Multidimensional particle swarm optimization and applications in data clustering and image retrieval

Particle swarm optimization (PSO) was introduced by Kennedy and Eberhart in 1995 [1] as a population based stochastic search and optimization process. The natural behavior of a bird flock when searching for food is simulated through the movements of the individuals (particles or living organisms) in the flock. The goal is to converge to the global optimum of some multi-dimensional function. PSO is conceptually related to other evolutionary algorithms such as Genetic Algorithms, Genetic Programming, Evolution Strategies, and Evolutionary Programming.