Affiliations: Complex System and Computational Intelligence
Laboratory, Taiyuan University of Science and Technology Shanxi, 030024, P.R.
China. E-mail: cuizhihua@gmail.com, cai_xing_juan@sohu.com
Abstract: Integral-controlled particle swarm optimization (ICPSO) is an
effective variant of particle swarm optimization (PSO) aiming to increase the
population diversity. Due to the additional accelerator items, the behavior of
ICPSO is more complex, and provides more chances to escaping from a local
optimum than the standard version of PSO. However, many experimental results
show the performance of ICPSO is not always well because of the particles'
un-controlled movements. Therefore, a new variant, integral particle swarm
optimization with dispersed accelerator information (IPSO-DAI) is designed to
improve the computational efficiency. In IPSO-DAI, a predefined predicted
velocity index is introduced to guide the moving direction. If the average
velocity of one particle is superior to the index value, it will choice a
convergent manner, otherwise, a divergent manner is employed. Furthermore, the
choice of convergent manner or divergent manner for each particle is associated
with its performance to fit different living experiences. Simulation results
show the proposed variant is more effective than other three variants of
particle swarm optimization especially for multi-modal numerical problems. The
IPSO-DAI algorithm is also applied to directing the orbits of discrete chaotic
dynamical systems by adding small bounded perturbations, and achieves the best
performance among four different variants of PSO.
Keywords: Particle swarm optimization, integral controller, social learning factor, cognitive learning factor, dispersed behavior
