[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121904-en":3,"doc-seo-121904-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121904,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Hybrid Approaches to Optimization and Machine Learning Methods - A Systematic Literature Review","Real-world problems increasingly demand sophisticated models and algorithms that can handle large datasets and support finding optimal solutions quickly. No single optimization or machine learning method is perfect, since each has inherent limitations. By combining optimization and machine learning, hybrid algorithms can integrate complementary strengths and improve efficiency. This work provides a systematic and bibliometric review of hybrid methods for clustering and classification, including bibliometric trends, a recent in-depth review, and a SWOT analysis of the most cited approaches.","Springer Nature 2021 LATEX template  \nHybrid Approaches to Optimization and Machine Learning Methods: a Systematic Literature Review  \nBeatriz Flamia Azevedo 1,2,3*, Ana Maria A. C. Rocha3† and Ana I. Pereira 1,2,3†  \n1* Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Polit´ecnico de Bragan¸ca, Bragan¸ca, 5300-253,  \nPortugal.  \n2 Laborat´orio Associado para a Sustentabilidade e Tecnologia em  \nRegi˜oes de Montanha,Instituto Polit´ecnico de Bragan¸ca, Bragan¸ca, 5300-253, Portugal.  \n3 ALGORITMI Research Centre/LASI, University of Minho,  \nCampus de Gualtar, 4710-057 Braga, Portugal.  \n*Corresponding author(s). E-mail(s): [beatrizflamia@ipb.pt](beatrizflamia@ipb.pt) ; Contributing authors: [arocha@dps.uminho.pt](arocha@dps.uminho.pt) ; [apereira@ipb.pt](apereira@ipb.pt) ;  \n†These authors contributed equally to this work.  \nAbstract  \nNotably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large datasets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigatedor eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the  \nSpringer Nature 2021 LATEX template  \n2 Hybrid Approaches to Optimization and Machine Learning Methods  \nworks published since 1970 is presented. Moreover, an in-depth state-ofart review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithmsand detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.  \nKeywords: machine learning, optimization, hybrid methods, literature  \nreview, clustering, classification  \n1 Introduction  \nMathematical models are present in almost every area of science, as they playa vital role in problem-solving. These models provide a simplified representation of reality from mathematical formulations. These formulations make it possible to understand complex systems, solve problems, and obtain essential information to support intelligent decision-making. Mathematical models use algorithms to find the most appropriate solution to the problem described.  \nNumerical Optimization is a well-known area of Mathematical Sciences that aims to identify extreme points of a function, whether maximum or minimum points. Optimization methods have become a crucial tool for management, decision-making, technology improvement, and development in the last two decades, providing competitive advantages to various systems [1] . Thus, optimization models and algorithms gained visibility in several areas, such as industry [2], disease diagnoses [3], professional and resources scheduling and allocation [4, 5], financial area with capital management and scenarios forecast [6], among others.  \nAnother well-known area focused on solving problems using mathematical models and algorithms is Machine","cbCaieG0VzuKITd9","https://ap.wps.com/l/cbCaieG0VzuKITd9","pdf",1265668,1,58,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Mathematical models and optimization\n## Machine learning and data-driven modeling\n## Motivation for hybrid methodologies","[{\"question\":\"Why are hybrid approaches important for optimization and machine learning?\",\"answer\":\"Real problems are complex and no single optimization or machine learning method fully solves them because each has limitations. Hybrid approaches integrate strengths from both areas to improve efficiency and overcome weaknesses.\"},{\"question\":\"How does the review study hybrid methods in this paper?\",\"answer\":\"It presents a systematic and bibliometric literature review focused on hybrid methods combining optimization and machine learning for clustering and classification, including numerical overview coverage since 1970 and an in-depth review of the last three years.\"},{\"question\":\"What additional analysis is performed on the cited algorithms?\",\"answer\":\"A SWOT analysis is conducted on the ten most cited algorithms from the collected database to assess strengths and weaknesses, and to identify opportunities and threats discussed through hybrid methods.\"}]","Hybrid Approaches to Optimization and Machine Learning Methods - A Systematic Literature Review | PDF",1785807660,146,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"hybrid-approaches-to-optimization-and-machine-learning-methods-a-systematic-literature-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/hybrid-approaches-to-optimization-and-machine-learning-methods-a-systematic-literature-review/121904/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are hybrid approaches important for optimization and machine learning?","Question",{"text":75,"@type":76},"Real problems are complex and no single optimization or machine learning method fully solves them because each has limitations. Hybrid approaches integrate strengths from both areas to improve efficiency and overcome weaknesses.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the review study hybrid methods in this paper?",{"text":80,"@type":76},"It presents a systematic and bibliometric literature review focused on hybrid methods combining optimization and machine learning for clustering and classification, including numerical overview coverage since 1970 and an in-depth review of the last three years.",{"name":82,"@type":73,"acceptedAnswer":83},"What additional analysis is performed on the cited algorithms?",{"text":84,"@type":76},"A SWOT analysis is conducted on the ten most cited algorithms from the collected database to assess strengths and weaknesses, and to identify opportunities and threats discussed through hybrid methods.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]