A review of classical methods and Nature-Inspired Algorithms (NIAs) for optimization problems
Optimization techniques are among the most promising methods to deal with real-world problems, consisting of several objective functions and constraints. Over the decades, many methods have come into existence to solve optimization problems. However, the complexity of these problems is increasing ov...
Uložené v:
| Vydané v: | Results in control and optimization Ročník 13; s. 100315 |
|---|---|
| Hlavný autor: | |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
01.12.2023
Elsevier |
| Predmet: | |
| ISSN: | 2666-7207, 2666-7207 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Shrnutí: | Optimization techniques are among the most promising methods to deal with real-world problems, consisting of several objective functions and constraints. Over the decades, many methods have come into existence to solve optimization problems. However, the complexity of these problems is increasing over time. Thereby, it opens up a field of research in developing a robust procedure compatible with such complex optimization problems that provide optimal solutions best suited to the needs of the decision-makers. This review paper presents a survey of the recent use of classical methods and Nature-Inspired Algorithms (NIAs) to solve single and multiple objective problems of optimization in diverse application areas. Moreover, this study briefly describes these widely used solution methods based on the classification of classical approaches and NIAs. Recently published articles based on real-world applications have been included to demonstrate the advantages of each solution technique. In addition, research gaps involving various techniques and future prospects within this field are discussed.
•This article reviews the classical methods and NIAs for optimization problems.•Classifications among the algorithms inspired by the nature family are discussed.•The research issues and gaps identified from the analysis are pointed out.•Future prospects for the use of different classical methods & NIAs are discussed. |
|---|---|
| ISSN: | 2666-7207 2666-7207 |
| DOI: | 10.1016/j.rico.2023.100315 |