Abstract
Particle swarm optimization (PSO) is one of the modern heuristic algorithms. PSO has attracted great attention due to its features of easy implementation, robustness to control parameters and computation efficiency compared with other existing heuristic algorithms. The performance of a PSO can depend on its parameters such as the inertia weight factors and two acceleration coefficients. The value of inertia weight can treat the balance between exploration and exploitation, so a proper control of inertia weight is very important to find the optimum solution efficiently. This paper compared several kinds of inertia weights. Experimental results demonstrate that simulated annealing inertia weight and linearly decreasing inertia weight have better convergence performance in the searching procedure. The dimension of the solution has no effect on the performance of inertia weight. The performance of the two kinds of inertia weight will change when the benchmark functions and acceleration coefficients change.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: 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.
