Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm

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.