{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T15:35:58Z","timestamp":1781883358611,"version":"3.54.5"},"reference-count":75,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T00:00:00Z","timestamp":1645488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Romanian Ministry of Education and Resarch","award":["411PED\/2020, code PN-III-P2-2.1- 520 PED-2019-2271, within PNCDI III."],"award-info":[{"award-number":["411PED\/2020, code PN-III-P2-2.1- 520 PED-2019-2271, within PNCDI III."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We live in a period when smart devices gather a large amount of data from a variety of sensors and it is often the case that decisions are taken based on them in a more or less autonomous manner. Still, many of the inputs do not prove to be essential in the decision-making process; hence, it is of utmost importance to find the means of eliminating the noise and concentrating on the most influential attributes. In this sense, we put forward a method based on the swarm intelligence paradigm for extracting the most important features from several datasets. The thematic of this paper is a novel implementation of an algorithm from the swarm intelligence branch of the machine learning domain for improving feature selection. The combination of machine learning with the metaheuristic approaches has recently created a new branch of artificial intelligence called learnheuristics. This approach benefits both from the capability of feature selection to find the solutions that most impact on accuracy and performance, as well as the well known characteristic of swarm intelligence algorithms to efficiently comb through a large search space of solutions. The latter is used as a wrapper method in feature selection and the improvements are significant. In this paper, a modified version of the salp swarm algorithm for feature selection is proposed. This solution is verified by 21 datasets with the classification model of K-nearest neighborhoods. Furthermore, the performance of the algorithm is compared to the best algorithms with the same test setup resulting in better number of features and classification accuracy for the proposed solution. Therefore, the proposed method tackles feature selection and demonstrates its success with many benchmark datasets.<\/jats:p>","DOI":"10.3390\/s22051711","type":"journal-article","created":{"date-parts":[[2022,2,22]],"date-time":"2022-02-22T22:35:00Z","timestamp":1645569300000},"page":"1711","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["Novel Improved Salp Swarm Algorithm: An Application for Feature Selection"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4351-068X","authenticated-orcid":false,"given":"Miodrag","family":"Zivkovic","sequence":"first","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5917-1857","authenticated-orcid":false,"given":"Catalin","family":"Stoean","sequence":"additional","affiliation":[{"name":"Human Language Technology Research Center, University of Bucharest, 010014 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2056-6231","authenticated-orcid":false,"given":"Amit","family":"Chhabra","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar 143005, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9237-3135","authenticated-orcid":false,"given":"Nebojsa","family":"Budimirovic","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3324-3909","authenticated-orcid":false,"given":"Aleksandar","family":"Petrovic","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2062-924X","authenticated-orcid":false,"given":"Nebojsa","family":"Bacanin","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s43069-021-00068-x","article-title":"Review on Nature-Inspired Algorithms","volume":"2","author":"Korani","year":"2021","journal-title":"Oper. Res. Forum"},{"key":"ref_2","first-page":"718","article-title":"Multi-objective Task Scheduling in Cloud Computing Environment by Hybridized Bat Algorithm","volume":"42","author":"Bezdan","year":"2020","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Strumberger, I., Minovic, M., Tuba, M., and Bacanin, N. (2019). Performance of elephant herding optimization and tree growth algorithm adapted for node localization in wireless sensor networks. Sensors, 19.","DOI":"10.3390\/s19112515"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Katyara, S., Shaikh, M.F., Shaikh, S., Khand, Z.H., Staszewski, L., Bhan, V., Majeed, A., Shah, M.A., and Zbigniew, L. (2021). Leveraging a genetic algorithm for the optimal placement of distributed generation and the need for energy management strategies using a fuzzy inference system. Electronics, 10.","DOI":"10.3390\/electronics10020172"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14637","DOI":"10.1007\/s00500-020-04812-z","article-title":"Red deer algorithm (RDA): A new nature-inspired meta-heuristic","volume":"24","year":"2020","journal-title":"Soft Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.advengsoft.2015.01.010","article-title":"The Ant Lion Optimizer","volume":"83","author":"Mirjalili","year":"2015","journal-title":"Adv. Eng. Softw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"50001","DOI":"10.1109\/ACCESS.2021.3067597","article-title":"Grasshopper Optimization Algorithm: Theory, Variants, and Applications","volume":"9","author":"Meraihi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s00521-015-1870-7","article-title":"Multi-Verse Optimizer: A nature-inspired algorithm for global optimization","volume":"27","author":"Mirjalili","year":"2015","journal-title":"Neural Comput. Appl."},{"key":"ref_9","first-page":"115","article-title":"Moths\u2013Flame Optimization Algorithm","volume":"Volume 927","author":"Okwu","year":"2021","journal-title":"Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.engappai.2018.04.009","article-title":"The Social Engineering Optimizer (SEO)","volume":"72","year":"2018","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1007\/s00521-015-1920-1","article-title":"Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems","volume":"27","author":"Mirjalili","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_12","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_13","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."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.knosys.2015.12.022","article-title":"SCA: A sine cosine algorithm for solving optimization problems","volume":"96","author":"Mirjalili","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.1007\/s10462-020-09909-3","article-title":"Advances in sine cosine algorithm: A comprehensive survey","volume":"54","author":"Abualigah","year":"2021","journal-title":"Artif. Intell. Rev."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1109\/TPAMI.1979.4766926","article-title":"A Problem of Dimensionality: A Simple Example","volume":"PAMI-1","author":"Trunk","year":"1979","journal-title":"Pattern Anal. Mach. Intell. IEEE Trans."},{"key":"ref_17","first-page":"13","article-title":"Dimensionality reduction: A comparative","volume":"10","author":"Postma","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1109\/PROC.1969.7277","article-title":"Feature extraction: A survey","volume":"57","author":"Levine","year":"1969","journal-title":"Proc. IEEE"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"106560","DOI":"10.1016\/j.knosys.2020.106560","article-title":"BEPO: A novel binary emperor penguin optimizer for automatic feature selection","volume":"211","author":"Dhiman","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","article-title":"A survey on feature selection methods","volume":"40","author":"Chandrashekar","year":"2014","journal-title":"Comput. Electr. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100663","DOI":"10.1016\/j.swevo.2020.100663","article-title":"A survey on swarm intelligence approaches to feature selection in data mining","volume":"54","author":"Nguyen","year":"2020","journal-title":"Swarm Evol. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hussien, A.G., Oliva, D., Houssein, E.H., Juan, A.A., and Yu, X. (2020). Binary Whale Optimization Algorithm for Dimensionality Reduction. Mathematics, 8.","DOI":"10.3390\/math8101821"},{"key":"ref_23","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_24","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_25","unstructured":"Li, X., Engelbrecht, A., and Epitropakis, M.G. (2013). Benchmark Functions for CEC\u20192013 Special Session and Competition on Niching Methods for Multimodal Function Optimization, Evolutionary Computation and Machine Learning Group, RMIT University. Techtechnical Report."},{"key":"ref_26","unstructured":"Dua, D., and Graff, C. (2017). Uci machine learning repository. The Absenteeism at Work Dataset Was Donated by Andrea Martiniano, Ricardo Pinto Ferreira, and Renato Jose Sassi, University of California."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1016\/j.procs.2016.07.111","article-title":"A Survey on Feature Selection","volume":"91","author":"Miao","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4543","DOI":"10.1007\/s10489-021-02550-9","article-title":"A comprehensive survey on feature selection in the various fields of machine learning","volume":"28","author":"Dhal","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/TEVC.2015.2504420","article-title":"A Survey on Evolutionary Computation Approaches to Feature Selection","volume":"20","author":"Xue","year":"2016","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Brezo\u010dnik, L., Fister, I., and Podgorelec, V. (2018). Swarm Intelligence Algorithms for Feature Selection: A Review. Appl. Sci., 8.","DOI":"10.3390\/app8091521"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Filipi\u010d, B., Minisci, E., and Vasile, M. (2020). Hybrid Variable Selection and Support Vector Regression for Gas Sensor Optimization. Bioinspired Optimization Methods and Their Applications, Proceedings of the 9th International Conference (BIOMA 2020), Brussels, Belgium, 19\u201320 November 2020, Springer International Publishing.","DOI":"10.1007\/978-3-030-63710-1"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.asoc.2018.11.001","article-title":"GA-SVM based feature selection and parameter optimization in hospitalization expense modeling","volume":"75","author":"Tao","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Stoean, C. (2016, January 24\u201327). In Search of the Optimal Set of Indicators when Classifying Histopathological Images. Proceedings of the 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania.","DOI":"10.1109\/SYNASC.2016.074"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1007\/s13042-014-0276-7","article-title":"A bumble bees mating optimization algorithm for the feature selection problem","volume":"7","author":"Marinaki","year":"2016","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.neucom.2014.06.067","article-title":"An advanced ACO algorithm for feature subset selection","volume":"147","author":"Kashef","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1057\/jors.2013.72","article-title":"An evolutionary algorithm with the partial sequential forward floating search mutation for large-scale feature selection problems","volume":"66","author":"Jeong","year":"2015","journal-title":"J. Oper. Res. Soc."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1016\/j.eswa.2013.09.004","article-title":"Genetic algorithm-based heuristic for feature selection in credit risk assessment","volume":"41","author":"Oreski","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Winkler, S.M., Affenzeller, M., Jacak, W., and Stekel, H. (2011, January 12\u201316). Identification of Cancer Diagnosis Estimation Models Using Evolutionary Algorithms: A Case Study for Breast Cancer, Melanoma, and Cancer in the Respiratory System. Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO\u201911), Dublin, Ireland.","DOI":"10.1145\/2001858.2002040"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1016\/j.dss.2011.01.015","article-title":"Improving the Ranking Quality of Medical Image Retrieval Using a Genetic Feature Selection Method","volume":"51","author":"Ribeiro","year":"2011","journal-title":"Decis. Support Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.asoc.2013.09.018","article-title":"Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms","volume":"18","author":"Xue","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ben Chaabane, S., Belazi, A., Kharbech, S., Bouallegue, A., and Clavier, L. (2021). Improved Salp Swarm Optimization Algorithm: Application in Feature Weighting for Blind Modulation Identification. Electronics, 10.","DOI":"10.3390\/electronics10162002"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"113122","DOI":"10.1016\/j.eswa.2019.113122","article-title":"Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection","volume":"145","author":"Tubishat","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_43","first-page":"335","article-title":"Improved salp swarm algorithm for feature selection","volume":"32","author":"Hegazy","year":"2020","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.jclepro.2018.07.258","article-title":"A bi-objective green home health care routing problem","volume":"200","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_45","first-page":"100246","article-title":"Bi-level programming for home health care supply chain considering outsourcing","volume":"25","author":"Smith","year":"2021","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Jain, S., and Dharavath, R. (2021). Memetic salp swarm optimization algorithm based feature selection approach for crop disease detection system. J. Ambient. Intell. Humaniz. Comput., 1\u201319.","DOI":"10.1007\/s12652-021-03406-3"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s42835-021-00889-0","article-title":"An Exhaustive Solution of Power System Unit Commitment Problem Using Enhanced Binary Salp Swarm Optimization Algorithm","volume":"17","year":"2022","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zivkovic, M., Bacanin, N., Tuba, E., Strumberger, I., Bezdan, T., and Tuba, M. (2020, January 15\u201319). Wireless Sensor Networks Life Time Optimization Based on the Improved Firefly Algorithm. Proceedings of the International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus.","DOI":"10.1109\/IWCMC48107.2020.9148087"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bacanin, N., Tuba, E., Zivkovic, M., Strumberger, I., and Tuba, M. (2019, January 10\u201312). Whale Optimization Algorithm with Exploratory Move for Wireless Sensor Networks Localization. Proceedings of the 19th International Conference on Hybrid Intelligent Systems (HIS 2019), Bhopal, India.","DOI":"10.1007\/978-3-030-49336-3_33"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zivkovic, M., Bacanin, N., Zivkovic, T., Strumberger, I., Tuba, E., and Tuba, M. (2020, January 26\u201327). Enhanced Grey Wolf Algorithm for Energy Efficient Wireless Sensor Networks. Proceedings of the Zooming Innovation in Consumer Technologies Conference (ZINC), Novi Sad, Serbia.","DOI":"10.1109\/ZINC50678.2020.9161788"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Bacanin, N., Arnaut, U., Zivkovic, M., Bezdan, T., and Rashid, T.A. (2022). Energy Efficient Clustering in Wireless Sensor Networks by Opposition-Based Initialization Bat Algorithm. Computer Networks and Inventive Communication Technologies, Springer.","DOI":"10.1007\/978-981-16-3728-5_1"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Bacanin, N., Bezdan, T., Tuba, E., Strumberger, I., Tuba, M., and Zivkovic, M. (2019, January 26\u201327). Task scheduling in cloud computing environment by grey wolf optimizer. Proceedings of the 27th Telecommunications Forum (TELFOR), Belgrade, Serbia.","DOI":"10.1109\/TELFOR48224.2019.8971223"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"102669","DOI":"10.1016\/j.scs.2020.102669","article-title":"COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach","volume":"66","author":"Zivkovic","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/978-981-33-4355-9_14","article-title":"Hybrid Genetic Algorithm and Machine Learning Method for COVID-19 Cases Prediction","volume":"Volume 176","author":"Zivkovic","year":"2021","journal-title":"Proceedings of International Conference on Sustainable Expert Systems"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bezdan, T., Zivkovic, M., Tuba, E., Strumberger, I., Bacanin, N., and Tuba, M. (2020). Glioma Brain Tumor Grade Classification from MRI Using Convolutional Neural Networks Designed by Modified FA. International Conference on Intelligent and Fuzzy Systems, Proceedings of the INFUS 2020 Conference, Istanbul, Turkey, 21\u201323 July 2020, Springer.","DOI":"10.1007\/978-3-030-51156-2_111"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Basha, J., Bacanin, N., Vukobrat, N., Zivkovic, M., Venkatachalam, K., Hub\u00e1lovsk\u1ef3, S., and Trojovsk\u1ef3, P. (2021). Chaotic Harris Hawks Optimization with Quasi-Reflection-Based Learning: An Application to Enhance CNN Design. Sensors, 21.","DOI":"10.3390\/s21196654"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Bezdan, T., Stoean, C., Naamany, A.A., Bacanin, N., Rashid, T.A., Zivkovic, M., and Venkatachalam, K. (2021). Hybrid Fruit-Fly Optimization Algorithm with K-Means for Text Document Clustering. Mathematics, 9.","DOI":"10.3390\/math9161929"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Strumberger, I., Tuba, E., Bacanin, N., Zivkovic, M., Beko, M., and Tuba, M. (2019, January 10). Designing convolutional neural network architecture by the firefly algorithm. Proceedings of the International Young Engineers Forum (YEF-ECE), Costa da Caparica, Portugal.","DOI":"10.1109\/YEF-ECE.2019.8740818"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Milosevic, S., Bezdan, T., Zivkovic, M., Bacanin, N., Strumberger, I., and Tuba, M. (2021). Feed-Forward Neural Network Training by Hybrid Bat Algorithm. Modelling and Development of Intelligent Systems, Proceedings of the 7th International Conference (MDIS 2020), Sibiu, Romania, 22\u201324 October 2020, Springer International Publishing. Revised Selected Papers 7.","DOI":"10.1007\/978-3-030-68527-0_4"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"169135","DOI":"10.1109\/ACCESS.2021.3135201","article-title":"Artificial Neural Networks Hidden Unit and Weight Connection Optimization by Quasi-Refection-Based Learning Artificial Bee Colony Algorithm","volume":"9","author":"Bacanin","year":"2021","journal-title":"IEEE Access"},{"key":"ref_61","first-page":"4199","article-title":"Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics","volume":"70","author":"Bacanin","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Bezdan, T., Petrovic, A., Zivkovic, M., Strumberger, I., Devi, V.K., and Bacanin, N. (2021, January 26\u201327). Current Best Opposition-Based Learning Salp Swarm Algorithm for Global Numerical Optimization. Proceedings of the Zooming Innovation in Consumer Technologies Conference (ZINC), Novi Sad, Serbia.","DOI":"10.1109\/ZINC52049.2021.9499275"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Bacanin, N., Petrovic, A., Zivkovic, M., Bezdan, T., and Chhabra, A. (2021). Enhanced Salp Swarm Algorithm for Feature Selection. International Conference on Intelligent and Fuzzy Systems, Proceedings of the INFUS 2021 Conference, Virtual, 24\u201326 August 2021, Springer.","DOI":"10.1007\/978-3-030-85626-7_57"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Watanabe, O., and Zeugmann, T. (2009). Firefly Algorithms for Multimodal Optimization. Stochastic Algorithms: Foundations and Applications, Springer.","DOI":"10.1007\/978-3-642-04944-6"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00500-017-2785-2","article-title":"Gravitational search algorithm with both attractive and repulsive forces","volume":"23","author":"Zandevakili","year":"2019","journal-title":"Soft Comput."},{"key":"ref_66","unstructured":"Haupt, R.L., and Haupt, S.E. (1998). Practical Genetic Algorithms, John Wiley and Sons."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Rashedi, E., and Nezamabadi-pour, H. (2012, January 2\u20133). Improving the precision of CBIR systems by feature selection using binary gravitational search algorithm. Proceedings of the 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), Shiraz, Iran.","DOI":"10.1109\/AISP.2012.6313714"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.scient.2011.04.003","article-title":"Disruption: A new operator in gravitational search algorithm","volume":"18","author":"Sarafrazi","year":"2011","journal-title":"Sci. Iran."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1080\/18756891.2014.966990","article-title":"Black hole: A new operator for gravitational search algorithm","volume":"7","author":"Doraghinejad","year":"2014","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_70","first-page":"436","article-title":"Clustered-gravitational search algorithm and its application in parameter optimization of a Low Noise Amplifier","volume":"258","author":"Shams","year":"2015","journal-title":"Appl. Math. Comput."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","article-title":"The use of ranks to avoid the assumption of normality implicit in the analysis of variance","volume":"32","author":"Friedman","year":"1937","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1214\/aoms\/1177731944","article-title":"A comparison of alternative tests of significance for the problem of m rankings","volume":"11","author":"Friedman","year":"1940","journal-title":"Ann. Math. Stat."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Sheskin, D.J. (2020). Handbook of Parametric and Nonparametric Statistical Procedures, Chapman and Hall\/CRC.","DOI":"10.1201\/9780429186196"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1080\/03610928008827904","article-title":"Approximations of the critical region of the fbietkan statistic","volume":"9","author":"Iman","year":"1980","journal-title":"Commun. Stat.-Theory Methods"},{"key":"ref_75","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. Proceedings of the ICNN\u201995\u2014International Conference on Neural Networks, Perth, WA, Australia."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1711\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:24:54Z","timestamp":1760135094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/5\/1711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,22]]},"references-count":75,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22051711"],"URL":"https:\/\/doi.org\/10.3390\/s22051711","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,22]]}}}