A modified particle swarm optimizer with dynamic adaptation

This paper proposes a modified particle swarm optimization algorithm with dynamic adaptation. In this algorithm, a modified velocity updating formula of the particle is used, where the randomness in the course of updating particle velocity is relatively decreased and the inertia weight of each particle is different. Moreover, this algorithm introduces two parameter describing the evolving state of the algorithm, the evolution speed factor and aggregation degree factor. By analyzing the influence of two parameters on the PSO search ability, a new strategy is presented that the inertia weight dynamically changes based on the run and evolution state. In the strategy the inertia weight is given by a function of evolution speed factor and aggregation degree factor, and the value of inertia weight is dynamically adjusted according to the evolution speed and aggregation degree. The feature of the proposed algorithm is analyzed and several testing functions are performed in simulation study. Experimental results show that, the proposed algorithm remarkably improves the ability of PSO to jump out of the local optima and significantly enhance the convergence precision.