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Radiation therapy is a critical component of cancer treatment. However, thedelivery of radiation poses inherent challenges, particularly in minimizingradiation exposure to healthy organs surrounding the tumor site. Onesignificant contributing factor to this challenge is the patient's respiration,which introduces uncertainties in the precise targeting of radiation. Managingthese uncertainties during radiotherapy is essential to ensure effective tumortreatment while minimizing the adverse effects on healthy tissues. Thisresearch addresses the crucial objective of achieving a balanced dosedistribution during radiation therapy under conditions of respirationuncertainty. To tackle this issue, we begin by developing a motion uncertaintymodel employing probability density functions that characterize breathingmotion patterns. This model forms the foundation for our efforts to optimizeradiation dose delivery. Next, we employ three bio-inspired optimizationtechniques: Cuckoo search optimization (CSO), flower pollination algorithm(FPA), and bat search Optimization (BSO). Our research evaluates the dosedistribution in Gy on both the tumor and healthy organs by applying thesebio-inspired optimization methods to identify the most effective approach. Thisresearch ultimately aids in refining the strategies used in radiation therapyplanning under the challenging conditions posed by respiration uncertainty.Through the application of bio-inspired optimization techniques and acomprehensive evaluation of dose distribution, we seek to improve the precisionand safety of radiation therapy, thereby advancing cancer treatment outcomes.

Bio-Inspired Strategies for Optimizing Radiation Therapy under Uncertainties