Dual therapy of cancer using optimal control supported by swarm intelligence
Document Type
Article
Publication Date
1-1-2024
Abstract
BACKGROUND: The scientific revolution in the treatment of many illnesses has been significantly aided by stem cells. This paper presents an optimal control on a mathematical model of chemotherapy and stem cell therapy for cancer treatment. OBJECTIVE: To develop effective hybrid techniques that combine the optimal control theory (OCT) with the evolutionary algorithm and multi-objective swarm algorithm. The developed technique is aimed to reduce the number of cancerous cells while utilizing the minimum necessary chemotherapy medications and minimizing toxicity to protect patients' health. METHODS: Two hybrid techniques are proposed in this paper. Both techniques combined OCT with the evolutionary algorithm and multi-objective swarm algorithm which included MOEA/D, MOPSO, SPEA II and PESA II. This study evaluates the performance of two hybrid techniques in terms of reducing cancer cells and drug concentrations, as well as computational time consumption. RESULTS: In both techniques, MOEA/ D emerges as the most effective algorithm due to its superior capability in minimizing tumour size and cancer drug concentration. CONCLUSION: This study highlights the importance of integrating OCT and evolutionary algorithms as a robust approach for optimizing cancer chemotherapy treatment.
Keywords
Hybrid optimal control, particle swarm optimization, evolutionary algorithms, constrained optimization, multiobjective optimization
Divisions
sch_ecs
Funders
UM International Collaboration (ST023-2022),King Khalid University King Saud University (RGP.2/201/44)
Publication Title
Bio-Medical Materials and Engineering
Volume
35
Issue
3
Publisher
IOS Press
Publisher Location
NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS