{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T05:09:19Z","timestamp":1762060159044,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T00:00:00Z","timestamp":1659398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Technology R&amp;D Program of China","award":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"],"award-info":[{"award-number":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"]}]},{"name":"National Natural Science Foundation of China","award":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"],"award-info":[{"award-number":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"]}]},{"DOI":"10.13039\/501100018617","name":"Liao Ning Revitalization Talents Program","doi-asserted-by":"publisher","award":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"],"award-info":[{"award-number":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"]}],"id":[{"id":"10.13039\/501100018617","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation and Entrepreneurship Team of Dalian University","award":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"],"award-info":[{"award-number":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"]}]},{"name":"Natural Science Foundation of Liaoning Province","award":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"],"award-info":[{"award-number":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"]}]},{"DOI":"10.13039\/501100013099","name":"Scientific Research Fund of Liaoning Provincial Education Department","doi-asserted-by":"publisher","award":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"],"award-info":[{"award-number":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"]}],"id":[{"id":"10.13039\/501100013099","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Dalian University Scientific Research Platform Program","award":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"],"award-info":[{"award-number":["2018YF C0910500","61425002","61751203","61772100","61972266","61802040","XLYC2008017","XQN202008","2021MS344","LJKZ1186","202101YB02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>To overcome the lack of flexibility of Harris Hawks Optimization (HHO) in switching between exploration and exploitation, and the low efficiency of its exploitation phase, an efficient improved greedy Harris Hawks Optimizer (IGHHO) is proposed and applied to the feature selection (FS) problem. IGHHO uses a new transformation strategy that enables flexible switching between search and development, enabling it to jump out of local optima. We replace the original HHO exploitation process with improved differential perturbation and a greedy strategy to improve its global search capability. We tested it in experiments against seven algorithms using single-peaked, multi-peaked, hybrid, and composite CEC2017 benchmark functions, and IGHHO outperformed them on optimization problems with different feature functions. We propose new objective functions for the problem of data imbalance in FS and apply IGHHO to it. IGHHO outperformed comparison algorithms in terms of classification accuracy and feature subset length. The results show that IGHHO applies not only to global optimization of different feature functions but also to practical optimization problems.<\/jats:p>","DOI":"10.3390\/e24081065","type":"journal-article","created":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T21:41:37Z","timestamp":1659476497000},"page":"1065","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection"],"prefix":"10.3390","volume":"24","author":[{"given":"Lewang","family":"Zou","sequence":"first","affiliation":[{"name":"Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China"}]},{"given":"Shihua","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China"}]},{"given":"Xiangjun","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Andrei, N. (2017). A SQP Algorithm for Large-Scale Constrained Optimization: SNOPT. Continuous Nonlinear Optimization for Engineering Applications in GAMS Technology, Springer. Springer Optimization and Its Applications.","DOI":"10.1007\/978-3-319-58356-3"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1007\/s00366-020-01258-7","article-title":"An efficient hybrid approach based on Harris Hawks optimization and imperialist competitive algorithm for structural optimization","volume":"38","author":"Kaveh","year":"2022","journal-title":"Eng. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104608","DOI":"10.1016\/j.engappai.2021.104608","article-title":"Adaptive Harris hawks optimization with persistent trigonometric differences for photovoltaic model parameter extraction","volume":"109","author":"Song","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"116432","DOI":"10.1016\/j.eswa.2021.116432","article-title":"Comprehensive learning Harris hawks-equilibrium optimization with terminal replacement mechanism for constrained optimization problems","volume":"192","author":"Zhong","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/TNB.2021.3121278","article-title":"Enhancing Physical and Thermodynamic Properties of DNA Storage Sets with End-constraint","volume":"21","author":"Wu","year":"2021","journal-title":"IEEE Trans. NanoBiosci."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.cmpb.2013.10.007","article-title":"Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis","volume":"113","author":"Inbarani","year":"2014","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.ins.2019.08.040","article-title":"Binary differential evolution with self-learning for multi-objective feature selection","volume":"507","author":"Zhang","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_9","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_10","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_11","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."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"112660","DOI":"10.1016\/j.enconman.2020.112660","article-title":"Boosted mutation-based Harris hawks optimizer for parameters identification of single-diode solar cell models","volume":"209","author":"Ridha","year":"2020","journal-title":"Energy Convers. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jia, H., Lang, C., Oliva, D., Song, W., and Peng, X. (2019). Dynamic harris hawks optimization with mutation mechanism for satellite image segmentation. Remote Sens., 11.","DOI":"10.3390\/rs11121421"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106018","DOI":"10.1016\/j.asoc.2019.106018","article-title":"An intensify Harris Hawks optimizer for numerical and engineering optimization problems","volume":"89","author":"Kamboj","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3741","DOI":"10.1007\/s00366-020-01028-5","article-title":"Boosted binary Harris hawks optimizer and feature selection","volume":"37","author":"Zhang","year":"2021","journal-title":"Eng. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.apm.2020.03.024","article-title":"Harris hawks optimization with information exchange","volume":"84","author":"Qu","year":"2020","journal-title":"Appl. Math. Model."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bui, D.T., Moayedi, H., Kalantar, B., Osouli, A., Pradhan, B., Nguyen, H., and Rashid, A. (2019). A novel swarm intelligence\u2014Harris hawks optimization for spatial assessment of landslide susceptibility. Sensors, 19.","DOI":"10.3390\/s19163590"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Roy, R., Mukherjee, V., and Singh, R.P. (2021). Harris hawks optimization algorithm for model order reduction of interconnected wind turbines. Isa Trans.","DOI":"10.3233\/JIFS-211132"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"14825","DOI":"10.1007\/s00500-020-04834-7","article-title":"A novel quasi-reflected Harris hawks optimization algorithm for global optimization problems","volume":"24","author":"Fan","year":"2020","journal-title":"Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113510","DOI":"10.1016\/j.eswa.2020.113510","article-title":"Opposition-based learning Harris hawks optimization with advanced transition rules: Principles and analysis","volume":"158","author":"Gupta","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10949","DOI":"10.1007\/s13369-020-04896-7","article-title":"Modified Harris Hawks optimization algorithm for global optimization problems","volume":"45","author":"Zhang","year":"2020","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s13042-021-01326-4","article-title":"A self-adaptive Harris Hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection","volume":"13","author":"Hussien","year":"2022","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"160297","DOI":"10.1109\/ACCESS.2020.3013332","article-title":"Improved Harris hawks optimization based on adaptive cooperative foraging and dispersed foraging strategies","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106347","DOI":"10.1016\/j.asoc.2020.106347","article-title":"A competitive chain-based Harris Hawks Optimizer for global optimization and multi-level image thresholding problems","volume":"95","author":"Abd","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"114778","DOI":"10.1016\/j.eswa.2021.114778","article-title":"An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection","volume":"176","author":"Hussain","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1002\/nme.6573","article-title":"A Canis lupus inspired upgraded Harris hawks optimizer for nonlinear, constrained, continuous, and discrete engineering design problem","volume":"122","author":"Nandi","year":"2021","journal-title":"Int. J. Numer. Methods Eng."},{"key":"ref_28","first-page":"1068","article-title":"Improved Butterfly Algorithm for Multi-dimensional Complex Function Optimization Problem","volume":"49","author":"Liu","year":"2021","journal-title":"Acta Electonica Sin."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1016\/j.dt.2021.07.008","article-title":"An improved adaptive differential evolution algorithm for single unmanned aerial vehicle multitasking","volume":"17","author":"Su","year":"2021","journal-title":"Def. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"124435","DOI":"10.1016\/j.jhydrol.2019.124435","article-title":"Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm","volume":"582","author":"Tikhamarine","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/j.ins.2019.07.018","article-title":"An instance voting approach to feature selection","volume":"504","author":"Chamakura","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.ins.2018.12.030","article-title":"Differential mutation and novel social learning particle swarm optimization algorithm","volume":"480","author":"Zhang","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.asoc.2014.11.003","article-title":"A sinusoidal differential evolution algorithm for numerical optimisation","volume":"27","author":"Draa","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.asoc.2018.02.025","article-title":"A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems","volume":"66","author":"Aydilek","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1016\/j.asoc.2017.09.039","article-title":"Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking","volume":"62","author":"Nenavath","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.apm.2019.03.046","article-title":"Multi-strategy boosted mutative whale-inspired optimization approaches","volume":"73","author":"Luo","year":"2019","journal-title":"Appl. Math. Model."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yong, J., He, F., Li, H., and Zhou, W. (2018, January 9\u201311). A novel bat algorithm based on collaborative and dynamic learning of opposite population. Proceedings of the 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD), Nanjing, China.","DOI":"10.1109\/CSCWD.2018.8464759"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5052","DOI":"10.1109\/TPWRS.2018.2812711","article-title":"A hybrid bat algorithm for economic dispatch with random wind power","volume":"33","author":"Liang","year":"2018","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7289674","DOI":"10.1155\/2018\/7289674","article-title":"An improved particle swarm optimization with biogeography-based learning strategy for economic dispatch problems","volume":"2018","author":"Chen","year":"2018","journal-title":"Complexity"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TEVC.2018.2885075","article-title":"Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions","volume":"23","author":"Cao","year":"2018","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"113282","DOI":"10.1016\/j.eswa.2020.113282","article-title":"Orthogonally-designed adapted grasshopper optimization: A comprehensive analysis","volume":"150","author":"Xu","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.swevo.2011.02.002","article-title":"A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms","volume":"1","author":"Derrac","year":"2011","journal-title":"Swarm Evol. Comput."},{"key":"ref_44","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":"Future Gener. Comput. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"194303","DOI":"10.1109\/ACCESS.2020.3033757","article-title":"Dynamic butterfly optimization algorithm for feature selection","volume":"8","author":"Tubishat","year":"2020","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1007\/s11831-020-09420-6","article-title":"Ant lion optimizer: A comprehensive survey of its variants and applications","volume":"28","author":"Abualigah","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1007\/s11831-020-09412-6","article-title":"A comprehensive analysis of nature-inspired meta-heuristic techniques for feature selection problem","volume":"28","author":"Sharma","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_48","unstructured":"Zheng, A., and Casari, A. (2018). Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists, O\u2019Reilly Media, Inc."},{"key":"ref_49","unstructured":"Patterson, G., and Zhang, M. (2007, January 2\u20136). Fitness functions in genetic programming for classification with unbalanced data. Proceedings of the Australasian Joint Conference on Artificial Intelligence, Gold Coast, Australia."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"104210","DOI":"10.1016\/j.engappai.2021.104210","article-title":"Review of swarm intelligence-based feature selection methods","volume":"100","author":"Rostami","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_51","unstructured":"Dheeru, D., and Karra, T.E. (2020, December 23). UCI Machine Learning Repository. Available online: http:\/\/archive.ics.uci.edu\/ml."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"15933","DOI":"10.1007\/s00521-021-06442-4","article-title":"A systematic review on emperor penguin optimizer","volume":"33","author":"Kader","year":"2021","journal-title":"Neural Comput. Appl."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/8\/1065\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:01:13Z","timestamp":1760140873000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/24\/8\/1065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,2]]},"references-count":52,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["e24081065"],"URL":"https:\/\/doi.org\/10.3390\/e24081065","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2022,8,2]]}}}