 Using particle swarm optimization and genetic algorithms for optimal control of non-linear fractional-order chaotic system of cancer cellsShaban Mohammadi and S. Reza Hejazi Mathematics and Computers in Simulation (MATCOM), 2023, vol. 206, issue C, 538-560 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. Keywords: Particle swarm optimization; Genetic algorithm; Optimal control; Chaotic system; Fractional-order (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:206:y:2023:i:c:p:538-560 DOI: 10.1016/j.matcom.2022.11.023 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
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