Modified particle swarm optimization with time varying velocity vector

Abstract

Particle Swarm Optimization (PSO) is a population-based computational intelligence paradigm; it originated as a simulation of simpli ed social model of birds in a ock. The PSO algorithm is easy to implement and has been proven to be very competitive for solving diverse global optimization problems including both test and ap- plication problems in comparison to conventional methods and other meta-heuristics. In the present study, a new velocity vector is introduced in the BPSO algorithms and is analyzed on thirty six benchmark problems and three real life problems taken from the literature. The numerical results show that the incorporation of the proposed velocity vector helps in improving the performance of BPSO in terms of nal objective function value, number of function evaluations and convergence rate.