CTOA: Toward a Chaotic-Based Tumbleweed Optimization Algorithm

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.

Automated summary

Metaheuristic algorithms are important in the field of artificial intelligence, and the tumbleweed optimization algorithm (TOA) is a new algorithm that mimics the growth and reproduction of tumbleweeds. Chaotic maps have been proven to be an improved method for optimization algorithms, and this paper presents a chaotic-based TOA (CTOA) that incorporates chaotic maps into the optimization process. The CTOA aims to improve population diversity, global exploration, and prevent falling into local optima. The performance of CTOA is tested using 28 benchmark functions, and the circle map is found to be the most effective in improving accuracy and convergence speed, especially in 50D.