N-Level Hierarchy-Based Optimal Control to Develop Therapeutic Strategies for Ecological Evolutionary Dynamics Systems

• Published in: IEEE transaction on neural networks and learning systems Vol. 35; no. 3; pp. 1 - 11
• Main Authors: Liu, Jinze, Sun, Jiayue, Zhang, Huaguang, Xu, Shun, Zou, Zifang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Algorithms; Antineoplastic drugs; Benchmarking; Biological Evolution; Biological system modeling; Cancer; Chemotherapy; Chemotherapy and immunotherapy; Drugs; ecological evolutionary dynamics systems (EEDS); Evolutionary algorithms; Humans; Immune system; Immunosuppressive agents; N-level hierarchy optimization (NLHO); Neural Networks, Computer; Optimal control; Research Design; Statistics; therapeutic strategies; Tumor cells; Tumors
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2022.3201517

This article mainly proposes an evolutionary algorithm and its first application to develop therapeutic strategies for ecological evolutionary dynamics systems (EEDS), obtaining the balance between tumor cells and immune cells by rationally arranging chemotherapeutic drugs and immune drugs. First, an EEDS nonlinear kinetic model is constructed to describe the relationship between tumor cells, immune cells, dose, and drug concentration. Second, the N-level hierarchy optimization (NLHO) algorithm is designed and compared with five algorithms on 20 benchmark functions, which proves the feasibility and effectiveness of NLHO. Finally, we apply NLHO into EEDS to give a dynamic adaptive optimal control policy and develop therapeutic strategies to reduce tumor cells, while minimizing the harm of chemotherapy drugs and immune drugs to the human body. The experimental results prove the validity of the research method.