A Self-Adaptive Chaos Particle Swarm Optimization Algorithm
Abstract: As a new
evolutionary algorithm, particle swarm optimization (PSO) achieves integrated
evolution through the information between the individuals. All the particles
have the ability to adjust their own speed and remember the optimal positions
they have experienced. This algorithm has solved many practicalengineering
problems and achieved better optimization effect. However, PSO can easily get
trapped in local extremum, making it fail to get the global optimal solution
and reducing its convergence speed. To settle these deficiencies, this paper
has proposed an adaptive chaos particle swarm optimization (ACPSO) based on the
idea of chaos optimization after analyzing the basic principles of PSO. This
algorithm canimprove the population diversity and the ergodicity of particle
search through the property of chaos; adjust the inertia weight according to
the premature convergence of the population and the individual fitness; consider
the global optimization and local optimization; effectively avoid premature
convergence and improve algorithm efficiency. The experimental simulation has
verified its effectiveness and superiority.
Author: Yalin Wu, Shuiping
Zhang
Journal Code: jptkomputergg150060
