This paper presents a particle swarm optimizer for solving constrained optimization problems which adopts a very small population size (five particles). The proposed approach uses...
Juan Carlos Fuentes Cabrera, Carlos A. Coello Coel...
This paper introduces a method for clustering complex and linearly non-separable datasets, without any prior knowledge of the number of naturally occurring clusters. The proposed ...
Self-reconfigurable adaptive systems have the possibility of adapting their own hardware configuration. This feature provides enhanced performance and flexibility, reflected i...
This paper presents an updated version of the adaptive learning particle swarm optimizer (ALPSO) [6], we call it ALPSO-II. In order to improve the performance of ALPSO on multi-mod...
—This paper presents an automatic object detection and tracking algorithm by using particle swarm optimization (PSO) based method, which is a searching algorithm inspired by the ...