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Artificial Intelligence And Stochastic Optimization Algorithms For The Chaotic Datasets

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  • FUZHANG WANG

    (Nanchang Institute of Technology, Nanchang 330044, P. R. China†School of Mathematics and Statistics, Xuzhou University of Technology, Xuzhou 2221018, Jiangsu, P. R. China)

  • AYESHA SOHAIL

    (��Department of Mathematics, COMSATS University Islamabad, Lahore Campus 54000, Pakistan)

  • WING-KEUNG WONG

    (�Department of Finance, FinTech & Blockchain Research Center and Big Data Research Center, Asia University, Taiwan¶Department of Medical Research, China Medical University Hospital, Yude Road, North District, Taichung City 404327, Taiwan, R.O.C.∥Department of Economics and Finance, The Hang Seng University of Hong Kong, Hang Shin Link, Siu Lek Yuen, Hong Kong)

  • QURAT UL AIN AZIM

    (��Department of Mathematics, COMSATS University Islamabad, Lahore Campus 54000, Pakistan)

  • SHABIEH FARWA

    (*Department of Mathematics, COMSATS University Islamabad, WahCant Campus, Mall Road, Quaid Avenue, Wah Cantt, Rawalpindi, Punjab, Pakistan)

  • MARIA SAJAD

    (��Department of Mathematics, COMSATS University Islamabad, Lahore Campus 54000, Pakistan)

Abstract

Almost every natural process is stochastic due to the basic consequences of nature’s existence and the dynamical behavior of each process that is not stationary but evolves with the passage of time. These stochastic processes not only exist and appear in the fields of biological sciences but are also evident in industrial, agricultural and economical research datasets. Stochastic processes are challenging to model and to solve as well. The stochastic patterns when repeated result into random fractals and are very common in natural processes. These processes are usually simulated with the aid of smart computational and optimization tools. With the progress in the field of artificial intelligence, smart tools are developed that can model the stochastic processes by generalization and genetic optimization. Based on the basic theoretical description of the stochastic optimization algorithms, the stochastic learning tools, stochastic modeling, stochastic approximation and stochastic fractals, a comparative analysis is presented with the aid of the stochastic fractal search, multi-objective stochastic fractal search and pattern search algorithms.

Suggested Citation

  • Fuzhang Wang & Ayesha Sohail & Wing-Keung Wong & Qurat Ul Ain Azim & Shabieh Farwa & Maria Sajad, 2023. "Artificial Intelligence And Stochastic Optimization Algorithms For The Chaotic Datasets," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 31(06), pages 1-14.
    • Handle: RePEc:wsi:fracta:v:31:y:2023:i:06:n:s0218348x22401752
    DOI: 10.1142/S0218348X22401752
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    Cited by:

    1. Junxiang Li & Ziang Li & Jian Zhang & Shuyuan Zhao & Feitian Cheng & Chuan Qian & Xingyu Hu & Guoxiang Zhou, 2023. "Automated Monitoring of the Uniform Demagnetization Faults in Permanent-Magnet Synchronous Motors: Practical Methods and Challenges," Sustainability, MDPI, vol. 15(23), pages 1-22, November.
    2. Khondaker Sazzadul Karim & Mohammad Ekramol Islam & Abdullah Mohammed Ibrahim & Shin-Hung Pan & Md. Mominur Rahman, 2023. "Online Marketing Trends and Purchasing Intent Advances in Customer Satisfaction through PLS-SEM and ANN Approach," Advances in Decision Sciences, Asia University, Taiwan, vol. 27(4), pages 24-54, December.

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