The MOEO algorithm for multi-objective optimization of the cancer immuno-chemotherapy

• Published in: Computers in biology and medicine Vol. 182; p. 109094
• Main Authors: Nozad, K., Varedi-Koulaei, S.M., Nazari, M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-11-2024; Elsevier Limited
• Subjects: Algorithms; Cancer; Cancer therapies; Chemotherapy; Clinical trials; Control theory; Cytokines; Drug dosages; Equilibrium Optimizer (EO); Genetic algorithms; Heuristic methods; Immune system; Immunotherapy; Lymphocytes; Mathematical models; Mixed chemo-immunotherapy; Multi-objective Equilibrium Optimization (MOEO); Multiple objective analysis; Optimal control; Optimization algorithm; Optimization algorithms; Ordinary differential equations; Pareto optimization; Patients; Optimization algorithm; Multi-objective Equilibrium Optimization (MOEO); Mixed chemo-immunotherapy; Optimal control; Equilibrium Optimizer (EO); Cancer
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.109094

In cancer treatment, chemotherapy has the disadvantage of killing both healthy and cancerous cells. Hence, the mixed-treatment of cancer such as chemo-immunotherapy is recommended. However, deriving the optimal dosage of each drug is a challenging issue. Although metaheuristic algorithms have received more attention in solving engineering problems due to their simplicity and flexibility, they have not consistently produced the best results for every problem. Thus, the need to introduce novel algorithms or extend the previous ones is felt for important optimization problems. Hence, in this paper, the multi-objective Equilibrium Optimizer algorithm, as an extension of the single-objective Equilibrium Optimizer algorithm, is recommended for cancer treatment problems. Then, the performance of the proposed algorithm is considered in both chemotherapy and mixed chemo-immunotherapy of cancer, considering the constraints of the tumor-immune dynamic system and the health level of the patients. For this purpose, two different patients with real clinical data are considered. The Pareto front obtained from the multi-objective optimization algorithm shows the points that can be selected for treatment under different criteria. Using the proposed multi-objective algorithm has reduced the total chemo-drug dose to 138.92 and 5.84 in the first patient, and 16.9 and 0.4384 in the second patient, for chemotherapy and chemo-immunotherapy, respectively. Comparing the results with previous studies demonstrates MOEO's superior performance in both chemotherapy and chemo-immunotherapy. However, it is shown that the proposed algorithm is more suitable for mixed-treatment approaches.
•The single-objective EO algorithm has extended to the multi-objective algorithm.•The performance of the proposed algorithm is considered in both chemotherapy and mixed chemo-immunotherapy of the cancer treatment.•For this purpose, two different patients with real clinical data are considered.•Comparing the obtained results with previous works shows the superior performance of the MOEO.