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

TLDR

Real‑world problems are increasingly complex, demanding sophisticated models that can handle large data sets, yet no single method is perfect, so combining different approaches can mitigate their limitations. The study aims to develop and evaluate hybrid algorithms that merge optimization and machine learning techniques to enhance efficiency and overcome the shortcomings of each method. A systematic and bibliometric review was conducted, summarizing optimization and machine learning methods, providing a numerical overview of publications since 1970, a recent state‑of‑art analysis, and a SWOT assessment of the ten most cited algorithms. The review identified key hybrid approaches for clustering and classification, highlighted their successes, and showed how they address the weaknesses of pure methods.

Abstract

Abstract 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.