Bio-Inspired Optimization Techniques and their Application to Biomedical Engineering

Optimization algorithms have a key role in solving number of Biomedical Engineering problems. Biomedical problems are complex in nature having multiple input parameters and objectives; this is still a difficult and challenging area need to be suitably addressed. Hence, in recent years a number of bio-inspired optimization algorithms have been developed and applied to solve complex biomedical problems. The objective of this paper is to critically evaluate and compare the two most popular bio-inspired optimization algorithms: (i) Genetic algorithm (GA) and (ii) Particle swarm optimization algorithm (PSO), and to demonstrate the application of optimization algorithms in solving biomedical problems for same. We have selected the following key application areas: (i) Optimal segmentation of medical images (CT and MR images), (ii) Treatment planning in radiotherapy, and (iii) Mathematical modelling of physiological systems. We conclude with a discussion on the future of optimization in biomedical research.