Comparison study of several kinds of inertia weights for PSO

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.