CTOA: Toward a Chaotic-Based Tumbleweed Optimization Algorithm

Tsu-Yang Wu, Ankang Shao and Jeng-Shyang Pan ()
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Tsu-Yang Wu: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Ankang Shao: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Jeng-Shyang Pan: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Mathematics, 2023, vol. 11, issue 10, 1-43

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 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.

Keywords: tumbleweed optimization algorithm; chaotic map; random initialization; metaheuristic optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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