A modified binary-PSO for continuous optimization

Metaheuristics based on swarm intelligence simulate the behavior of a biological social system like as a flock of birds or a swarm of bees, and they have achieved important advances for solving optimization problems. In this paper, we propose a variant for a particular kind of those metaheurisitcs: Particle Swarm Optimization (PSO). This modification arises after discovering a low rate of convergence produced by a high level of dispersal at the swarm. Finally, we analyzed and compared the results obtained by an original PSO algorithm and our proposal. From those, we can see the improvement obtained by our variant since it allows to explore more the search space. Presentado en el X Workshop Agentes y Sistemas Inteligentes Red de Universidades con Carreras en Informática (RedUNCI)