# Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm
## 2 Literature Review  
### Chemotherapy Dose Optimization Based on Mathematical Models  
The use of mathematical models to systematically test a large number of different drug and dosage combinations narrows down the range of regimens to be tested in the clinical setting and provides a cost-effective and ethical way to develop and optimize dosing regimens [7]. Many researchers have studied the growth of tumors and the development of drug resistance, and they have established corresponding mathematical models which are capable of generating process simulations and outcome predictions similar to those in clinical practice, introducing factors such as drug action, toxicity characterization, and other factors to the growth model, constituting a pharmacodynamic model of the action of chemotherapeutic drugs on tumors. Based on the mathematical model, researchers have proposed many studies related to chemotherapy drug optimization. [PERSON] et al. [8] proposed an intuitive fuzzy logic controller for controlling chemotherapeutic drug infusion based on the Gompertz tumor growth model proposed by [PERSON], which yielded a chemotherapy regimen that shrunk a tumor to a state of near complete elimination in 84 days of treatment. [PERSON] et al. [9], on the other hand, used the Gompertz tumor growth model to study the combination of anthracyclines and the anti-cardiotoxic adjuvant drug dexrazoxane, and generated the dosage of the drug by Gaussian pseudo-spectral method, which improved the reduction in the tumor cell count by 66.54% as compared to the conventional drug scheduling model. [PERSON] et al. [4] used the cycle-specific tumor growth compartmentalization model proposed by [PERSON] et al. [10] to determine cycle-specific chemotherapy dosing under the influence of drug resistance and chose compatible regimens for the three special cases of young patients, old patients, and pregnant patients from the set of non-dominated solutionsobtained. [PERSON] et al. [11] similarly used the [PERSON] et al. [10] compartmentalized model to explore the efficacy of sequential and simultaneous dosing strategies in the treatment of rectal cancer, using a combination of 5-Fluorouracil (5-FU) and CPT-11, optimizing the dosage of the drugs by using Genetic Algorithm (GA). The simultaneous dosing strategy in the experiments achieved maximum tumor reduction and demonstrated stable performance in simulation experiments with different tumor sizes. It is worth noting that the above studies using the Gompertz growth model did not take into account drug resistance and chemotherapeutic drug cycle specificity in the development of dosing schedules. [PERSON] et al. [12] used a two-degree-of-freedom fractional-order PID controller for closed-loop scheduling of chemotherapeutic drugs using an exponential tumor growth model, which produced a set of chemotherapeutic dosing regimens with varying trade-offs between tumor reduction and toxicity levels. [PERSON] et al. [13] also conducted a study based on the exponential growth model and proposed a chemotherapy drug scheduling plan using a two-degree-of-freedom PID controller cascade model of chemotherapy drug scheduling; the obtained chemotherapy regimen had lower drug toxicity and tumor cell residues than the conventional PID controller, and it also used fewer drug doses. However, studies by [PERSON] et al. and [PERSON] et al. have focused on single-agent chemotherapy, which often requires larger doses of drugs to achieve the same efficacy as compared to combination chemotherapy, with a consequent elevation in toxicity and resistance; this is therefore often preferred for the treatment of many types of cancers. [PERSON] et al. [14] proposed a chemotherapeutic drug scheduling model based on a random walk Moth Flame Optimization Algorithm (MFOA) regulated fractional-order internal mode controller for drug resistance considerations, using a three-drug combination chemotherapy model proposed by [PERSON] et al. [15], which determines the ideal concentration for each dosing interval.