Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (1)

Search Parameters:
Keywords = tumbleweed optimization algorithm

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
43 pages, 19537 KB  
Article
CTOA: Toward a Chaotic-Based Tumbleweed Optimization Algorithm
by Tsu-Yang Wu, Ankang Shao and Jeng-Shyang Pan
Mathematics 2023, 11(10), 2339; https://doi.org/10.3390/math11102339 - 17 May 2023
Cited by 140 | Viewed by 2932
Abstract
Metaheuristic algorithms are an important area of research in artificial intelligence. The tumbleweed optimization algorithm (TOA) is the newest metaheuristic optimization algorithm that mimics the growth and reproduction of tumbleweeds. In practice, chaotic maps have proven to be an improved method of optimization [...] Read more.
Metaheuristic algorithms are an important area of research in artificial intelligence. The tumbleweed optimization algorithm (TOA) is the newest metaheuristic optimization algorithm that mimics the growth and reproduction of tumbleweeds. In practice, chaotic maps have proven to be an improved method of optimization algorithms, allowing the algorithm to jump out of the local optimum, maintain population diversity, and improve global search ability. This paper presents a chaotic-based tumbleweed optimization algorithm (CTOA) that incorporates chaotic maps into the optimization process of the TOA. By using 12 common chaotic maps, the proposed CTOA aims to improve population diversity and global exploration and to prevent the algorithm from falling into local optima. The performance of CTOA is tested using 28 benchmark functions from CEC2013, and the results show that the circle map is the most effective in improving the accuracy and convergence speed of CTOA, especially in 50D. Full article
Show Figures

Figure 1

Back to TopTop