Generalizations of Particle Swarm Optimization: Applications of Particle Swarm algorithms to Statistical Physics and Bioinformatics problems

Particle Swarm Optimization is a modern and powerful optimization technique that has known rapid development since its introduction. During a relatively short period of time, the particle swarm paradigm metamorphosed from a small curiosity into an fully fledged field of research, with an exponentially growing number of papers published each year presenting new research directions and applications. The effervescence around the particle swarm paradigm and its applications provided the general motivation of this thesis --- to identify novel applications of the particle swarm meta-heuristic. Within this motivation, the thesis research focuses on various difficult problems encountered in Statistical Physics and Bioinformatics and investigates variations and hybrids of the Particle Swarm Optimization meta-heuristic in an attempt to improve the performance of the algorithm on these problems.