Abstract
Particle swarm optimization algorithm is easy to reach premature convergence in the solution process, and fall into the local optimal solution. Aiming at the problem, this paper proposes a particle swarm optimization algorithm with chaotic mapping (CM-PSO). The algorithms uses chaotic mapping function to optimize the initial state of population, im- prove the probability of obtain optimal solution. Then, CM-PSO algorithm introduces nonlinear decreasing strategy on the inertia weight to avoid local optimal solution. In the experimental stage, four different functions are used to validate the performance of the algorithm. The experimental results show that, compared with the standard particle swarm algorithm, CM-PSO algorithm has strong global searching ability, can effectively avoid the premature convergence problem, and en- hance the ability of the algorithm to escape from local optima. Although the algorithm consumes time is slightly in- creased, it is worth for getting the global optimal solution with such cost.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.