{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T21:48:08Z","timestamp":1767649688834,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,8,28]],"date-time":"2017-08-28T00:00:00Z","timestamp":1503878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In this paper, a comparison among Particle swarm optimization (PSO), Bee Colony Optimization (BCO) and the Bat Algorithm (BA) is presented. In addition, a modification to the main parameters of each algorithm through an interval type-2 fuzzy logic system is presented. The main aim of using interval type-2 fuzzy systems is providing dynamic parameter adaptation to the algorithms. These algorithms (original and modified versions) are compared with the design of fuzzy systems used for controlling the trajectory of an autonomous mobile robot. Simulation results reveal that PSO algorithm outperforms the results of the BCO and BA algorithms.<\/jats:p>","DOI":"10.3390\/a10030101","type":"journal-article","created":{"date-parts":[[2017,8,28]],"date-time":"2017-08-28T12:08:37Z","timestamp":1503922117000},"page":"101","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers"],"prefix":"10.3390","volume":"10","author":[{"given":"Frumen","family":"Olivas","sequence":"first","affiliation":[{"name":"Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22379, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6771-8007","authenticated-orcid":false,"given":"Leticia","family":"Amador-Angulo","sequence":"additional","affiliation":[{"name":"Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22379, Mexico"}]},{"given":"Jonathan","family":"Perez","sequence":"additional","affiliation":[{"name":"Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22379, Mexico"}]},{"given":"Camilo","family":"Caraveo","sequence":"additional","affiliation":[{"name":"Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22379, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0159-0407","authenticated-orcid":false,"given":"Fevrier","family":"Valdez","sequence":"additional","affiliation":[{"name":"Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22379, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7385-5689","authenticated-orcid":false,"given":"Oscar","family":"Castillo","sequence":"additional","affiliation":[{"name":"Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22379, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2017,8,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, X.S. (2010). A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer.","DOI":"10.1007\/978-3-642-12538-6_6"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.engappai.2011.09.018","article-title":"PID-type fuzzy logic controller tuning based on particle swarm optimization","volume":"25","author":"Ayadi","year":"2012","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tran, H.-K., and Chiou, J.-S. (2016). PSO-based algorithm applied to quadcopter micro air vehicle controller design. Micromachines, 7.","DOI":"10.3390\/mi7090168"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1016\/j.ijepes.2014.08.021","article-title":"A novel hybrid PSO-PS optimized fuzzy PI controller for AGC in multi area interconnected power systems","volume":"64","author":"Sahu","year":"2015","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_5","unstructured":"Azadani, H.N., and Torkzadeh, R. (2013, January 27\u201329). Design of GA optimized fuzzy logic-based PID controller for the two area non-reheat thermal power system. Proceedings of the 13th Iranian Conference on Fuzzy Systems (IFSC), Qazvin, Iran."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13624","DOI":"10.3182\/20110828-6-IT-1002.00938","article-title":"Gravitational search algorithms in fuzzy control systems tuning","volume":"44","author":"Precup","year":"2011","journal-title":"IFAC Proc. Vol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1016\/j.eswa.2013.07.110","article-title":"Novel adaptive charged system search algorithm for optimal tuning of fuzzy controllers","volume":"41","author":"Precup","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.ins.2014.09.040","article-title":"New approach using ant colony optimization with ant set partition for fuzzy control design applied to the ball and beam system","volume":"294","author":"Castillo","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.asoc.2014.12.002","article-title":"A new approach for dynamic fuzzy logic parameter tuning in ant colony optimization and its application in fuzzy control of a mobile robot","volume":"28","author":"Castillo","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.amc.2015.11.001","article-title":"A hybrid PSO-GA algorithm for constrained optimization problems","volume":"274","author":"Garg","year":"2016","journal-title":"Appl. Math. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"777","DOI":"10.3934\/jimo.2014.10.777","article-title":"Solving structural engineering design optimization problems using an artificial bee colony algorithm","volume":"10","author":"Garg","year":"2014","journal-title":"J. Ind. Manag. Optim."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2015.05.001","article-title":"An efficient biogeography based optimization algorithm for solving reliability optimization problems","volume":"24","author":"Garg","year":"2015","journal-title":"Swarm Evolut. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Garg, H. (2015). A hybrid GA-GSA algorithm for optimizing the performance of an industrial system by utilizing uncertain data. Handbook of Research on Artificial Intelligence Techniques and Algorithms, IGI Global.","DOI":"10.4018\/978-1-4666-7258-1.ch020"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1007\/s40430-016-0552-4","article-title":"Performance analysis of an industrial system using soft computing based hybridized technique","volume":"39","author":"Garg","year":"2017","journal-title":"J. Braz. Soc. Mech. Sci. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1007\/s10489-016-0869-9","article-title":"Distance measures between type-2 intuitionistic fuzzy sets and their application to multicriteria decision-making process","volume":"46","author":"Singh","year":"2017","journal-title":"Appl. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Amador-Angulo, L., and Castillo, O. (2016). A new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers. Soft Comput., 1\u201324.","DOI":"10.1007\/s00500-016-2354-0"},{"key":"ref_17","unstructured":"Banu, U.S. (2013, January 19\u201321). Implementation of Fractional Order PID Controller for Three Interacting Tank Process Optimally Tuned Using Bee Colony Optimization. Proceedings of the International Conference on Swarm, Evolutionary, and Memetic Computing, Chennai, India."},{"key":"ref_18","first-page":"6049","article-title":"A Bee Colony Optimization based-fuzzy logic-PID control design of electrolyzer for microgrid stabilization","volume":"8","author":"Chaiyatham","year":"2012","journal-title":"Int. J. Innov. Comput. Inf. Control"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.trc.2017.02.006","article-title":"Area-wide urban traffic control: A Bee Colony Optimization approach","volume":"77","author":"Jovanovic","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rajeswari, M., Amudhavel, J., Pothula, S., and Dhavachelvan, P. (2017). Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem. Comput. Intell. Neurosci., 2017.","DOI":"10.1155\/2017\/6563498"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3229","DOI":"10.1007\/s11227-017-2015-9","article-title":"Efficient cooperative relaying in flying ad hoc networks using fuzzy-bee colony optimization","volume":"73","author":"Sharma","year":"2017","journal-title":"J. Supercomput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wong, L.P., Low, M.Y.H., and Chong, C.S. (2008, January 13\u201315). A bee colony optimization algorithm for traveling salesman problem. Proceedings of the Second Asia International Conference on Modeling Simulation, Kuala Lumpur, Malaysia.","DOI":"10.1109\/AMS.2008.27"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.trc.2017.03.019","article-title":"Solving the gate assignment problem through the Fuzzy Bee Colony Optimization","volume":"80","author":"Marinelli","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1080\/03081060.2017.1314498","article-title":"Pre-timed control for an under-saturated and over-saturated isolated intersection: A Bee Colony Optimization approach","volume":"40","author":"Jovanovic","year":"2017","journal-title":"Transp. Plan. Technol."},{"key":"ref_25","first-page":"273","article-title":"Routing and wavelength assignment in all-optical networks based on the bee colony optimization","volume":"20","author":"Markovic","year":"2007","journal-title":"AI Commun."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s10346-016-0711-9","article-title":"Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization","volume":"14","author":"TienBui","year":"2017","journal-title":"Landslides"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s12647-013-0081-x","article-title":"Reliability, availability and maintainability analysis of industrial systems using PSO and fuzzy methodology","volume":"29","author":"Garg","year":"2014","journal-title":"MAPAN"},{"key":"ref_28","first-page":"14","article-title":"An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm","volume":"4","author":"Garg","year":"2015","journal-title":"Beni-Suef Univ. J. Basic Appl. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.cie.2012.09.015","article-title":"Multi-objective reliability-redundancy allocation problem using particle swarm optimization","volume":"64","author":"Garg","year":"2013","journal-title":"Comput. Ind. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2961","DOI":"10.1016\/j.cor.2013.07.014","article-title":"An efficient two phase approach for solving reliability\u2013redundancy allocation problem using artificial bee colony technique","volume":"40","author":"Garg","year":"2013","journal-title":"Comput. Oper. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3157","DOI":"10.1016\/j.eswa.2013.11.014","article-title":"Intuitionistic fuzzy optimization technique for solving multi-objective reliability optimization problems in interval environment","volume":"41","author":"Garg","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chatterjee, A., Biswas, M., Maji, D., Jana, D., Brojabasi, S., Sarkar, G., and Das, S. (2017, January 9\u201311). Discrete Wavelet Transform based VI image fusion with Artificial Bee Colony Optimization. Proceedings of the 7th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC.2017.7868491"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4609","DOI":"10.1016\/j.eswa.2013.01.063","article-title":"Empirical study of the Bee Colony Optimization (BCO) algorithm","volume":"40","author":"Nikolic","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.asoc.2016.02.033","article-title":"Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation","volume":"43","author":"Caraveo","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Amador-Angulo, L., Mendoza, O., Castro, J.R., Rodr\u00edguez-D\u00edaz, A., Melin, P., and Castillo, O. (2016). Fuzzy sets in dynamic adaptation of parameters of a bee colony optimization for controlling the trajectory of an autonomous mobile robot. Sensors, 16.","DOI":"10.3390\/s16091458"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1007\/s13042-015-0357-2","article-title":"An improved artificial bee colony algorithm for solving constrained optimization problems","volume":"8","author":"Liang","year":"2017","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.swevo.2016.12.005","article-title":"A survey of swarm intelligence for dynamic optimization: Algorithms and applications","volume":"33","author":"Mavrovouniotis","year":"2017","journal-title":"Swarm Evolut. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, J., Valdez, F., and Castillo, O. (2017). Modification of the bat algorithm using type-2 fuzzy logic for dynamical parameter adaptation. Nature-Inspired Design of Hybrid Intelligent Systems, Springer.","DOI":"10.1007\/978-3-319-47054-2_23"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.eswa.2016.10.050","article-title":"New directional bat algorithm for continuous optimization problems","volume":"69","author":"Chakri","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1108\/02644401211235834","article-title":"Bat algorithm: A novel approach for global engineering optimization","volume":"29","author":"Yang","year":"2012","journal-title":"Eng. Comput."},{"key":"ref_41","first-page":"137","article-title":"A Combined Ant Colony Optimization and Simulated Annealing Algorithm to Assess Stability and Fault-Proneness of Classes Based on Internal Software Quality Attributes","volume":"14","author":"Azar","year":"2016","journal-title":"Int. J. Artif. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"153","DOI":"10.12700\/APH.9.6.2012.6.10","article-title":"A hybrid algorithm for parameter tuning in fuzzy model identification","volume":"9","author":"Papp","year":"2012","journal-title":"Acta Polytech. Hung."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"8288","DOI":"10.1016\/j.eswa.2012.01.165","article-title":"Iterative performance improvement of fuzzy control systems for three tank systems","volume":"39","author":"Precup","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_44","first-page":"1","article-title":"Optimizing shift scheduling for tank trucks using an effective stochastic variable neighbourhood approach","volume":"14","author":"Solos","year":"2016","journal-title":"Int. J. Artif. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Monson, C.K., and Seppi, K.D. (2006, January 8\u201312). Adaptive diversity in PSO. Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, Seattle, WA, USA.","DOI":"10.1145\/1143997.1144006"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1007\/s00500-014-1567-3","article-title":"Dynamic parameter adaptation in particle swarm optimization using interval type-2 fuzzy logic","volume":"20","author":"Olivas","year":"2016","journal-title":"Soft Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3196","DOI":"10.1016\/j.eswa.2012.12.033","article-title":"Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic","volume":"40","author":"Melin","year":"2013","journal-title":"Elsevier Exp. Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1016\/j.asoc.2011.12.010","article-title":"A review on the design and optimization of interval type-2 fuzzy controllers","volume":"12","author":"Castillo","year":"2012","journal-title":"Appl. Soft Comput."},{"key":"ref_49","unstructured":"Shi, Y., and Eberhart, R.C. (2001, January 27\u201330). Fuzzy adaptive particle swarm optimization. Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, Korea."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Olivas, F., Valdez, F., and Castillo, O. (2014, January 24\u201326). An interval type-2 fuzzy logic system for dynamic parameter adaptation in particle swarm optimization. Proceedings of the 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW), Boston, MA, USA.","DOI":"10.1109\/NORBERT.2014.6893881"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.ins.2010.02.022","article-title":"Comparative study of bio inspired algorithms applied to the optimization of type-1 andtype-2 fuzzy controllers for an autonomous mobile robot","volume":"192","author":"Castillo","year":"2012","journal-title":"Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.apenergy.2009.05.016","article-title":"A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem","volume":"87","author":"Niknam","year":"2010","journal-title":"Appl. Energy"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Khosla, M., Sarin, R.K., and Uddin, M. (2012, January 11\u201313). Identification of type-2 fuzzy models for time-series forecasting using particle swarm optimization. Proceedings of the 2012 International Conference on Communication Systems and Net-work Technologies (CSNT), Rajkot, India.","DOI":"10.1109\/CSNT.2012.64"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.asoc.2012.08.032","article-title":"Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications","volume":"13","author":"Maldonado","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Krzeszowski, T., and Wiktorowicz, K. (2016, January 11\u201314). Evaluation of selected fuzzy particle swarm optimization algorithms. Proceedings of the 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, Poland.","DOI":"10.15439\/2016F206"},{"key":"ref_56","first-page":"13527","article-title":"Bee colony optimization for combined heat and power economic dispatch","volume":"38","author":"Basu","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1016\/j.cor.2010.12.002","article-title":"Bee colony optimization for the p-center problem","volume":"38","author":"Davidovic","year":"2011","journal-title":"Comput. Oper. Res."},{"key":"ref_58","unstructured":"Kennedy, J., and Eberhart, R.C. (December, January 27). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia."},{"key":"ref_59","unstructured":"Kennedy, J., and Eberhart, R.C. (2001). Swarm Intelligence, Morgan Kaufmann."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Perez, J., Valdez, F., Castillo, O., and Roeva, O. (2016, January 4\u20136). Bat algorithm with parameter adaptation using interval type-2 fuzzy logic for benchmark mathematical functions. Proceedings of the 2016 IEEE 8th International Conference on Intelligent Systems (IS), Sofia, Bulgaria.","DOI":"10.1109\/IS.2016.7737409"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Tuba, M., and Bacanin, N. (2015, January 25\u201328). Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning. Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan.","DOI":"10.1109\/CEC.2015.7256931"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, J., Valdez, F., and Castillo, O. (2015, January 25\u201328). Modification of the bat algorithm using fuzzy logic for dynamical parameter adaptation. Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan.","DOI":"10.1109\/CEC.2015.7256926"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.3844\/ajassp.2012.1464.1471","article-title":"Design and Development of an Intelligent Control by Using Bee Colony Optimization Technique","volume":"9","author":"Tiacharoen","year":"2012","journal-title":"Am. J. Appl. Sci."},{"key":"ref_64","first-page":"667","article-title":"Interval type-2 fuzzy logic for dynamic parameter adaptation in the bat algorithm","volume":"3","author":"Perez","year":"2016","journal-title":"Soft Comput."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1109\/87.911382","article-title":"Tracking control of unicycle-modeled mobile robots using a saturation feedback controller","volume":"9","author":"Lee","year":"2001","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_66","unstructured":"Eberhart, R.C., and Shi, Y. (2000, January 16\u201319). Comparing inertia weights and constriction factors in particle swarm optimization. Proceedings of the 2000 Congress on Evolutionary Computation, La Jolla, CA, USA."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/10\/3\/101\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:43:30Z","timestamp":1760208210000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/10\/3\/101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8,28]]},"references-count":66,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["a10030101"],"URL":"https:\/\/doi.org\/10.3390\/a10030101","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2017,8,28]]}}}