Evolutionary Dynamics-Driven Learning of Optimal Decisions for Nonlinear System: Extend-Policy Iterative Algorithm

An extend-policy iterative algorithm is proposed for solving the ecological evolving-lung cancer cells growth inhibition optimal drug delivery scheme. With the analysis of the cell proliferation-apoptosis process of lung cancer cells with primitive immune system and external drug interventions, such...

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Bibliographic Details
Published inIEEE transactions on cybernetics Vol. 54; no. 11; pp. 6583 - 6592
Main Authors Tan, Zilong, Sun, Jiayue, Zhao, Zhenjin, Li, Xiaoxiao, Zou, Zifang
Format Journal Article
LanguageEnglish
Published United States IEEE 01.11.2024
Subjects
Adaptive dynamic programming
Drugs
Dynamic programming
Heuristic algorithms
Immune system
Indexes
Iterative methods
lung cancer cell
Optimal control
policy iterative
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Summary:An extend-policy iterative algorithm is proposed for solving the ecological evolving-lung cancer cells growth inhibition optimal drug delivery scheme. With the analysis of the cell proliferation-apoptosis process of lung cancer cells with primitive immune system and external drug interventions, such as chemotherapeutic drugs and immunological agents, a model of ecological containment of lung cancer cells mimicking injection labeling is constructed. The HJB equation for biological tissue damage has also been established by considering the concentration of lung cancer cells in the blood and the amount of drug administered. The final simulation experiment proved the effectiveness of the drug delivery scheme.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2024.3378390