# Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm
###### Abstract  
Chemotherapy is one of the most effective treatments for cancer, but the efficacy of standard chemotherapy regimens is often limited by toxicities and the individual heterogeneity of cancers. Precise dosing is an important tool to improve efficacy and reduce significant differences in toxicity. However, most of the existing studies on chemotherapy optimization fail to fully consider the toxic side effects, drug resistance, and drug combinations, and thus the chemotherapy regimens obtained may face difficulty in achieving the expected efficacy and also affect the subsequent treatment. Therefore, this paper establishes a tumor growth model for the combination chemotherapy of cell cycle-specific and non-cycle-specific drugs and includes the factors of acquired drug resistance and toxic side effects, proposing an improved multi-objective Squirrel Search Algorithm, the TA-MOSSA, to solve the problem of accurate chemotherapy drug optimization. In this paper, experiments were conducted to analyze the efficacy of chemotherapy dosing regimens obtained by the TA-MOSSA based on the tumor growth model, and the results show that the TA-MOSSA can provide effective chemotherapy regimens for patients who take different treatment approaches.  
multi-objective optimization; squirrel search algorithm; chemotherapy optimization; tumor dynamics model; cell cycle-specific +
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and recurrence, the reformulation of the treatment regimen will also increase the economic burden of patients. How to achieve accurate drug administration according to the mechanism of action of different drugs and patient's specific conditions is an urgent problem in chemotherapy, and it requires careful consideration of tumor shrinkage, toxic side effects, and drug resistance.  
In this paper, we conducted a study on precise chemotherapy dosing optimization using the Squirrel Search Algorithm (SSA) [6], a meta-heuristic optimization approach known for its high performance. The SSA, proposed by [PERSON] et al., emulates the dynamic foraging behavior of flying squirrels in forests. It exhibits remarkable convergence and diversity, outperforming traditional optimization algorithms such as the Genetic Algorithm, Particle Swarm Optimization, Bat Algorithm, Firefly Algorithm, and Artificial Bee Colony Algorithm.  
The main contributions of this paper are outlined as follows:  
1. We established a tumor growth model incorporating drug resistance and toxicity factors for cell cycle-specific and non-cycle-specific combination chemotherapy drug administration.
2. The Squirrel Search Algorithm (SSA) was extended to the realm of multi-objective optimization. Introducing novel enhancements, including the two external archive mechanism and two-point crossover operator, we proposed an improved version named the two-archive multi-objective Squirrel Search Algorithm (TA-MOSSA).
3. Simulation experiments with the TA-MOSSA were conducted on the mathematical model of tumor dynamics. Experiment results confirmed the TA-MOSSA's ability to generate chemotherapy regimens with significant efficacy across various chemotherapy scenarios. It provided a diverse range of medication choices and made several options available for medical decision-making.  
The rest of this work is structured as follows: Section 2 reviews the related literature, Section 3 introduces the proposed tumor model and method, Section 4 describes the experimental setups, results and corresponding analysis, and Section 5 is the conclusion of this paper.