The novel non-linear strategy of inertia weight in particle swarm optimization
Li Li, Bing Xue, Ben Niu, Yujuan Chai, Jianhuang Wu · 2009
Inertia weight is one of the most important adjustable parameter of particle swarm optimization (PSO). The proper selection of inertia weight can prove a right balance between global search and local search. In this paper, two novel PSOs with non-linear inertia weight based on the tangent function and the arc tangent function are provided, respectively. The performance of the proposed PSO model is compared with standard PSO with linearly-decrease inertia weight. The experimental results demonstrated that our proposed PSO model is better than standard PSO in terms of convergence rate and solution precision.