Hybrid approaches to optimization and machine learning methods: a systematic literature review

Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by co...

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Vydáno v:Machine learning Ročník 113; číslo 7; s. 4055 - 4097
Hlavní autoři: Azevedo, Beatriz Flamia, Rocha, Ana Maria A. C., Pereira, Ana I.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.07.2024
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Bibliometrics
Classification
Clustering
Computer Science
Control
Literature reviews
Machine Learning
Mechatronics
Natural Language Processing (NLP)
Optimization
Robotics
Simulation and Modeling
Special Issue on Emerging Applications and Frontiers for Data Science (DSAA 2023)
SWOT analysis
Literature review
Hybrid methods
Clustering
Optimization
Machine learning
Classification
ISSN:0885-6125, 1573-0565
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Shrnutí:Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or 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 works published since 1970 is presented. Moreover, an in-depth state-of-art 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 algorithms and 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.
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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-023-06467-x