Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review

For years, extensive research has been in the binarization of continuous metaheuristics for solving binary-domain combinatorial problems. This paper is a continuation of a previous review and seeks to draw a comprehensive picture of the various ways to binarize this type of metaheuristics; the study uses a standard systematic review consisting of the analysis of 512 publications from 2017 to January 2022 (5 years). The work will provide a theoretical foundation for novice researchers tackling combinatorial optimization using metaheuristic algorithms and for expert researchers analyzing the binarization mechanism's impact on the metaheuristic algorithms' performance. Structuring this information allows for improving the results of metaheuristics and broadening the spectrum of binary problems to be solved. We can conclude from this study that there is no single general technique capable of efficient binarization; instead, there are multiple forms with different performances.

Automated summary

This study is a continuation of research on the binarization of continuous metaheuristics for solving binary-domain combinatorial problems. By analyzing 512 publications from 2017 to January 2022, the authors provide a comprehensive overview of the various ways to binarize this type of metaheuristics. The findings offer a theoretical foundation for novice researchers and expert researchers in the field of combinatorial optimization using metaheuristic algorithms, and highlight the impact of binarization mechanism on the performance of metaheuristic algorithms. The study emphasizes that there is no single general technique for efficient binarization, but rather multiple forms with different performances.