  Radiation Therapy (RT) plays a pivotal role in the treatment of cancer,
offering the potential to effectively target and eliminate tumour cells while
minimizing harm to surrounding healthy tissues. However, the success of RT
heavily depends on meticulous treatment planning that ensures the optimal
balance between delivering a sufficiently high dose to the tumour and sparing
nearby critical organs. This critical process demands a multidisciplinary
approach that combines medical expertise, advanced imaging techniques, and
computational tools. Optimization techniques have emerged as indispensable
tools in refining RT planning, enabling the precise adjustment of radiation
beam arrangements and intensities to achieve treatment objectives while
adhering to strict dose constraints. This study delves into the realm of
constrained optimization within RT Treatment Planning, employing metaheuristic
algorithms to enhance the efficacy of this process. The research focuses on the
design and performance comparison of three prominent optimization techniques:
Bat Search Optimization (BSO), Bacterial Foraging Algorithm (BFA), and
Artificial Bee Colony (ABC). Through systematic evaluation, it is observed that
BFA exhibits superior execution time and convergence capabilities in comparison
to the other algorithms. This research underscores the crucial importance of RT
planning and highlights the imperative need for optimization methodologies to
achieve optimal treatment outcomes.
