# Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm
## 5 Conclusions  
The focus of this study is on the precise dose optimization of chemotherapy involving the non-cycle-specific drug cisplatin and the cycle-specific drug docetaxel. We modified the tumor growth model for cycle-specific drug combination chemotherapy to better suit this topic, taking into account acquired drug resistance and toxic side effects on bone marrow cell populations. We proposed a multi-objective Squirrel Search Algorithm named the TA-MOSSA, improved with a dual external archive mechanism and a local search mechanism, for analyzing chemotherapy regimen formulation in two different chemotherapy scenarios. The two-archive mechanism maintains the diversity and convergence of the population in the dynamic foraging mechanism of the Squirrel Search Algorithm, while the use of a local search in season monitoring mechanism effectively helps the search process approach the ideal Pareto front and achieve a superior Pareto solution set. Experimental results demonstrate that, compared to other multi-objective optimization algorithms, the TA-MOSSA generates a more diverse, evenly distributed, and broader coverage Pareto solution set, covering a wide range of trade-offs between conflicting objectives. Comparative experiments on the chemotherapy regimen selection based on different treatment strategies (aggressive and mild chemotherapy) show that the TA-MOSSA provides more comprehensive and effective candidate chemotherapy regimens for medical decision-making.  
It is important to note that our study primarily serves as decision-making support for clinicians rather than a direct clinical study. As a mathematical model, our results offer insights into the potential efficacy of different treatment regimens and can inform clinical decision-making processes. Whether the optimized treatment plan can be applied to clinical requires the ultimate validation and implementation of these findings in clinical practice, requiring in vivo and further empirical validation and consideration of additional factors beyond the scope of our model. Another limitation of our study lies in the simplification of the biological system using a linear model. While linear models offer transparency and ease of interpretation, they inherently simplify the complex dynamics of biological systems. As a result, the applicability of our findings to clinical studies may be limited by the assumptions and constraints imposed by the linear model.  
Moving forward, we recognize the need for further research efforts to validate our model against clinical data and to consider additional objectives, such as treatment duration and drug cost. These efforts will enhance the applicability and effectiveness of our chemotherapy optimization framework in real-world clinical settings. Furthermore, our commitment to collecting clinical data and conducting further testing underscores our dedication to refining and validating our model for practical application in clinical settings.  
Conceptualization, L.H., X.L. and D.H.; methodology, L.H., X.L. and D.H.; software, L.H., X.L. and D.H; validation, L.H. and X.L.; investigation, L.H. and X.L.; resources, L.H. and X.L.; data curation, L.H. and X.L.; writing--original draft preparation, X.L. and D.H.; writing--review and editing, L.H; visualization, L.H., X.L. and D.H.; supervision, L.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author (accurately indicate status).  
The authors gratefully acknowledge the support provided by the School of Computer and Electronic Information, Guangxi University, Nanning, China.  
The authors declare no conflicts of interest.