Particle Swarm Optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO often easily falls into local optima because the particles would quickly get closer to the best particle. Under these circumstances, the best particle could hardly be improved. This paper proposes a new hybrid PSO (HPSO) to solve this problem by combining space transformation search (STS) with a new modified velocity model. Experimental studies on 8 benchmark functions demonstrate that the HPSO holds good performance in solving both unimodal and multimodal functions optimization problems. © 2010 Springer-Verlag.
CITATION STYLE
Yu, S., Wu, Z., Wang, H., & Chen, Z. (2010). A hybrid particle swarm optimization algorithm based on space transformation search and a modified velocity model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5938 LNCS, pp. 522–527). https://doi.org/10.1007/978-3-642-11842-5_73
Mendeley helps you to discover research relevant for your work.