# Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm
## 2 Literature Review
### Chemotherapy Dose Optimization Based on Meta-Heuristic Algorithm
They initially applied the NSGA-II algorithm to a Two-Degree-of-Freedom Fractional-Order Proportional-Integral-Derivative controller (2 DOF FOPID) in a cascaded drug scheduling model [12], demonstrating its superior performance over the Two-Degree-of-Freedom Proportional-Integral-Derivative controller (2 DOF PID) in terms of toxicity level peak, drug concentration mean, and the quantity of normal tissue cells. Later, they employed NSGA-II for open-loop drug scheduling optimization [22], considering two optimization objectives, which were the total number of tumor cells and cumulative damage to normal tissue cells. The performance of NSGA-II in open-loop drug scheduling was superior to that of the Modified Fractional-Order Internal Model Controller (MFOIMC) and the Integral\(-\)Proportional\(-\)Derivative (I-PD) controller in closed-loop drug scheduling under two scenarios of drug resistance. [PERSON] et al. [4] conducted open-loop drug scheduling research using NSGA-II to optimize precise doses for each treatment cycle, with the total tumor cell load and damage to normal tissue being used as optimization objectives. The non-dominated solution set with good diversity and objective trade-offs provided sufficient options for oncologists' personalized decision-making. In the studies using single-objective meta-heuristic algorithms mentioned above, the emphasis is often placed on minimizing tumor size as the sole or primary optimization objective while overlooking the side effects of chemotherapy drugs on patients' overall health. Even when considering additional factors, such as toxicity levels and treatment duration, in order to fit the optimization process into a single-objective framework, weighted summation or range constraints are often employed, assuming trade-offs between objectives and leading the single-objective optimization process to converge to a single optimal point. However, different treatment strategies in chemotherapy have different priorities for treatment goals. For instance, curative chemotherapy aims to maximize drug efficacy to eliminate as many cancer cells as possible, while palliative chemotherapy aims to improve the patient's quality of life. If a single-objective optimization process produces a unique optimal solution, multiple optimization processes are needed to obtain multiple dosing regimens. Multi-objective optimization not only balances multiple conflicting optimization objectives but also generates a set of superior non-dominated solutions that effectively address conflicting objectives. Oncologists can then make the final treatment decisions for patients based on tumor policies. Moreover, most existing studies focus on established multi-objective optimization algorithms such as Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Genetic Algorithm (MOGA). Although these algorithms are classical methods for solving multi-objective optimization problems, they were not specifically designed for the optimization problem of combination chemotherapy. The existing literature has not adjusted these algorithms based on the characteristics of the problem, raising questions about whether there is still room for improvement in their performance.