{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:40:00Z","timestamp":1760236800320,"version":"build-2065373602"},"reference-count":65,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T00:00:00Z","timestamp":1640217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012543","name":"Science and Technology Innovation Plan of Shanghai Science and Technology Commission","doi-asserted-by":"publisher","award":["19DZ1202200","20DZ1201002","2021289"],"award-info":[{"award-number":["19DZ1202200","20DZ1201002","2021289"]}],"id":[{"id":"10.13039\/100012543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004739","name":"Youth Innovation Promotion Association CAS","doi-asserted-by":"publisher","award":["19DZ1202200","20DZ1201002","2021289"],"award-info":[{"award-number":["19DZ1202200","20DZ1201002","2021289"]}],"id":[{"id":"10.13039\/501100004739","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Chemical industrial parks, which act as critical infrastructures in many cities, need to be responsive to chemical gas leakage accidents. Once a chemical gas leakage accident occurs, risks of poisoning, fire, and explosion will follow. In order to meet the primary emergency response demands in chemical gas leakage accidents, source tracking technology of chemical gas leakage has been proposed and evolved. This paper proposes a novel method, Outlier Mutation Optimization (OMO) algorithm, aimed to quickly and accurately track the source of chemical gas leakage. The OMO algorithm introduces a random walk exploration mode and, based on Swarm Intelligence (SI), increases the probability of individual mutation. Compared with other optimization algorithms, the OMO algorithm has the advantages of a wider exploration range and more convergence modes. In the algorithm test session, a series of chemical gas leakage accident application examples with random parameters are first assumed based on the Gaussian plume model; next, the qualitative experiments and analysis of the OMO algorithm are conducted, based on the application example. The test results show that the OMO algorithm with default parameters has superior comprehensive performance, including the extremely high average calculation accuracy: the optimal value, which represents the error between the final objective function value obtained by the optimization algorithm and the ideal value, reaches 2.464e-15 when the number of sensors is 16; 2.356e-13 when the number of sensors is 9; and 5.694e-23 when the number of sensors is 4. There is a satisfactory calculation time: 12.743 s\/50 times when the number of sensors is 16; 10.304 s\/50 times when the number of sensors is 9; and 8.644 s\/50 times when the number of sensors is 4. The analysis of the OMO algorithm\u2019s characteristic parameters proves the flexibility and robustness of this method. In addition, compared with other algorithms, the OMO algorithm can obtain an excellent leakage source tracing result in the application examples of 16, 9 and 4 sensors, and the accuracy exceeds the direct search algorithm, evolutionary algorithm, and other swarm intelligence algorithms.<\/jats:p>","DOI":"10.3390\/s22010071","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T21:40:21Z","timestamp":1640295621000},"page":"71","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization Algorithm"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8289-325X","authenticated-orcid":false,"given":"Zhiyu","family":"Xia","sequence":"first","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengyi","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianming","family":"Wei","sequence":"additional","affiliation":[{"name":"Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1016\/j.jclepro.2017.11.167","article-title":"Pollution control and cost analysis of wastewater treatment at industrial parks in Taihu and Haihe water basins, China","volume":"172","author":"Long","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jenvman.2006.12.045","article-title":"Strategies for sustainable development of industrial park in Ulsan, South Korea\u2014From spontaneous evolution to systematic expansion of industrial symbiosis","volume":"87","author":"Park","year":"2008","journal-title":"J. Environ. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, R., Worden, S., Cao, H., and Li, C. (2021). Public participation in environmental governance initiatives of chemical industrial parks. J. Clean. Prod., 305.","DOI":"10.1016\/j.jclepro.2021.127092"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yet-Pole, I., and Fu, J.M. (2021). Risk analysis of a cross-regional toxic chemical disaster by using the integrated mesoscale and microscale consequence analysis model. J. Loss Prev. Process. Ind., 71.","DOI":"10.1016\/j.jlp.2021.104424"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Xu, Z., Wei, J., and Teng, G. (2020). Fused CFD-interpolation model for real-time prediction of hazardous gas dispersion in emergency rescue. J. Loss Prev. Process. Ind., 63.","DOI":"10.1016\/j.jlp.2019.103988"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nooralishahi, P., Lopez, F., and Maldague, X. (2021). A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis. Appl. Sci., 11.","DOI":"10.3390\/app11041412"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, C., Reniers, G., and Khakzad, N. (2019). Integrating safety and security resources to protect chemical industrial parks from man-made domino effects: A dynamic graph approach. Reliab. Eng. Syst. Saf., 191.","DOI":"10.1016\/j.ress.2019.04.023"},{"key":"ref_8","unstructured":"(2021, June 25). Gas Leak Accidents that Made the Headlines in the Past. Available online: https:\/\/www.deccanherald.com\/national\/gas-leak-accidents-that-made-the-headlines-in-the-past-834754.html."},{"key":"ref_9","unstructured":"(2021, July 10). Acute Exposure Guideline Levels (AEGLs), Available online: https:\/\/response.restoration.noaa.gov\/oil-and-chemical-spills\/chemical-spills\/resources\/acute-exposure-guideline-levels-aegls.html."},{"key":"ref_10","unstructured":"(2021, July 10). Ammonia Results-AEGL Program, Available online: https:\/\/www.epa.gov\/aegl\/ammonia-results-aegl-programs."},{"key":"ref_11","unstructured":"(2021, July 10). Chlorine Results-AEGL Program, Available online: https:\/\/www.epa.gov\/aegl\/chlorine-results-aegl-program."},{"key":"ref_12","unstructured":"(2021, July 10). Phosgene Results-AEGL Program, Available online: https:\/\/www.epa.gov\/aegl\/phosgene-results-aegl-program."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hou, Q., and Zhu, W. (2019). An EKF-Based Method and Experimental Study for Small Leakage Detection and Location in Natural Gas Pipelines. Appl. Sci., 9.","DOI":"10.3390\/app9153193"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Huang, Y., Xiu, G., Lu, Y., Gao, S., Li, L., Chen, L., Huang, Q., Yang, Y., Che, X., and Chen, X. (2021). Application of an emission profile-based method to trace the sources of volatile organic compounds in a chemical industrial park. Sci. Total Environ., 768.","DOI":"10.1016\/j.scitotenv.2020.144694"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.atmosenv.2014.04.012","article-title":"A least-squares inversion technique for identification of a point release: Application to Fusion Field Trials 2007","volume":"92","author":"Singh","year":"2014","journal-title":"Atmos. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.atmosenv.2015.08.063","article-title":"Assimilation of concentration measurements for retrieving multiple point releases in atmosphere: A least-squares approach to inverse modelling","volume":"119","author":"Singh","year":"2015","journal-title":"Atmos. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.engappai.2018.08.005","article-title":"Source term estimation of hazardous material releases using hybrid genetic algorithm with composite cost functions","volume":"75","author":"Wang","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1016\/B978-0-444-64241-7.50388-8","article-title":"Deep Neural Networks for Source Tracking of Chemical Leaks and Improved Chemical Process Safety","volume":"Volume 44","author":"Mario","year":"2018","journal-title":"13th International Symposium on Process Systems Engineering (PSE 2018)"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1038\/s41370-020-00271-8","article-title":"Spatial identification and temporal prediction of air pollution sources using conditional bivariate probability function and time series signature","volume":"31","author":"Althuwaynee","year":"2020","journal-title":"J. Expo. Sci. Environ. Epidemiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1016\/j.apr.2020.03.012","article-title":"Modification and validation of the Gaussian plume model (GPM) to predict ammonia and particulate matter dispersion","volume":"11","author":"Yang","year":"2020","journal-title":"Atmos. Pollut. Res."},{"key":"ref_21","first-page":"1","article-title":"Development of PUFF-Gaussian dispersion model for the prediction of atmospheric distribution of particle concentration","volume":"11","author":"Lee","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ye, W., Zhou, B., Tu, Z., Xiao, X., Yan, J., Wu, T., Wu, F., Zheng, C., and Tittel, F.K. (2020). Leakage source location based on Gaussian plume diffusion model using a near-infrared sensor. Infrared Phys. Technol., 109.","DOI":"10.1016\/j.infrared.2020.103411"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/TSTE.2018.2879566","article-title":"Considering the Differentiating Health Impacts of Fuel Emissions in Optimal Generation Scheduling","volume":"11","author":"Ban","year":"2020","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pirhalla, M., Heist, D., Perry, S., Tang, W., and Brouwer, L. (2021). Simulations of dispersion through an irregular urban building array. Atmos. Environ., 258.","DOI":"10.1016\/j.atmosenv.2021.118500"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hanna, S. (2020). Britter and McQuaid (B&M) 1988 workbook nomograms for dense gas modeling applied to the Jack Rabbit II chlorine release trials. Atmos. Environ., 232.","DOI":"10.1016\/j.atmosenv.2020.117539"},{"key":"ref_26","first-page":"706","article-title":"A dynamic model of gas diffusion","volume":"13","author":"Long","year":"2006","journal-title":"Dyn. Contin. Discret. Impulsive Syst. Ser. B Appl. Algorithms"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.ijheatmasstransfer.2015.07.117","article-title":"Computational fluid dynamics study on liquefied natural gas dispersion with phase change of water","volume":"91","author":"Zhang","year":"2015","journal-title":"Int. J. Heat Mass Transf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1016\/j.psep.2021.01.048","article-title":"CFD analysis of the influence of a perimeter wall on the natural gas dispersion from an LNG pool","volume":"148","author":"Bellegoni","year":"2021","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Huh, C., Choi, S., and Lee, J.M. (2020). Concentration model for gas releases in buildings and the mitigation effect. J. Loss Prev. Process Ind., 65.","DOI":"10.1016\/j.jlp.2020.104135"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.jlp.2018.09.008","article-title":"Application and improvement of swarm intelligence optimization algorithm in gas emission source identification in atmosphere","volume":"56","author":"Ma","year":"2018","journal-title":"J. Loss Prev. Process Ind."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1364\/AO.55.000242","article-title":"Underwater gas pipeline leakage source localization by distributed fiber-optic sensing based on particle swarm optimization tuning of the support vector machine","volume":"55","author":"Huang","year":"2016","journal-title":"Appl. Opt."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","article-title":"Harris hawks optimization: Algorithm and applications","volume":"97","author":"Heidari","year":"2019","journal-title":"Future Gener. Comput. Syst. Int. J. Escience"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, R., Li, J., and Xin, Z. (2020). Locating unknown number of multi-point hazardous gas leaks using Principal Component Analysis and a Modified Genetic Algorithm. Atmos. Environ., 230.","DOI":"10.1016\/j.atmosenv.2020.117515"},{"key":"ref_34","first-page":"444","article-title":"Leakage Localization with Differential Evolution: A Closer Look on Distance Metrics","volume":"Volume 186","author":"Steffelbauer","year":"2017","journal-title":"Proceedings of the 18th International Conference on Water Distribution System Analysis (WDSA)"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.jlp.2011.01.002","article-title":"Inverse calculation approaches for source determination in hazardous chemical releases","volume":"24","author":"Zheng","year":"2011","journal-title":"J. Loss Prev. Process Ind."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1145\/321062.321069","article-title":"Direct Search Solution of Numerical and Statistical Problems","volume":"8","author":"Hooke","year":"1961","journal-title":"J. ACM"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4185","DOI":"10.1007\/s11269-015-1053-4","article-title":"An Optimization Approach to Leak Detection in Pipe Networks Using Simulated Annealing","volume":"29","author":"Huang","year":"2015","journal-title":"Water Resour. Manag."},{"key":"ref_38","first-page":"63","article-title":"Locating leaks in water distribution networks with simulated annealing and graph theory","volume":"Volume 119","author":"Ulanicki","year":"2015","journal-title":"Computing and Control for the Water Industry"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jiao, S.B., Fan, L., Wu, P.Y., Qiao, L., Wang, Y., and Xie, G. (2017, January 20\u201322). Assessment of leakage degree of underground heating primary pipe network based on chaotic simulated annealing neural network. Proceedings of the Chinese Automation Congress, Jinan, China.","DOI":"10.1109\/CAC.2017.8243837"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.sigpro.2009.06.022","article-title":"Sequential Monte Carlo methods for contour tracking of contaminant clouds","volume":"90","author":"Jaward","year":"2010","journal-title":"Signal Process."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Quy, T.B., and Kim, J.M. (2021). Real-Time Leak Detection for a Gas Pipeline Using a k-NN Classifier and Hybrid AE Features. Sensors, 21.","DOI":"10.3390\/s21020367"},{"key":"ref_42","unstructured":"Fitriana, G.F., and Nurmaini, S. (2017, January 16\u201318). The development of hybrid methods in simple swarm robots for gas leak localization. Proceedings of the 2017 International Conference on Signals and Systems, Bali, Indonesia."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ma, T., Liu, S., and Xiao, H. (2020). Multirobot searching method of natural gas leakage sources on offshore platform using ant colony optimization. Int. J. Adv. Robot. Syst., 17.","DOI":"10.1177\/1729881420959012"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.atmosenv.2018.01.056","article-title":"Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization","volume":"178","author":"Qiu","year":"2018","journal-title":"Atmos. Environ."},{"key":"ref_45","first-page":"3143","article-title":"Improved PSO-Based Method for Leak Detection and Localization in Liquid Pipelines","volume":"14","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, R., Chen, B., Qiu, S., Ma, L., Zhu, Z., Wang, Y., and Qiu, X. (2018). Hazardous Source Estimation Using an Artificial Neural Network, Particle Swarm Optimization and a Simulated Annealing Algorithm. Atmosphere, 9.","DOI":"10.3390\/atmos9040119"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lalle, Y., Abdelhafidh, M., Fourati, L.C., and Rezgui, J. (2019, January 24\u201328). A hybrid optimization algorithm based on K-means plus plus and Multi-objective Chaotic Ant Swarm Optimization for WSN in pipeline monitoring. Proceedings of the 15th IEEE International Wireless Communications and Mobile Computing Conference, Tangier, Morocco.","DOI":"10.1109\/IWCMC.2019.8766637"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.envpol.2015.07.039","article-title":"On the use of numerical modelling for near-field pollutant dispersion in urban environments\u2014A review","volume":"208","author":"Lateb","year":"2016","journal-title":"Environ. Pollut."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/S0093-6413(03)00024-7","article-title":"Pollution dispersion anaysis using the puff model with numerical flow field data","volume":"30","author":"Jung","year":"2003","journal-title":"Mech. Res. Commun."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Xu, H.-X., Li, G., Yang, S.-L., and Xu, X. (2014, January 19\u201321). Modeling and simulation of haze process based on Gaussian model. Proceedings of the 11th International Computer Conference on Wavelet Active Media Technology and Information Processing, Chengdu, China.","DOI":"10.1109\/ICCWAMTIP.2014.7073363"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1137\/10080991X","article-title":"The Mathematics of Atmospheric Dispersion Modeling","volume":"53","author":"Stockie","year":"2011","journal-title":"Siam Rev."},{"key":"ref_52","unstructured":"Masoero, M.C., and Arsie, I. (2016). Theoretical and experimental study of Gaussian Plume model in small scale systems. Energy Procedia, Proceedings of the 71st Conference of the Italian Thermal Machines Engineering Association, Politecnico Torino, Turin, Italy, 14\u201316 September 2016, Elsevier Ltd."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1109\/JIOT.2017.2777820","article-title":"Real-Time Profiling of Fine-Grained Air Quality Index Distribution Using UAV Sensing","volume":"5","author":"Yang","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Jain, R.K., Cui, Z.C., and Domen, J.K. (2016). Environmental Impact of Mining and Mineral Processing: Management, Monitoring, and Auditing Strategies, Elsevier Ltd.","DOI":"10.1016\/B978-0-12-804040-9.00006-1"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.jhazmat.2016.03.022","article-title":"Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere","volume":"311","author":"Ma","year":"2016","journal-title":"J. Hazard. Mater."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Cao, B., Cui, W., Chen, C., and Chen, Y. (2020). Development and uncertainty analysis of radionuclide atmospheric dispersion modeling codes based on Gaussian plume model. Energy, 194.","DOI":"10.1016\/j.energy.2020.116925"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.atmosenv.2018.05.058","article-title":"Atmospheric stability characterization using the Pasquill method: A critical evaluation","volume":"187","author":"Kahl","year":"2018","journal-title":"Atmos. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1504\/IJEP.2017.089420","article-title":"Validation of Gaussian plume model Aeropol against Cabauw field experiment","volume":"62","author":"Kaasik","year":"2017","journal-title":"Int. J. Environ. Pollut."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wang, H., Song, W., Zio, E., Kudreyko, A., and Zhang, Y. (2020). Remaining useful life prediction for Lithium-ion batteries using fractional Brownian motion and Fruit-fly Optimization Algorithm. Measurement, 161.","DOI":"10.1016\/j.measurement.2020.107904"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Song, W., Cattani, C., and Chi, C.H. (2020). Multifractional Brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting: An integrated approach. Energy, 194.","DOI":"10.1016\/j.energy.2019.116847"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Cavalca, D.L., and Fernandes, R.A.S. (2018, January 8\u201313). Gradient-based mechanism for PSO algorithm: A comparative study on numerical benchmarks. Proceedings of the IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI), Rio de Janeiro, Brazil.","DOI":"10.1109\/CEC.2018.8477798"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"80","DOI":"10.4103\/0377-2063.63085","article-title":"Multimodal Function Optimization Using Synchronous Bacterial Foraging Optimization Technique","volume":"56","author":"Bakwad","year":"2010","journal-title":"IETE J. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey Wolf Optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","article-title":"Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems","volume":"114","author":"Mirjalili","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The Whale Optimization Algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/71\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:51:54Z","timestamp":1760169114000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/71"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,23]]},"references-count":65,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010071"],"URL":"https:\/\/doi.org\/10.3390\/s22010071","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,12,23]]}}}