","journal-title":"Symmetry"},{"issue":"2","key":"3677_CR12","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1007\/s11276-021-02866-x","volume":"28","author":"M Otair","year":"2022","unstructured":"Otair, M., Ibrahim, O. T., Abualigah, L., Altalhi, M., & Sumari, P. (2022). An enhanced grey wolf optimizer based particle swarm optimizer for intrusion detection system in wireless sensor networks. Wireless Networks, 28(2), 721\u2013744.","journal-title":"Wireless Networks"},{"issue":"4","key":"3677_CR13","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.3390\/s22041407","volume":"22","author":"GY Liu","year":"2022","unstructured":"Liu, G. Y., Zhao, H. Q., Fan, F., Liu, G., Xu, Q., & Nazir, S. (2022). An enhanced intrusion detection model based on improved kNN in WSNs. Sensors, 22(4), 1407.","journal-title":"Sensors"},{"issue":"3","key":"3677_CR14","first-page":"420","volume":"18","author":"MH Aghdam","year":"2016","unstructured":"Aghdam, M. H., & Kabiri, P. (2016). Feature selection for intrusion detection system using ant colony optimization. International Journal of Network Security, 18(3), 420\u2013432.","journal-title":"International Journal of Network Security"},{"issue":"4","key":"3677_CR15","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCI.2006.329691","volume":"1","author":"M Dorigo","year":"2006","unstructured":"Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28\u201339.","journal-title":"IEEE Computational Intelligence Magazine"},{"key":"3677_CR16","doi-asserted-by":"crossref","unstructured":"Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN\u201995-international conference on neural networks (Vol.\u00a04, pp. 1942\u20131948). IEEE.","DOI":"10.1109\/ICNN.1995.488968"},{"issue":"4","key":"3677_CR17","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1007\/s40747-020-00190-z","volume":"7","author":"Y Yu","year":"2021","unstructured":"Yu, Y., Xu, Y., Wang, F., Li, W., Mai, X., & Wu, H. (2021). Adsorption control of a pipeline robot based on improved PSO algorithm. Complex & Intelligent Systems, 7(4), 1797\u20131803.","journal-title":"Complex & Intelligent Systems"},{"issue":"4","key":"3677_CR18","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. Journal of Global Optimization, 11(4), 341\u2013359.","journal-title":"Journal of Global Optimization"},{"key":"3677_CR19","doi-asserted-by":"crossref","unstructured":"Shi, L., Hu, Z., Su, Q., & Miao, Y. (2022). A modified multifactorial differential evolution algorithm with optima-based transformation. Applied Intelligence, 1\u201313.","DOI":"10.1007\/s10489-022-03537-w"},{"issue":"2","key":"3677_CR20","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/BF00175354","volume":"4","author":"D Whitley","year":"1994","unstructured":"Whitley, D. (1994). A genetic algorithm tutorial. Statistics and Computing, 4(2), 65\u201385.","journal-title":"Statistics and Computing"},{"key":"3677_CR21","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, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46\u201361.","journal-title":"Advances in Engineering Software"},{"key":"3677_CR22","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","volume":"114","author":"S Mirjalili","year":"2017","unstructured":"Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163\u2013191.","journal-title":"Advances in Engineering Software"},{"issue":"15","key":"3677_CR23","doi-asserted-by":"crossref","first-page":"11195","DOI":"10.1007\/s00521-019-04629-4","volume":"32","author":"L Abualigah","year":"2020","unstructured":"Abualigah, L., Shehab, M., Alshinwan, M., & Alabool, H. (2020). Salp swarm algorithm: A comprehensive survey. Neural Computing and Applications, 32(15), 11195\u201311215.","journal-title":"Neural Computing and Applications"},{"key":"3677_CR24","doi-asserted-by":"crossref","unstructured":"Wang, C., Xu, R. Q., Ma, L., Zhao, J., Wang, L., Xie, N. G., & Cheong, K. H. (2022). An efficient salp swarm algorithm based on scale-free informed followers with self-adaption weight. Applied Intelligence, 1\u201333.","DOI":"10.1007\/s10489-022-03438-y"},{"issue":"1","key":"3677_CR25","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.amc.2009.03.090","volume":"214","author":"D Karaboga","year":"2009","unstructured":"Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1), 108\u2013132.","journal-title":"Applied Mathematics and Computation"},{"key":"3677_CR26","doi-asserted-by":"crossref","unstructured":"Jiang, Q., Cui, J., Ma, Y., Wang, L., Lin, Y., Li, X., Feng, T., & Wu, Y. (2022). Improved adaptive coding learning for artificial bee colony algorithms. Applied Intelligence, 1\u201349.","DOI":"10.1007\/s10489-021-02711-w"},{"key":"3677_CR27","doi-asserted-by":"crossref","unstructured":"Cong, C. (2015). A coverage algorithm for WSN based on the improved PSO. In 2015 International conference on intelligent transportation, big data and smart city (pp. 12\u201315). IEEE.","DOI":"10.1109\/ICITBS.2015.9"},{"issue":"8","key":"3677_CR28","doi-asserted-by":"crossref","first-page":"e4344","DOI":"10.1002\/dac.4344","volume":"33","author":"D Agrawal","year":"2020","unstructured":"Agrawal, D., Wasim Qureshi, M. H., Pincha, P., Srivastava, P., Agarwal, S., Tiwari, V., & Pandey, S. (2020). GWO-C: Grey wolf optimizer-based clustering scheme for WSNs. International Journal of Communication, Systems, 33(8), e4344.","journal-title":"International Journal of Communication, Systems"},{"issue":"1","key":"3677_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4236\/jsip.2021.121001","volume":"12","author":"J Wu","year":"2021","unstructured":"Wu, J., Xu, M., Liu, F. F., Huang, M., Ma, L. H., & Lu, Z. M. (2021). Solar wireless sensor network routing algorithm based on multi-objective particle swarm optimization. Journal of Information Hiding and Multimedia Signal Processing, 12(1), 1\u201311.","journal-title":"Journal of Information Hiding and Multimedia Signal Processing"},{"key":"3677_CR30","doi-asserted-by":"crossref","unstructured":"Chen, S., Wu, J., & Lu, Z. H. (2012). A cloud computing resource scheduling policy based on genetic algorithm with multiple fitness. In 2012 IEEE 12th international conference on computer and information technology (pp. 177\u2013184). IEEE.","DOI":"10.1109\/CIT.2012.56"},{"issue":"5","key":"3677_CR31","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1109\/TEVC.2022.3149601","volume":"26","author":"S Liu","year":"2022","unstructured":"Liu, S., Wang, H., Peng, W., & Yao, W. (2022). A surrogate-assisted evolutionary feature selection algorithm with parallel random grouping for high-dimensional classification. IEEE Transactions on Evolutionary Computation, 26(5), 1087\u20131101.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"3677_CR32","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s00500-016-2385-6","volume":"3","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 Computing, 3, 811\u2013822.","journal-title":"Soft Computing"},{"key":"3677_CR33","doi-asserted-by":"crossref","first-page":"108912","DOI":"10.1016\/j.patcog.2022.108912","volume":"132","author":"WL Al-Yaseen","year":"2022","unstructured":"Al-Yaseen, W. L., Idrees, A. K., & Almasoudy, F. H. (2022). Wrapper feature selection method based differential evolution and extreme learning machine for intrusion detection system. Pattern Recognition, 132, 108912.","journal-title":"Pattern Recognition"},{"issue":"4","key":"3677_CR34","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1109\/TEVC.2021.3065707","volume":"25","author":"F Zhang","year":"2021","unstructured":"Zhang, F., Mei, Y., Nguyen, S., Zhang, M., & Tan, K. C. (2021). Surrogate-assisted evolutionary multitask genetic programming for dynamic flexible job shop scheduling. IEEE Transactions on Evolutionary Computation, 25(4), 651\u2013665.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"3677_CR35","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.cirpj.2021.03.006","volume":"33","author":"B Denkena","year":"2021","unstructured":"Denkena, B., Schinkel, F., Pirnay, J., & Wilmsmeier, S. (2021). Quantum algorithms for process parallel flexible job shop scheduling. CIRP Journal of Manufacturing Science and Technology, 33, 100\u2013114.","journal-title":"CIRP Journal of Manufacturing Science and Technology"},{"key":"3677_CR36","doi-asserted-by":"crossref","unstructured":"Gu, Q., Wang, Q., Xiong, N. N., Jiang, S., & Chen, L. (2021). Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems. Complex & Intelligent Systems, 1\u201320.","DOI":"10.1007\/s40747-020-00249-x"},{"key":"3677_CR37","doi-asserted-by":"crossref","unstructured":"Liu, N., Pan, J. S., Chu, S. C., & Lai, T. (2022). A surrogate-assisted bi-swarm evolutionary algorithm for expensive optimization. Applied Intelligence, 1\u201324.","DOI":"10.1007\/s10489-022-04080-4"},{"issue":"2","key":"3677_CR38","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.swevo.2011.05.001","volume":"1","author":"Y Jin","year":"2011","unstructured":"Jin, Y. (2011). Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation, 1(2), 61\u201370.","journal-title":"Swarm and Evolutionary Computation"},{"key":"3677_CR39","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhao, J., Zeng, J., & Tan, Y. (2022). A two-stage infill strategy and surrogate-ensemble assisted expensive many-objective optimization. Complex & Intelligent Systems, 1\u201317.","DOI":"10.1007\/s40747-022-00751-4"},{"key":"3677_CR40","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.ins.2020.11.056","volume":"561","author":"JS Pan","year":"2021","unstructured":"Pan, J. S., Liu, N., Chu, S. C., & Lai, T. (2021). An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems. Information Sciences, 561, 304\u2013325.","journal-title":"Information Sciences"},{"key":"3677_CR41","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., Sun, C., & Jin, Y. (2018). Surrogate-assisted hierarchical particle swarm optimization. Information Sciences, 454, 59\u201372.","journal-title":"Information Sciences"},{"key":"3677_CR42","doi-asserted-by":"crossref","unstructured":"Loshchilov, I., Schoenauer, M., & Sebag, M. (2010) Comparison-based optimizers need comparison-based surrogates. In International conference on parallel problem solving from nature (pp. 364\u2013373). Springer.","DOI":"10.1007\/978-3-642-15844-5_37"},{"issue":"1","key":"3677_CR43","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TEVC.2016.2622301","volume":"22","author":"T Chugh","year":"2016","unstructured":"Chugh, T., Jin, Y., Miettinen, K., Hakanen, J., & Sindhya, K. (2016). A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization. IEEE Transactions on Evolutionary Computation, 22(1), 129\u2013142.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"3677_CR44","doi-asserted-by":"crossref","first-page":"126659","DOI":"10.1016\/j.cej.2020.126659","volume":"407","author":"S Cho","year":"2021","unstructured":"Cho, S., Kim, M., Lyu, B., & Moon, I. (2021). Optimization of an explosive waste incinerator via an artificial neural network surrogate model. Chemical Engineering Journal, 407, 126659.","journal-title":"Chemical Engineering Journal"},{"key":"3677_CR45","unstructured":"Zhou, Z., Ong, Y. S., Nguyen, M. H, & Lim, D. (2005) A study on polynomial regression and gaussian process global surrogate model in hierarchical surrogate-assisted evolutionary algorithm. In 5 IEEE congress on evolutionary computation. (pp. 2832\u20132839). IEEE."},{"key":"3677_CR46","doi-asserted-by":"crossref","unstructured":"D\u00edaz-Manr\u00edquez, A., Toscano-Pulido, G., & G\u00f3mez-Flores, W. (2011). On the selection of surrogate models in evolutionary optimization algorithms. In 2011 IEEE congress of evolutionary computation (CEC) (pp. 2155\u20132162). IEEE.","DOI":"10.1109\/CEC.2011.5949881"},{"key":"3677_CR47","doi-asserted-by":"crossref","first-page":"108736","DOI":"10.1016\/j.asoc.2022.108736","volume":"121","author":"P Hu","year":"2022","unstructured":"Hu, P., Pan, J. S., Chu, S. C., & Sun, C. (2022). Multi-surrogate assisted binary particle swarm optimization algorithm and its application for feature selection. Applied Soft Computing, 121, 108736.","journal-title":"Applied Soft Computing"},{"key":"3677_CR48","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.ins.2012.09.030","volume":"221","author":"C Sun","year":"2013","unstructured":"Sun, C., Zeng, J., Pan, J. S., Xue, S., & Jin, Y. (2013). A new fitness estimation strategy for particle swarm optimization. Information Sciences, 221, 355\u2013370.","journal-title":"Information Sciences"},{"key":"3677_CR49","doi-asserted-by":"crossref","first-page":"106939","DOI":"10.1016\/j.knosys.2021.106939","volume":"220","author":"SC Chu","year":"2021","unstructured":"Chu, S. C., Du, Z. G., Peng, Y. J., & Pan, J. S. (2021). Fuzzy hierarchical surrogate assists probabilistic particle swarm optimization for expensive high dimensional problem. Knowledge-Based Systems, 220, 106939.","journal-title":"Knowledge-Based Systems"},{"issue":"5","key":"3677_CR50","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1109\/TEVC.2002.800884","volume":"6","author":"Y Jin","year":"2002","unstructured":"Jin, Y., Olhofer, M., & Sendhoff, B. (2002). A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation, 6(5), 481\u2013494.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"3677_CR51","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1007\/s40747-021-00277-1","volume":"7","author":"Z Ren","year":"2021","unstructured":"Ren, Z., Sun, C., Tan, Y., Zhang, G., & Qin, S. (2021). A bi-stage surrogate-assisted hybrid algorithm for expensive optimization problems. Complex & Intelligent Systems, 7(3), 1391\u20131405.","journal-title":"Complex & Intelligent Systems"},{"issue":"8","key":"3677_CR52","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1029\/JB076i008p01905","volume":"76","author":"RL Hardy","year":"1971","unstructured":"Hardy, R. L. (1971). Multiquadric equations of topography and other irregular surfaces. Journal of Geophysical Research, 76(8), 1905\u20131915.","journal-title":"Journal of Geophysical Research"},{"key":"3677_CR53","doi-asserted-by":"crossref","first-page":"123863","DOI":"10.1016\/j.energy.2022.123863","volume":"251","author":"JS Pan","year":"2022","unstructured":"Pan, J. S., Tian, A. Q., Sn\u00e1\u0161el, V., Kong, L., & Chu, S. C. (2022). Maximum power point tracking and parameter estimation for multiple-photovoltaic arrays based on enhanced pigeon-inspired optimization with taguchi method. Energy, 251, 123863.","journal-title":"Energy"},{"issue":"1","key":"3677_CR54","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jocs.2013.07.004","volume":"5","author":"RG Regis","year":"2014","unstructured":"Regis, R. G. (2014). Particle swarm with radial basis function surrogates for expensive black-box optimization. Journal of Computational Science, 5(1), 12\u201313.","journal-title":"Journal of Computational Science"},{"issue":"4","key":"3677_CR55","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., Ding, J., & Zeng, J. (2017). Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems. IEEE Transactions on Evolutionary Computation, 21(4), 644\u2013660.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"3677_CR56","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the kdd cup 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications (pp. 1\u20136). IEEE.","DOI":"10.1109\/CISDA.2009.5356528"},{"issue":"4","key":"3677_CR57","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1145\/382912.382923","volume":"3","author":"J McHugh","year":"2000","unstructured":"McHugh, J. (2000). Testing intrusion detection systems: a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory. ACM Transactions on Information and System Security, 3(4), 262\u2013294.","journal-title":"ACM Transactions on Information and System Security"}],"container-title":["Wireless Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-024-03677-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11276-024-03677-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11276-024-03677-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T18:15:00Z","timestamp":1717006500000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11276-024-03677-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,1]]},"references-count":57,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["3677"],"URL":"https:\/\/doi.org\/10.1007\/s11276-024-03677-6","relation":{},"ISSN":["1022-0038","1572-8196"],"issn-type":[{"type":"print","value":"1022-0038"},{"type":"electronic","value":"1572-8196"}],"subject":[],"published":{"date-parts":[[2024,3,1]]},"assertion":[{"value":"17 January 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}