# Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm
## 2 Literature Review
### Chemotherapy Dose Optimization Based on Mathematical Models
However, of the three chemotherapeutic agents used in this model, Docetaxel and Irinotecan are cycle-specific chemotherapeutic agents, whereas the model characterizes all three drugs as non-cycle-specific; therefore, the dosing regimen obtained in this study is questionable in terms of accuracy and interpretability. In order to determine the chemotherapy dosage while scheduling the self-recovery period for the patients, [PERSON] et al. [16] chose a mathematical model that can simulate the tumor cells and the immune system cells, and they developed a chemotherapy schedule for the patients by taking into account the duration of the treatment, the resting time, and the dosage of the drugs. Additionally, the amount of the chemotherapy dosage was calculated by the linear optimal controller, while the time scheduling was conducted by the particle swarm optimization algorithm (PSO), in which the tumor growth model is in a Logistic form. [PERSON] et al. [17] introduced combination chemotherapy and drug resistance mechanisms to the tumor growth model considering cell cycle-specific drugs proposed by [PERSON] et al. [18], proposing a memetic algorithm (MA) based on a feasible local search strategy for determining the level of chemotherapeutic drugs used and the duration of dosing, thereby determining the presence of a resistance premise that, even with small dose variations, will have a large impact on the efficacy. Two studies by [PERSON] et al. [19,20] were based on Integral\(-\)Proportional\(-\)Derivative (I-PD) controllers to deal with the optimal control problem of scheduling cell cycle-specific chemotherapeutic agents, using a linear growth model for tumors and a logistic growth model for normal tissue (bone marrow) cells proposed by [PERSON] et al. [21]. In the experiment, the dosing regimen with maximum tumor reduction reduced tumor cells in proliferative and quiescent phases by 72.2% and 60.4%, respectively, but this mathematical model only investigates the effect of a single drug and fails to take into account the effect of drug resistance. [PERSON] et al. [22] extended the two linear tumor growth models proposed by [PERSON] et al. [21,23], which consider drug resistance and cell cycle-specific drugs. They proposed a dynamic model of tumor growth capable of simultaneously simulating acquired drug resistance, cell cycle specificity, and multi-agent combination chemotherapy. The study evaluated the efficacy of two chemotherapeutic drugs, Docetaxel and Irinotecan, using the NSGA-II algorithm to identify optimal doses and dosing schedules for their use in combination. In the above literature, there is no lack of research on solving the chemotherapy drug optimization problem as a closed-loop control problem, aiming to maintain the optimal drug infusion rate through continuous adjustment, and the implementation of this strategy requires a fixed reference drug input value and continuous monitoring of various clinical output parameters [24], which is highly demanding in terms of equipment accuracy and personnel operational ability. The cost of deploying monitoring equipment and the training of specialized personnel will be a great challenge for areas with scarce medical resources. Therefore, open-loop controlled drug scheduling, in which the chemotherapy dose is determined in advance based on patient characteristics, disease progression, and therapeutic goals, will still have a place in chemotherapy. In addition, existing studies in the literature have mostly seen single-agent chemotherapy dose optimization or combination chemotherapy with two cell cycle-specific agents. Therefore, in this paper, we worked on the precise dosing of combination chemotherapy with non-cell cycle-specific and cell cycle-specific drugs to fill the research gap, and we took into account factors such as drug resistance and cell cycle specificity of the drugs.