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CTOA: Toward a Chaotic-Based Tumbleweed Optimization Algorithm

Author

Listed:
  • 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
    Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan)

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.

Suggested Citation

  • Tsu-Yang Wu & Ankang Shao & Jeng-Shyang Pan, 2023. "CTOA: Toward a Chaotic-Based Tumbleweed Optimization Algorithm," Mathematics, MDPI, vol. 11(10), pages 1-43, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2339-:d:1149374
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    References listed on IDEAS

    as
    1. Xingsi Xue & Jianhua Guo & Miao Ye & Jianhui Lv, 2023. "Similarity Feature Construction for Matching Ontologies through Adaptively Aggregating Artificial Neural Networks," Mathematics, MDPI, vol. 11(2), pages 1-24, January.
    2. Jianwei Yang & Zhen Liu & Xin Zhang & Gang Hu, 2022. "Elite Chaotic Manta Ray Algorithm Integrated with Chaotic Initialization and Opposition-Based Learning," Mathematics, MDPI, vol. 10(16), pages 1-34, August.
    3. Kutlu Onay, Funda & Aydemı̇r, Salih Berkan, 2022. "Chaotic hunger games search optimization algorithm for global optimization and engineering problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 514-536.
    4. Gehad Ismail Sayed & Ashraf Darwish & Aboul Ella Hassanien, 2018. "A New Chaotic Whale Optimization Algorithm for Features Selection," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 300-344, July.
    Full references (including those not matched with items on IDEAS)

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