A comprehensive survey: Nature-inspired algorithms
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Recently, metaheuristic algorithms have become increasingly important. The purpose of this chapter is to provide readers with an overview of metaheuristic algorithms. This chapter provides an overview of the key elements of these metaheuristic algorithms including physics-based, evolution-based, and swarm-based algorithms and their evolutionary operators and functionalities. There have also been surveys examining these algorithms, but a comprehensive comparison and contrast study is lacking in current survey papers. As this chapter will introduce each algorithm individually, detailed introductions will be provided for each algorithm. There has been a great deal of effort devoted to this chapter to compare the metaheuristic algorithms that have been proposed in the last decade and, from among them, the most popular ones have been chosen for discussion in this chapter. Each algorithm has been evaluated according to the performance of well-known benchmark functions to determine its performance. As a result of this comparative study, we are aiming to provide a broader view of nature-inspired algorithms and meaningful insights into their design and implementation. The remaining of this section is: Nature-inspired algorithms. Physics-based algorithms. Evolution-based algorithms. Swarm-based algorithms. Multiobjective algorithms. Unconstrained/constrained nonlinear optimization. © 2024 Elsevier Inc. All rights reserved.
Açıklama
Anahtar Kelimeler
Metaheuristic Algorithm, Nature-İnspired, Optimization Algorithm
Kaynak
Decision-Making Models: A Perspective of Fuzzy Logic and Machine Learning
