Search Performance Improvement for PSO in High Dimensional Space
Toshiharu Hatanaka, Takeshi Korenaga, Nobuhiko Kondo, Katsuji Uosaki · InTech eBooks · 2009
The purpose of this study is to improve the early convergence of the particle swarm optimization in high-dimensional function optimization problems by the degeneracy. We have proposed the novel particle driven model, called Rotated Particle Swarm (RPS). It employs a coordinate conversion where information of other dimensions is utilized to keep diversity of each dimension. It is very simple technique and it is able to apply to any modified PSO model. The experimental results have shown that the proposed RPS is more efficient in optimizing high-dimensional functions than a standard PSO. The proposed RPS indicated remarkable improvement in convergence for high-dimensional space, especially in unimodal functions. An appropriate selection of rotated angles and dimensions are the future study, however it is envisioned that the performance of the proposed algorithm has robustness for such parameter settings. To compare the proposed method to the other modifications and to develop more powerful algorithm by combining with local optima technique are now under investigation.