{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T22:42:18Z","timestamp":1780526538010,"version":"3.54.1"},"reference-count":58,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T00:00:00Z","timestamp":1623283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation\u2013neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA\u2019s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets.<\/jats:p>","DOI":"10.3390\/computation9060068","type":"journal-article","created":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T03:48:08Z","timestamp":1623296888000},"page":"68","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9927-5893","authenticated-orcid":false,"given":"Zenab Mohamed","family":"Elgamal","sequence":"first","affiliation":[{"name":"Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Norizan Mohd","family":"Yasin","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4758-5400","authenticated-orcid":false,"given":"Aznul Qalid Md","family":"Sabri","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8326-3655","authenticated-orcid":false,"given":"Rami","family":"Sihwail","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1464-8345","authenticated-orcid":false,"given":"Mohammad","family":"Tubishat","sequence":"additional","affiliation":[{"name":"School of Information Technology, Skyline University College, Sharjah P.O. Box 1797, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hazim","family":"Jarrah","sequence":"additional","affiliation":[{"name":"School of Information Technology, Skyline University College, Sharjah P.O. Box 1797, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Devanathan, K., Ganapathy, N., and Swaminathan, R. (2019, January 23\u201327). Binary Grey Wolf Optimizer based Feature Selection for Nucleolar and Centromere Staining Pattern Classification in Indirect Immunofluorescence Images. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856872"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.asoc.2018.01.011","article-title":"Feature selection with modified lion\u2019s algorithms and support vector machine for high-dimensional data","volume":"68","author":"Lin","year":"2018","journal-title":"Appl. Soft Comput. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.asoc.2018.10.036","article-title":"Feature selection based on artificial bee colony and gradient boosting decision tree","volume":"74","author":"Rao","year":"2019","journal-title":"Appl. Soft Comput. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.asoc.2018.11.012","article-title":"Adaptive multi-parent crossover GA for feature optimization in epileptic seizure identification","volume":"75","author":"Bimba","year":"2019","journal-title":"Appl. Soft Comput. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"186638","DOI":"10.1109\/ACCESS.2020.3029728","article-title":"An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field","volume":"8","author":"Elgamal","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.asoc.2016.01.044","article-title":"A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy","volume":"43","author":"Moradi","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.knosys.2018.05.009","article-title":"An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems","volume":"154","author":"Faris","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.asoc.2017.11.006","article-title":"Whale optimization approaches for wrapper feature selection","volume":"62","author":"Mafarja","year":"2018","journal-title":"Appl. Soft Comput. J."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Too, J., Abdullah, A.R., Saad, N.M., Ali, N.M., and Tee, W. (2018). A new competitive binary grey wolf optimizer to solve the feature selection problem in EMG signals classification. Computers, 7.","DOI":"10.3390\/computers7040058"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3390\/axioms8030079","article-title":"Hybrid binary particle swarm optimization differential evolution-based feature selection for EMG signals classification","volume":"8","author":"Too","year":"2019","journal-title":"Axioms"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"12201","DOI":"10.1007\/s00521-019-04368-6","article-title":"Feature selection using binary grey wolf optimizer with elite-based crossover for Arabic text classification","volume":"32","author":"Chantar","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Too, J., Abdullah, A.R., Saad, N.M., and Ali, N.M. (2018). Feature selection based on binary tree growth algorithm for the classification of myoelectric signals. Machines, 6.","DOI":"10.3390\/machines6040065"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Too, J., Abdullah, A.R., Saad, N.M., and Tee, W. (2019). EMG feature selection and classification using a Pbest-guide binary particle swarm optimization. Computation, 7.","DOI":"10.3390\/computation7010012"},{"key":"ref_15","first-page":"1","article-title":"A Hybrid Gene Selection Method Based on ReliefF and Ant Colony Optimization Algorithm for Tumor Classification","volume":"9","author":"Sun","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.ins.2019.05.038","article-title":"An evolutionary gravitational search-based feature selection","volume":"497","author":"Taradeh","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105190","DOI":"10.1016\/j.knosys.2019.105190","article-title":"Equilibrium optimizer: A novel optimization algorithm","volume":"191","author":"Faramarzi","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Abdel-Basset, M., Chang, V., and Mohamed, R. (2020). A Novel Equilibrium Optimization Algorithm for Multi-Thresholding Image Segmentation Problems, Springer.","DOI":"10.1007\/s00521-020-04820-y"},{"key":"ref_19","first-page":"1","article-title":"Prediction of laser cutting parameters for polymethylmethacrylate sheets using random vector functional link network integrated with equilibrium optimizer","volume":"32","author":"Elsheikh","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106867","DOI":"10.1016\/j.asoc.2020.106867","article-title":"Equilibrium optimization algorithm for network reconfiguration and distributed generation allocation in power systems","volume":"98","author":"Shaheen","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1007\/s10489-018-1334-8","article-title":"Improved whale optimization algorithm for feature selection in Arabic sentiment analysis","volume":"49","author":"Tubishat","year":"2019","journal-title":"Appl. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.asoc.2017.04.025","article-title":"A novel improved particle swarm optimization algorithm based on individual difference evolution","volume":"57","author":"Gou","year":"2017","journal-title":"Appl. Soft Comput. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.eswa.2018.08.051","article-title":"Binary butterfly optimization approaches for feature selection","volume":"116","author":"Arora","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"22094","DOI":"10.1109\/ACCESS.2020.2968943","article-title":"Improved Ant Lion Optimizer Based on Spiral Complex Path Searching Patterns","volume":"8","author":"Guo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, C., Wang, W., and Pan, Y. (2020). Enhancing electronic nose performance by feature selection using an improved grey wolf optimization based algorithm. Sensors, 20.","DOI":"10.3390\/s20154065"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.eswa.2018.06.023","article-title":"Improved grasshopper optimization algorithm using opposition-based learning","volume":"112","author":"Ewees","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Park, J., Park, M.W., Kim, D.W., and Lee, J. (2020). Multi-population genetic algorithm for multilabel feature selection based on label complementary communication. Entropy, 22.","DOI":"10.3390\/e22080876"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Pichai, S., Sunat, K., and Chiewchanwattana, S. (2020). An asymmetric chaotic competitive swarm optimization algorithm for feature selection in high-dimensional data. Symmetry, 12.","DOI":"10.3390\/sym12111782"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.procs.2017.09.133","article-title":"Feature Selection using Gravitational Search Algorithm for Biomedical Data","volume":"115","author":"Nagpal","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"140936","DOI":"10.1109\/ACCESS.2020.3013617","article-title":"An Efficient Binary Equilibrium Optimizer Algorithm for Feature Selection","volume":"8","author":"Gao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4787","DOI":"10.1016\/j.aej.2020.08.043","article-title":"Equilibrium optimizer based multi dimensions operation of hybrid AC\/DC grids","volume":"59","author":"Shaheen","year":"2020","journal-title":"Alexandria Eng. J."},{"key":"ref_34","first-page":"1","article-title":"General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification","volume":"35","author":"Too","year":"2020","journal-title":"Appl. Artif. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ghosh, K.K., Guha, R., Bera, S.K., Sarkar, R., and Mirjalili, S. (2020). BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection. ResearchSquare.","DOI":"10.21203\/rs.3.rs-28683\/v1"},{"key":"ref_36","unstructured":"Tizhoosh, H.R. (2005, January 28\u201330). Opposition-Based Learning: A New Scheme for Machine Intelligence. Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC\u201906), Vienna, Austria."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"121127","DOI":"10.1109\/ACCESS.2020.3006473","article-title":"Improved Harris Hawks Optimization Using Elite Opposition-Based Learning and Novel Search Mechanism for Feature Selection","volume":"8","author":"Sihwail","year":"2020","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.neucom.2015.01.110","article-title":"Elite opposition-based flower pollination algorithm","volume":"188","author":"Zhou","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S1469026817500122","article-title":"Hybrid Grey Wolf Optimizer Using Elite Opposition-Based Learning Strategy and Simplex Method","volume":"16","author":"Zhang","year":"2017","journal-title":"Int. J. Comput. Intell. Appl."},{"key":"ref_40","first-page":"243","article-title":"IWOA: An improved whale optimization algorithm for optimization problems","volume":"6","author":"Yazdani","year":"2019","journal-title":"J. Comput. Des. Eng."},{"key":"ref_41","first-page":"567","article-title":"A cuckoo search algorithm with elite opposition-based strategy","volume":"2015","author":"Huang","year":"2015","journal-title":"J. Intell. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4699","DOI":"10.1016\/j.ins.2011.03.016","article-title":"Enhancing particle swarm optimization using generalized opposition-based learning","volume":"181","author":"Wang","year":"2011","journal-title":"Inf. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1016\/j.ejor.2017.03.031","article-title":"A hybrid Particle Swarm Optimization\u2013Variable Neighborhood Search algorithm for Constrained Shortest Path problems","volume":"261","author":"Marinakis","year":"2017","journal-title":"Eur. J. Oper. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s11063-015-9450-5","article-title":"Hybrid Harmony Search Combined with Stochastic Local Search for Feature Selection","volume":"44","author":"Nekkaa","year":"2016","journal-title":"Neural Process. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.future.2018.03.020","article-title":"A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem","volume":"85","author":"Manogaran","year":"2018","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.chemolab.2018.11.010","article-title":"Hybrid binary Coral Reefs Optimization algorithm with Simulated Annealing for Feature Selection in high-dimensional biomedical datasets","volume":"184","author":"Yan","year":"2019","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1016\/j.ijepes.2015.12.032","article-title":"A hybrid algorithm of Ant Colony Optimization (ACO) and Iterated Local Search (ILS) for estimating electricity domestic consumption: Case of Turkey","volume":"78","author":"Toksari","year":"2016","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.neucom.2017.04.053","article-title":"Hybrid Whale Optimization Algorithm with simulated annealing for feature selection","volume":"260","author":"Mafarja","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.future.2020.08.019","article-title":"Explicit aspects extraction in sentiment analysis using optimal rules combination","volume":"114","author":"Tubishat","year":"2021","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1109\/TEVC.2008.2009457","article-title":"Differential evolution using a neighborhood-based mutation operator","volume":"13","author":"Das","year":"2009","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"113873","DOI":"10.1016\/j.eswa.2020.113873","article-title":"Dynamic Salp swarm algorithm for feature selection","volume":"164","author":"Tubishat","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3462","DOI":"10.1007\/s10489-018-1158-6","article-title":"A novel chaotic salp swarm algorithm for global optimization and feature selection","volume":"48","author":"Sayed","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Khan, T.A., Zain-Ul-Abideen, K., and Ling, S.H. (2019, January 3\u20135). A Modified Particle Swarm Optimization Algorithm Used for Feature Selection of UCI Biomedical Data Sets. Proceedings of the 60th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), Riga, Latvia.","DOI":"10.1109\/ITMS47855.2019.8940760"},{"key":"ref_54","first-page":"1598","article-title":"Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection","volume":"29","author":"Ghosh","year":"2019","journal-title":"J. Intell. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neucom.2016.03.101","article-title":"Binary ant lion approaches for feature selection","volume":"213","author":"Emary","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.future.2020.03.055","article-title":"Slime mould algorithm: A new method for stochastic optimization","volume":"111","author":"Li","year":"2020","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1007\/s00500-018-3102-4","article-title":"Butterfly optimization algorithm: A novel approach for global optimization","volume":"23","author":"Arora","year":"2019","journal-title":"Soft Comput."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Salgotra, R., Singh, U., Saha, S., and Gandomi, A.H. (2020, January 19\u201324). Improving Cuckoo Search: Incorporating Changes for CEC 2017 and CEC 2020 Benchmark Problems. Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK.","DOI":"10.1109\/CEC48606.2020.9185684"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/9\/6\/68\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:12:43Z","timestamp":1760163163000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/9\/6\/68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,10]]},"references-count":58,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["computation9060068"],"URL":"https:\/\/doi.org\/10.3390\/computation9060068","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,10]]}}}