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Using particle swarm optimization and genetic algorithms for optimal control of non-linear fractional-order chaotic system of cancer cells

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  • Mohammadi, Shaban
  • Hejazi, S. Reza

Abstract

The purpose of this paper is to investigate optimal control the non-linear fractional-order chaotic system of cancer cells by use of particle swarm optimization and genetic algorithms. The chaotic behavior of cancer cell growth and the tumor growth model were expressed as a system of differential equations. Then, optimal control of cancer cells using drugs was presented. Particle swarm optimization method and genetic algorithms were used to solve the problem of optimal control of cancer cell growth. The results of the control applied to the model can control the cancer cell growth. The application of control results in decreasing the number of cancer cells to zero. When the controller is applied from the beginning, the Results of genetic algorithm method are excellent. All the results obtained for the particle swarm optimization method show that this method has also been very successful and have results very close to the genetic algorithm method.

Suggested Citation

  • Mohammadi, Shaban & Hejazi, S. Reza, 2023. "Using particle swarm optimization and genetic algorithms for optimal control of non-linear fractional-order chaotic system of cancer cells," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 206(C), pages 538-560.
    • Handle: RePEc:eee:matcom:v:206:y:2023:i:c:p:538-560
    DOI: 10.1016/j.matcom.2022.11.023
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    References listed on IDEAS

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    1. El-Gohary, Awad & Alwasel, I.A., 2009. "The chaos and optimal control of cancer model with complete unknown parameters," Chaos, Solitons & Fractals, Elsevier, vol. 42(5), pages 2865-2874.
    2. Xiaojun Liu & Ling Hong & Lixin Yang & Dafeng Tang, 2019. "Bifurcations of a New Fractional-Order System with a One-Scroll Chaotic Attractor," Discrete Dynamics in Nature and Society, Hindawi, vol. 2019, pages 1-15, January.
    3. Yanxin Liu & Johnny Siu-Hang Li, 2021. "An Efficient Method for Mitigating Longevity Value-at-Risk," North American Actuarial Journal, Taylor & Francis Journals, vol. 25(S1), pages 309-340, February.
    4. Habibi, Noora & Lashkarian, Elham & Dastranj, Elham & Hejazi, S. Reza, 2019. "Lie symmetry analysis, conservation laws and numerical approximations of time-fractional Fokker–Planck equations for special stochastic process in foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 750-766.
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    Cited by:

    1. Abrar Yaqoob & Rabia Musheer Aziz & Navneet Kumar Verma & Praveen Lalwani & Akshara Makrariya & Pavan Kumar, 2023. "A Review on Nature-Inspired Algorithms for Cancer Disease Prediction and Classification," Mathematics, MDPI, vol. 11(5), pages 1-32, February.

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