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