{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:08:25Z","timestamp":1760144905216,"version":"build-2065373602"},"reference-count":64,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T00:00:00Z","timestamp":1716854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2023YFB3308802","52275480","QKHZYD [2023]002"],"award-info":[{"award-number":["2023YFB3308802","52275480","QKHZYD [2023]002"]}]},{"name":"National Natural Science Foundation of China\u2019s Top-Level Program","award":["2023YFB3308802","52275480","QKHZYD [2023]002"],"award-info":[{"award-number":["2023YFB3308802","52275480","QKHZYD [2023]002"]}]},{"name":"Reserve Projects for Centralized Guidance of Local Science and Technology Development Funds","award":["2023YFB3308802","52275480","QKHZYD [2023]002"],"award-info":[{"award-number":["2023YFB3308802","52275480","QKHZYD [2023]002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>The growth optimizer (GO) is a novel metaheuristic algorithm designed to tackle complex optimization problems. Despite its advantages of simplicity and high efficiency, GO often encounters localized stagnation when dealing with discretized, high-dimensional, and multi-constraint problems. To address these issues, this paper proposes an enhanced version of GO called CODGBGO. This algorithm incorporates three strategies to enhance its performance. Firstly, the Circle-OBL initialization strategy is employed to enhance the quality of the initial population. Secondly, an exploration strategy is implemented to improve population diversity and the algorithm\u2019s ability to escape local optimum traps. Finally, the exploitation strategy is utilized to enhance the convergence speed and accuracy of the algorithm. To validate the performance of CODGBGO, it is applied to solve the CEC2017, CEC2020, 18 feature selection problems, and 4 real engineering optimization problems. The experiments demonstrate that the novel CODGBGO algorithm effectively addresses the challenges posed by complex optimization problems, offering a promising approach.<\/jats:p>","DOI":"10.3390\/axioms13060361","type":"journal-article","created":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T13:32:55Z","timestamp":1716903175000},"page":"361","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-Strategy-Improved Growth Optimizer and Its Applications"],"prefix":"10.3390","volume":"13","author":[{"given":"Rongxiang","family":"Xie","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Guizhou University, Guiyang 550025, China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liya","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4759-6000","authenticated-orcid":false,"given":"Shaobo","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengbin","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0227-020X","authenticated-orcid":false,"given":"Panliang","family":"Yuan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,28]]},"reference":[{"key":"ref_1","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_2","first-page":"116","article-title":"Virtual Factory System Design and Implementation: Integrated Sustainable Manufacturing","volume":"5","author":"Hao","year":"2018","journal-title":"Int. J. Syst. Sci. Oper. Logist."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1061\/(ASCE)0733-9496(1994)120:4(423)","article-title":"Genetic algorithms compared to other techniques for pipe optimization","volume":"120","author":"Simpson","year":"1994","journal-title":"J. Water Resour. Plan. Manag."},{"key":"ref_4","first-page":"262","article-title":"Modelling And Optimal Lot-Sizing of the Replenishments in Constrained, Multi-Product and Bi-Objective EPQ Models with Defective Products: Generalised Cross Decomposition","volume":"7","author":"Gharaei","year":"2020","journal-title":"Int. J. Syst. Sci. Oper. Logist."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1080\/0305215X.2019.1624740","article-title":"New Binary Whale Optimization Algorithm for Discrete Optimization Problems","volume":"52","author":"Hussien","year":"2020","journal-title":"Eng. Optim."},{"key":"ref_6","first-page":"182","article-title":"An Integrated Approach Based on System Dynamics and ANP for Evaluating Sustainable Transportation Policies","volume":"7","author":"Sayyadi","year":"2020","journal-title":"Int. J. Syst. Sci. Oper. Logist."},{"key":"ref_7","first-page":"3","article-title":"Evolution Strategies-A Comprehensive Introduction Evolution Strategies A Comprehensive Introduction","volume":"1","author":"Schwefel","year":"2002","journal-title":"ACM Comput. Classif."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1109\/TEVC.2008.919004","article-title":"Biogeography-Based Optimization","volume":"12","author":"Simon","year":"2008","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/S1474-6670(17)49015-X","article-title":"Genetic algorithms in control systems engineering","volume":"26","author":"Fleming","year":"1993","journal-title":"IFAC Proc. Vol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s10462-022-10173-w","article-title":"Fire Hawk Optimizer: A Novel Metaheuristic Algorithm","volume":"56","author":"Azizi","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1007\/s10489-022-03533-0","article-title":"FOX: A FOX-Inspired Optimization Algorithm","volume":"53","author":"Mohammed","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"116924","DOI":"10.1016\/j.eswa.2022.116924","article-title":"Golden Jackal Optimization: A Novel Nature-Inspired Optimizer for Engineering Applications","volume":"198","author":"Chopra","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.advengsoft.2005.04.005","article-title":"A New Optimization Method: Big Bang-Big Crunch","volume":"37","author":"Erol","year":"2006","journal-title":"Adv. Eng. Softw."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.patrec.2017.10.031","article-title":"Magnetic Optimization Algorithm for Data Clustering","volume":"115","author":"Kushwaha","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.compstruc.2016.01.008","article-title":"Water Evaporation Optimization: A Novel Physically Inspired Optimization Algorithm","volume":"167","author":"Kaveh","year":"2016","journal-title":"Comput. Struct."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.knosys.2018.08.030","article-title":"Atom Search Optimization and Its Application to Solve a Hydrogeologic Parameter Estimation Problem","volume":"163","author":"Zhao","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113698","DOI":"10.1016\/j.eswa.2020.113698","article-title":"Search and Rescue Optimization Algorithm: A New Optimization Method for Solving Constrained Engineering Optimization Problems","volume":"161","author":"Shabani","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1007\/s10489-017-0903-6","article-title":"Human Mental Search: A New Population-Based Metaheuristic Optimization Algorithm","volume":"47","author":"Mousavirad","year":"2017","journal-title":"Appl. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113609","DOI":"10.1016\/j.cma.2020.113609","article-title":"The Arithmetic Optimization Algorithm","volume":"376","author":"Abualigah","year":"2021","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"108630","DOI":"10.1016\/j.asoc.2022.108630","article-title":"Binary Artificial Algae Algorithm for Feature Selection [Formula Presented]","volume":"120","author":"Turkoglu","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107638","DOI":"10.1016\/j.knosys.2021.107638","article-title":"An Enhanced Black Widow Optimization Algorithm for Feature Selection","volume":"235","author":"Hu","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11395","DOI":"10.1007\/s00500-023-08274-x","article-title":"Binary Arithmetic Optimization Algorithm for Feature Selection","volume":"27","author":"Xu","year":"2023","journal-title":"Soft Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"114901","DOI":"10.1016\/j.cma.2022.114901","article-title":"An Enhanced Hybrid Arithmetic Optimization Algorithm for Engineering Applications","volume":"394","author":"Hu","year":"2022","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"110206","DOI":"10.1016\/j.knosys.2022.110206","article-title":"Growth Optimizer: A Powerful Metaheuristic Algorithm for Solving Con-tinuous and Discrete Global Optimization Problems","volume":"261","author":"Zhang","year":"2023","journal-title":"Knowl. Based Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Aribia, H.B., El-Rifaie, A.M., Tolba, M.A., Shaheen, A., Moustafa, G., Elsayed, F., and Elshahed, M. (2023). Growth Optimizer for Parameter Identification of Solar Photovoltaic Cells and Modules. Sustainability, 15.","DOI":"10.3390\/su15107896"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hakmi, S.H., Alnami, H., Moustafa, G., Ginidi, A.R., and Shaheen, A.M. (2024). Modified Rime-Ice Growth Optimizer with Polynomial Differential Learning Operator for Single- and Double-Diode PV Parameter Estimation Problem. Electronics, 13.","DOI":"10.3390\/electronics13091611"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"121218","DOI":"10.1016\/j.eswa.2023.121218","article-title":"Quadruple Parameter Adaptation Growth Optimizer with Integrated Distribution, Confrontation, and Balance Features for Optimization","volume":"235","author":"Gao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fatani, A., Dahou, A., Abd Elaziz, M., Al-qaness, M.A.A., Lu, S., Alfadhli, S.A., and Alresheedi, S.S. (2023). Enhancing Intrusion Detection Systems for IoT and Cloud Environments Using a Growth Optimizer Algorithm and Conventional Neural Networks. Sensors, 23.","DOI":"10.3390\/s23094430"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3979","DOI":"10.1007\/s10462-021-10100-5","article-title":"Chaotic Slime Mould Optimization Algorithm for Global Optimization","volume":"55","author":"Altay","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"8989","DOI":"10.1007\/s00521-022-06921-2","article-title":"Large Scale Salp-Based Grey Wolf Optimization for Feature Selection and Global Optimization","volume":"34","author":"Qaraad","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"11835","DOI":"10.1016\/j.aej.2022.05.028","article-title":"Differential Evolution with Modified Initialization Scheme Using Chaotic Oppositional Based Learning Strategy","volume":"61","author":"Ahmad","year":"2022","journal-title":"Alex. Eng. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"16718","DOI":"10.1007\/s10489-021-03037-3","article-title":"Chaotic Arithmetic Optimization Algorithm","volume":"52","author":"Li","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1007\/s00366-021-01545-x","article-title":"Differential Evolution-Assisted Salp Swarm Algorithm with Chaotic Structure for Real-World Problems","volume":"39","author":"Zhang","year":"2023","journal-title":"Eng. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"162059","DOI":"10.1109\/ACCESS.2021.3133286","article-title":"Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems","volume":"9","author":"Dehghani","year":"2021","journal-title":"IEEE Access."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1016\/j.ins.2022.06.029","article-title":"Random Neighbor Elite Guided Differential Evolution for Global Numerical Optimization","volume":"607","author":"Yang","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_40","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle Swarm Optimization. Proceedings of the ICNN\u201995-International Conference on Neural Networks, Perth, WA, Australia."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2013a simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TEVC.2005.857610","article-title":"Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions","volume":"10","author":"Liang","year":"2006","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"113875","DOI":"10.1016\/j.eswa.2020.113875","article-title":"Bezier Search Differential Evolution Algorithm for Numerical Function Optimization: A Comparative Study with CRMLSP, MVO, WA, SHADE and LSHADE","volume":"165","author":"Civicioglu","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6603","DOI":"10.1007\/s00521-022-08013-7","article-title":"Bernstein-Levy Differential Evolution Algorithm for Numerical Function Optimization","volume":"35","author":"Civicioglu","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Malik, N.A., Chang, C.L., Chaudhary, N.I., Raja, M.A.Z., Cheema, K.M., Shu, C.M., and Alshamrani, S.S. (2022). Knacks of Fractional Order Swarming Intelligence for Parameter Estimation of Harmonics in Electrical Systems. Mathematics, 10.","DOI":"10.3390\/math10091570"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Mehmood, K., Chaudhary, N.I., Khan, Z.A., Cheema, K.M., and Raja, M.A.Z. (2023). Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model. Biomimetics, 8.","DOI":"10.3390\/biomimetics8020141"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.cad.2010.12.015","article-title":"Teaching-Learning-Based Optimization: A Novel Method for Constrained Mechanical Design Optimization Problems","volume":"43","author":"Rao","year":"2011","journal-title":"CAD Comput. Aided Des."},{"key":"ref_48","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_49","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":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_50","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_51","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_52","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.jocs.2017.06.003","article-title":"A New Meta-Heuristic Butterfly-Inspired Algorithm","volume":"23","author":"Qi","year":"2017","journal-title":"J. Comput. Sci."},{"key":"ref_53","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_54","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_55","doi-asserted-by":"crossref","first-page":"7305","DOI":"10.1007\/s11227-022-04959-6","article-title":"Dung Beetle Optimizer: A New Meta-Heuristic Algorithm for Global Optimization","volume":"79","author":"Xue","year":"2023","journal-title":"J. Supercomput."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"116516","DOI":"10.1016\/j.eswa.2022.116516","article-title":"INFO: An Efficient Optimization Algorithm Based on Weighted Mean of Vectors","volume":"195","author":"Ahmadianfar","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"108320","DOI":"10.1016\/j.knosys.2022.108320","article-title":"Snake Optimizer: A Novel Meta-Heuristic Optimization Algorithm","volume":"242","author":"Hashim","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_58","unstructured":"IEEE Computational Intelligence Society, and Institute of Electrical and Electronics Engineers (2014, January 6\u201311). Behavioral Study of the Surrogate Model-aware Evolutionary Search Framework. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, Beijing, China."},{"key":"ref_59","unstructured":"Institute of Electrical and Electronics Engineers, and IEEE Computational Intelligence Society (2020, January 19\u201324). Hybrid Single and Multiobjective Optimization for Engineering Design without Exact Specifications. Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC): 2020 Conference Proceedings, Glasgow, UK."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.ins.2022.05.058","article-title":"A Novel Adaptive L-SHADE Algorithm and Its Application in UAV Swarm Resource Configuration Problem","volume":"606","author":"Li","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.jare.2022.01.002","article-title":"Shape and Size Optimization of Truss Structures by Chaos Game Optimization Considering Frequency Constraints","volume":"41","author":"Azizi","year":"2022","journal-title":"J. Adv. Res."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"105521","DOI":"10.1016\/j.engappai.2022.105521","article-title":"A Qualitative Systematic Review of Metaheuristics Applied to Tension\/Compression Spring Design Problem: Current Situation, Recommendations, and Research Direction","volume":"118","author":"Tzanetos","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"100693","DOI":"10.1016\/j.swevo.2020.100693","article-title":"A Test-Suite of Non-Convex Constrained Optimization Problems from the Real-World and Some Baseline Results","volume":"56","author":"Kumar","year":"2020","journal-title":"Swarm Evol. Comput."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1007\/s10845-010-0393-4","article-title":"Artificial Bee Colony Algorithm for Large-Scale Problems and Engineering Design Optimization","volume":"23","author":"Akay","year":"2012","journal-title":"J. Intell. Manuf."}],"container-title":["Axioms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1680\/13\/6\/361\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:49:42Z","timestamp":1760107782000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1680\/13\/6\/361"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,28]]},"references-count":64,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["axioms13060361"],"URL":"https:\/\/doi.org\/10.3390\/axioms13060361","relation":{},"ISSN":["2075-1680"],"issn-type":[{"type":"electronic","value":"2075-1680"}],"subject":[],"published":{"date-parts":[[2024,5,28]]}}}