Enhanced Global Best Particle Swarm Classification

2. Pso-Based Classification Background

The roots of PSO lay in ethological metaphors of computing models (Poli, Kenndy & Blackwell, 2001; Kennedy, Eberhar, 2001; Abraham, Guo, Liu, 2006). Formally, a PSO algorithm is based on a swarm of M individuals or particles, each evolving in N-dimensional space with its coordinates representing a potential solution to a problem with N attributes. Its genotype consists of 2N parameters, the first half representing the coordinates of the particle in the search space and the second half the corresponding velocity components. A particle moves with adaptable velocity within the search space, and it constantly updates and retains the best position it ever reaches in memory. The best position reached by the swarm, and sometimes also in a neighborhood of particles, are also constantly updated and memorized.