Adaptive Particle Swarm Optimization Guided by Acceleration Information

In order to improve the global convergent ability of the standard particle swarm optimization (SPSO), the paper develops a new version of particle swarm optimization guided by the acceleration information (AGPSO). Firstly, the paper introduces the concept of acceleration into the AGPSO version and makes a convergent analysis of the new model. Secondly, the paper studies the parameter choices of the AGPSO model. Thirdly, the paper provides the A GPSO with an oscillating factor to adjust the influence of the acceleration on the velocity, which can guarantee the AGPSO to converge to the global optimization validly. Finally, the proposed AGPSO versions are used to some benchmark optimizations, the experimental results show those AGPSO versions can overcome the premature problem validly, and outperforms the standard PSO in the global search ability with a quicker convergent speed

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