{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:53:50Z","timestamp":1760151230175,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Hradec Kralove, Czech Republic","award":["2210\/2022-2023"],"award-info":[{"award-number":["2210\/2022-2023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable objective function. Achieving the optimal solution to such problems has become a major challenge. To address this challenge and provide a solution to deal with the complexities and difficulties of optimization applications, a new stochastic-based optimization algorithm is proposed in this study. Optimization algorithms are a type of stochastic approach for addressing optimization issues that use random scanning of the search space to produce quasi-optimal answers. The Selecting Some Variables to Update-Based Algorithm (SSVUBA) is a new optimization algorithm developed in this study to handle optimization issues in various fields. The suggested algorithm\u2019s key principles are to make better use of the information provided by different members of the population and to adjust the number of variables used to update the algorithm population during the iterations of the algorithm. The theory of the proposed SSVUBA is described, and then its mathematical model is offered for use in solving optimization issues. Fifty-three objective functions, including unimodal, multimodal, and CEC 2017 test functions, are utilized to assess the ability and usefulness of the proposed SSVUBA in addressing optimization issues. SSVUBA\u2019s performance in optimizing real-world applications is evaluated on four engineering design issues. Furthermore, the performance of SSVUBA in optimization was compared to the performance of eight well-known algorithms to further evaluate its quality. The simulation results reveal that the proposed SSVUBA has a significant ability to handle various optimization issues and that it outperforms other competitor algorithms by giving appropriate quasi-optimal solutions that are closer to the global optima.<\/jats:p>","DOI":"10.3390\/s22051795","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"1795","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems"],"prefix":"10.3390","volume":"22","author":[{"given":"Mohammad","family":"Dehghani","sequence":"first","affiliation":[{"name":"Department of Mathematics, Faculty of Science, University of Hradec Kr\u00e1lov\u00e9, 500 03 Hradec Kralove, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8992-125X","authenticated-orcid":false,"given":"Pavel","family":"Trojovsk\u00fd","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science, University of Hradec Kr\u00e1lov\u00e9, 500 03 Hradec Kralove, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106926","DOI":"10.1016\/j.knosys.2021.106926","article-title":"SSC: A hybrid nature-inspired meta-heuristic optimization algorithm for engineering applications","volume":"222","author":"Dhiman","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_2","unstructured":"Fletcher, R. (2013). Practical Methods of Optimization, John Wiley & Sons."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cavazzuti, M. (2013). Deterministic Optimization. Optimization Methods: From Theory to Design Scientific and Technological Aspects in Mechanics, Springer.","DOI":"10.1007\/978-3-642-31187-1"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Dehghani, M., Montazeri, Z., Dehghani, A., Samet, H., Sotelo, C., Sotelo, D., Ehsanifar, A., Malik, O.P., Guerrero, J.M., and Dhiman, G. (2020). DM: Dehghani Method for modifying optimization algorithms. Appl. Sci., 10.","DOI":"10.3390\/app10217683"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1109\/59.317674","article-title":"Reactive power optimization by genetic algorithm","volume":"9","author":"Iba","year":"1994","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Banerjee, A., De, S.K., Majumder, K., Das, V., Giri, D., Shaw, R.N., and Ghosh, A. (2022). Construction of effective wireless sensor network for smart communication using modified ant colony optimization technique. Advanced Computing and Intelligent Technologies, Springer.","DOI":"10.1007\/978-981-16-2164-2_22"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.jvcir.2019.03.004","article-title":"Application of artificial intelligence algorithms in image processing","volume":"61","author":"Zhang","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.eswa.2017.10.042","article-title":"Bees swarm optimization guided by data mining techniques for document information retrieval","volume":"94","author":"Djenouri","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"114288","DOI":"10.1016\/j.eswa.2020.114288","article-title":"Feature selection using Binary Crow Search Algorithm with time varying flight length","volume":"168","author":"Chaudhuri","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Singh, T., Saxena, N., Khurana, M., Singh, D., Abdalla, M., and Alshazly, H. (2021). Data Clustering Using Moth-Flame Optimization Algorithm. Sensors, 21.","DOI":"10.3390\/s21124086"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101548","DOI":"10.1016\/j.asej.2021.06.032","article-title":"Archimedes optimization algorithm based maximum power point tracker for wind energy generation system","volume":"13","author":"Fathy","year":"2022","journal-title":"Ain Shams Eng. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hasan, M.Z., and Al-Rizzo, H. (2020). Beamforming optimization in internet of things applications using robust swarm algorithm in conjunction with connectable and collaborative sensors. Sensors, 20.","DOI":"10.3390\/s20072048"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No free lunch theorems for optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Trojovsk\u00fd, P., and Dehghani, M. (2022). Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications. Sensors, 22.","DOI":"10.3390\/s22030855"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dehghani, M., and Trojovsk\u00fd, P. (2021). Teamwork Optimization Algorithm: A New Optimization Approach for Function Minimization\/Maximization. Sensors, 21.","DOI":"10.3390\/s21134567"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1023\/A:1022602019183","article-title":"Genetic Algorithms and Machine Learning","volume":"3","author":"Goldberg","year":"1988","journal-title":"Mach. Learn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/3477.484436","article-title":"Ant system: Optimization by a colony of cooperating agents","volume":"26","author":"Dorigo","year":"1996","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"key":"ref_18","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle Swarm Optimization. Proceedings of the ICNN\u201995\u2014International Conference on Neural Networks, Perth, Australia."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","article-title":"Optimization by simulated annealing","volume":"220","author":"Kirkpatrick","year":"1983","journal-title":"Science"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1504\/IJBIC.2010.032124","article-title":"Firefly algorithm, stochastic test functions and design optimisation","volume":"2","author":"Yang","year":"2010","journal-title":"Int. J. Bio-Inspir. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.cad.2010.12.015","article-title":"Teaching\u2013learning-based optimization: A novel method for constrained mechanical design optimization problems","volume":"43","author":"Rao","year":"2011","journal-title":"Comput.-Aided Des."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1177\/003754970107600201","article-title":"A new heuristic optimization algorithm: Harmony search","volume":"76","author":"Geem","year":"2001","journal-title":"Simulation"},{"key":"ref_23","first-page":"32","article-title":"An optimizing method based on autonomous animats: Fish-swarm algorithm","volume":"22","author":"Li","year":"2002","journal-title":"Syst. Eng.-Theory Pract."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","article-title":"GSA: A gravitational search algorithm","volume":"179","author":"Rashedi","year":"2009","journal-title":"Inf. Sci."},{"key":"ref_26","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."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"113377","DOI":"10.1016\/j.eswa.2020.113377","article-title":"Marine Predators Algorithm: A nature-inspired metaheuristic","volume":"152","author":"Faramarzi","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103541","DOI":"10.1016\/j.engappai.2020.103541","article-title":"Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization","volume":"90","author":"Kaur","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104314","DOI":"10.1016\/j.engappai.2021.104314","article-title":"QANA: Quantum-based avian navigation optimizer algorithm","volume":"104","author":"Zamani","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105583","DOI":"10.1016\/j.asoc.2019.105583","article-title":"CCSA: Conscious neighborhood-based crow search algorithm for solving global optimization problems","volume":"85","author":"Zamani","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103249","DOI":"10.1016\/j.engappai.2019.103249","article-title":"Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems","volume":"87","author":"Hayyolalam","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"114107","DOI":"10.1016\/j.eswa.2020.114107","article-title":"Red fox optimization algorithm","volume":"166","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"114194","DOI":"10.1016\/j.cma.2021.114194","article-title":"Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications","volume":"388","author":"Zhao","year":"2022","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"116158","DOI":"10.1016\/j.eswa.2021.116158","article-title":"Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer","volume":"191","author":"Abualigah","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.matcom.2021.08.013","article-title":"Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems","volume":"192","author":"Hashim","year":"2022","journal-title":"Math. Comput. Simul."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"114616","DOI":"10.1016\/j.cma.2022.114616","article-title":"Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization","volume":"392","author":"Zamani","year":"2022","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/4235.771163","article-title":"Evolutionary programming made faster","volume":"3","author":"Yao","year":"1999","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_38","unstructured":"Awad, N., Ali, M., Liang, J., Qu, B., and Suganthan, P. (2016). Problem Definitions Evaluation Criteria for the CEC 2017 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization, Kyungpook National University. Technology Report."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wilcoxon, F. (1992). Individual comparisons by ranking methods. Breakthroughs in Statistics, Springer.","DOI":"10.1007\/978-1-4612-4380-9_16"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nadimi-Shahraki, M.H., Fatahi, A., Zamani, H., Mirjalili, S., Abualigah, L., and Abd Elaziz, M. (2021). Migration-based moth-flame optimization algorithm. Processes, 9.","DOI":"10.3390\/pr9122276"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1115\/1.2919393","article-title":"An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design","volume":"116","author":"Kannan","year":"1994","journal-title":"J. Mech. Des."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gandomi, A.H., and Yang, X.-S. (2011). Benchmark problems in structural optimization. Computational Optimization, Methods and Algorithms, Springer.","DOI":"10.1007\/978-3-642-20859-1_12"},{"key":"ref_43","unstructured":"Mezura-Montes, E., and Coello, C.A.C. (2021, January 25\u201330). Useful infeasible solutions in engineering optimization with evolutionary algorithms. Proceedings of the Mexican International Conference on Artificial Intelligence, Mexico City, Mexico."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1795\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:26:53Z","timestamp":1760135213000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1795"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,24]]},"references-count":43,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22051795"],"URL":"https:\/\/doi.org\/10.3390\/s22051795","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,2,24]]}}}