Abstract:

In order to tackle the problems of slow convergence, low accuracy and parameterdependence of the standard particle swarm optimization (PSO) algorithm, we propose a mean PSO algorithm based on adaptive inertia weight. The new algorithm improves its performance by adjusting inertia weight dynamically and adaptively. It divides particles into three groups according to each fitness value and applies different inertia weight strategies for  particles of different groups, making the particles choose appropriate inertia weight values according to their own position, and converges to the global optimal position faster. Furthermore, the individual and global optima are replaced by their linear combination during the algorithm iteration phase to enhance the calculation performance of the PSO. Experimental results show that the proposed algorithm outperforms the standard PSO and some other improved PSO algorithms with less iteration for finding the optimal solution, and has faster convergence and higher convergence precision.

Key words: particle swarm optimization;mean value;adaptive inertia weight;fitness value