An Improved Particle Swarm Optimization and Application

Particle swarm optimization (PSO) is prone to fall into the premature convergence, and convergence precision is low and other shortcomings in solving complex problems. This paper presents an algorithm based on the adaptive inertia weight of the second-order oscillation particle swarm optimization (SOPSO); the algorithm combines SOPSO and adaptive inertia weight advantages, and it is good solution to the above-mentioned problem. Finally, the simulation on four test functions and the application on the key frame extraction from human motion capture data show that the algorithm not only has a strong search capability, and the convergence precision and stability have been effectively improved.

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