A Filtering Mechanism Based Optimization for Particle Swarm Optimization Algorithm

2016 
Particle swarm optimization algorithm is one of the most common algorithms for optimization. Because of the code is simple convenient operation, and good robustness advantages of attention. Particle swarm optimization algorithm is popularly, however, there is operation precision which is not high, with the poor population diversity .Many scholars have made this algorithm improved. This paper proposes a new particle swarm algorithm improvement program, introduced the particle swarm optimization algorithm to update them. With the introduction of filtering mechanism, it reduced the number of particles, and made a disturbance on the global extreme, to a more specific tracking for specific issues. Improved cut particle swarm optimization algorithm based on filtering mechanism (ELPSO) makes its operation speed and more accurate.
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