The novel non-linear strategy of inertia weight in particle swarm optimization

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.

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