[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118902-en":3,"doc-seo-118902-105":30,"detail-sidebar-cat-0-en-105":90},{"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":4,"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},118902,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Hybrid Approaches to Optimization and Machine Learning Methods","This paper delivers a comprehensive literature review of hybrid techniques that integrate optimization and machine learning for clustering and classification. The study surveys optimization and machine learning approaches, quantifies publication trends since 1970, and reviews detailed developments from the last three years. A bibliometric methodology based on three databases is complemented by a SWOT analysis of the top ten most cited algorithms, highlighting strengths, weaknesses, opportunities, and threats, and showing how these integrations address limitations of standalone techniques.","2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA) | 979-8-3503-4503-2/23/$31.00 ©2023 IEEE | DOI: 10. 1 109/DSAA60987.2023. 10302494  \nHybrid Approaches to Optimization and Machine  \nLearning Methods  \n1st Beatriz Flamia Azevedo  \nResearch Centre in Digitalization and Intelligent Robotics (CeDRI/SusTEC), Instituto Polit e´cnico de Braganc¸a, Braganc¸a, Portugal  \nALGORITMI Research Centre/LASI, University of Minho, Braga, Portugal  \n[beatrizflamia@ipb.pt](beatrizflamia@ipb.pt)  \n2nd Ana Maria A. C. Rocha  \nALGORITMI Research Centre/LASI University of Minho Braga, Portugal [arocha@dps.uminho.pt](arocha@dps.uminho.pt)  \n3rd Ana I. Pereira  \nResearch Centre in Digitalization and Intelligent Robotics (CeDRI/SusTEC) Instituto Polite´cnico de Braganc¸a  \nBraganc¸a, Portugal  \n[apereira@ipb.pt](apereira@ipb.pt)  \nAbstract—This paper conducts a comprehensive literature review concerning hybrid techniques that combine optimization and machine learning approaches for clustering and classification problems. The aim is to identify the potential benefits of integrating these methods to address challenges in both fields. The paper outlines optimization and machine learning methods and provides a quantitative overview of publications since 1970. Additionally, it offers a detailed review of recent advancementsin the last three years. The study includes a SWOT analysis of the top ten most cited algorithms from the collected database, examining their strengths and weaknesses as well as uncovering opportunities and threats explored through hybrid approaches. Through this research, the study highlights significant findingsin the realm of hybrid methods for clustering and classification, showcasing how such integrations can enhance the shortcomings of individual techniques.  \nIndex Terms—machine learning, optimization, hybrid methods, literature review, clustering, classification.  \nI. INTRODUCTION  \nIn addressing the growing complexity of real-world challenges and the need for efficient solutions for large datasets, the demand for advanced models and algorithms has risen, both in optimization and machine learning. A hybrid algorithm can combine optimization and machine learning techniques is an effective strategy that uses the advantages of both methodologies to provide a powerful framework for tackling complex problems. This fusion improves decision-making by blending optimization into machine learning and vice versa. Thereby, a hybrid algorithm can leverage optimization capabilities to guide the learning process and enhance the accuracy and efficiency of decision-making. This integration empowers the algorithm to use mathematical optimization and learning, resulting in better decision-making [2]–[4] .  \nThe authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020), Algoritmi (UIDB/00319/2020) and SusTEC (LA/P/0007/2021) . Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 .  \nThis paper describes and explores the main characteristics of numerical optimization and machine learning methods. Furthermore, a deep and systematic literature review looks at how these methods have evolved and analyze how they can be combined to overcome challenges. The goal is to find ways to make them work better together, like using ideas from one method to improve the other. Although the work presents the different types of machine learning in detail, the literature review will be restricted to algorithms that perform the classification or clustering task.  \nIn this way, this paper makes a significant contribution by systematically identifying and analyzing the existing knowledge on hybrid algorithms that combine optimization and machine learning. It finds gaps in research and suggests future directions. Additionally, the paper presents a comprehensive SWOT analysis of the","cbCaidPRUSrVHExq","https://ap.wps.com/l/cbCaidPRUSrVHExq","pdf",110011,1,2,"English","en",105,"# Introduction\n# Methodology and Data Base\n# Summary","[{\"question\":\"What problem domains does the paper focus on for hybrid optimization and machine learning methods?\",\"answer\":\"It focuses on clustering and classification problems, emphasizing how hybrid designs address challenges in both optimization and machine learning.\"},{\"question\":\"How does the paper conduct its literature review and bibliometric analysis?\",\"answer\":\"It performs a historical survey of works since 1970 and applies a logic search across Scopus, IEEE, and WoS with constraints on year, language, and publisher type, followed by systematic and bibliometric analysis.\"},{\"question\":\"What does the SWOT analysis cover in this study?\",\"answer\":\"The paper performs a SWOT analysis of the top ten most frequently cited algorithms in the collected dataset, evaluating their strengths, weaknesses, opportunities, and threats.\"}]","Hybrid Approaches to Optimization and Machine Learning Methods | 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problem domains does the paper focus on for hybrid optimization and machine learning methods?","Question",{"text":74,"@type":75},"It focuses on clustering and classification problems, emphasizing how hybrid designs address challenges in both optimization and machine learning.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the paper conduct its literature review and bibliometric analysis?",{"text":79,"@type":75},"It performs a historical survey of works since 1970 and applies a logic search across Scopus, IEEE, and WoS with constraints on year, language, and publisher type, followed by systematic and bibliometric analysis.",{"name":81,"@type":72,"acceptedAnswer":82},"What does the SWOT analysis cover in this study?",{"text":83,"@type":75},"The paper performs a SWOT analysis of the top ten most frequently cited algorithms in the collected dataset, evaluating their strengths, weaknesses, opportunities, and 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