Particle Swarm Optimization in Biomedical Technologies
S. Kannadhasan, R. Nagarajan
Abstract
This chapter explores particle swarm optimization (PSO) in the rapidly evolving landscape of biomedical technologies. The study begins by introducing the fundamental principles of PSO, emphasizing its advantages in addressing complex optimization problems common in biomedical applications. The authors delve into innovative uses of PSO in various biomedical fields, including image enhancement, data clustering, and drug development, highlighting how PSO contributes to more accurate diagnoses, efficient treatment plans, and streamlined research methodologies. Significantly, this chapter identifies emerging opportunities where PSO can be further leveraged, particularly in personalized medicine and predictive health analytics, suggesting a roadmap for future research and development. By combining theoretical insights with practical examples, this work aims to provide a comprehensive overview of PSO's role in advancing biomedical technologies, offering valuable perspectives for researchers, practitioners, and policymakers in the field.