Affiliations: [a] Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia | [b] Mechanical Engineering Department, College of Engineering, King Khalid University, Abha, Saudi Arabia | [c] Department of Chemistry, Faculty of Science, King Khalid University, Abha, Saudi Arabia | [d] Department of Mechanical Engineering, CMR Technical Campus, Hyderabad, Telangana, India | [e] Department of Mathematics, Faculty of Science, University of Tabuk, Tabuk, Saudi Arabia
Correspondence:
[*]
Corresponding authors: Jeevan Kanesan, Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia. E-mail: jievan@um.edu.my. Irfan Anjum Badruddin, Mechanical Engineering Department, College of Engineering, King Khalid University, Abha, Saudi Arabia. E-mail: magami.irfan@gmail.com
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.
