| Summary: | 碩士 === 國立中央大學 === 電機工程學系 === 104 === This thesis proposes a new particle swarm optimization (PSO) called Adaptive Self-Learning Particle Swarm Optimization (ASLPSO), and applies it to the classification problem. A self-learning method is introduced in the ASLPSO that every particle randomly selects its learning object among the better particles to acquire useful information. We also designs a dynamic transition strategy to improve the searching approach of particles during the iterations. In the experiments, the performance of the proposed ASLPSO is compared to several improved PSO’s in the literature by testing sixteen benchmark functions. The experimental results show that the proposed algorithm performs better on most of the functions. At last, the ASLPSO is applied to a classification problem. In our experiments, many classification results are better, but not all. To be more precisely, the ASLPSO is supposed to be refined in some ways.
|
|---|
