# Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm
## 2 Literature Review
### Chemotherapy Dose Optimization Based on Meta-Heuristic Algorithm  
Meta-heuristic algorithms have found extensive applications in various aspects of chemotherapy drug dose optimization, such as optimizing the design parameters of controllers and determining dosage and time of administration. [PERSON] et al. [25] compared the performance difference between the Whale Optimization Algorithm (WOA) and the Simulated Annealing Algorithm (SA) in tuning the design parameters of a Proportional-Integral-Derivative controller (PID) with a single optimization objective of weighted sum of the toxicity level in the patient's body and the number of ultimately proliferating tumor cells, and the experimental results showed that the WOA converged to the optimal solution faster, as well as that the WOA-PID had more precise control of the drug concentration. The control of drug concentration by WOA-PID is also more accurate. [PERSON] et al. [8] used the Invasive Weed Optimization (IWO) algorithm to tune the parameters of an intuitionistic fuzzy logic controller with the final tumor size as the single optimization objective. [PERSON] et al. [22] used the Genetic Algorithm (GA) to solve the optimal control problem of cell cycle-specific chemotherapy dosing, again using the total number of tumor cells at the end of the regimen as the optimization objective, to find the optimal drug accumulation and drug dosage. [PERSON] et al. [16] chose the Particle Swarm Optimization (PSO) algorithm to minimize the duration of the chemotherapy course and the tumor size as the optimization objectives, and the two objectives were aggregated in a weighted manner. The fractional order internal mode (IMC FOPI) controller closed-loop multidrug scheduling model proposed by [PERSON] et al. [14] consists of a random walk Moth-flame Optimization Algorithm (MFOA) to optimize the design parameters, with the optimization objective set as the total number of drug-sensitive and drug-resistant tumor cells at the end of the regimen. The above literature studies utilized single-objective meta-heuristic algorithms to address the optimization problem of chemotherapy drug dose optimization. However, in recent years, there has been a trend toward the adoption of multi-objective meta-heuristic algorithms, allowing for the incorporation of multiple optimization objectives. Meta-heuristic optimization algorithms have been widely used by researchers in different aspects of chemotherapy drug scheduling, such as optimizing the design parameters of controllers and determining the dosage and time of administration. [PERSON] et al. utilized the Integral-Proportional-Derivative (I-PD) controller in two studies addressing the optimal control problem of chemotherapy drug scheduling [14; 20]. They employed Multi-Objective Particle Swarm Optimization (MOPSO) and the Multi-Objective Genetic Algorithm (MOGA) to optimize controller design parameters, with tumor size and toxicity level acting as optimization objectives. Considering the need to control toxicity levels while ensuring efficacy, the quantity of normal tissue cells and the concentration of drugs at tumor sites were constrained within specific ranges. [PERSON] et al. [13] proposed a Proportional\(-\)Integral\(-\)Derivative controller (2 DOF PID) in a cascaded drug scheduling model. They used NSGA-II to optimize design parameters, with the optimization objectives being the toxicity level and the absolute error of drug concentration compared to their respective desired values. The number of tumor cells was not used as an optimization objective. Their scheduling model selected a solution from the non-dominated solution set, achieving an excellent trade-off between toxicity level and the number of tumor cells. They demonstrated that NSGA-II outperformed Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Genetic Algorithm (MOGA) in this optimization problem. [PERSON] et al. recognized the limitations of single-objective optimization.