@Article{IJNAM-9-371,
author = {S. Yu, Z. Wu, H. Wang, Z. Chen and H. Zhong},
title = {A Hybrid Particle Swarm Optimization Algorithm Based on Space Transformation Search and a Modified Velocity Model},
journal = {International Journal of Numerical Analysis and Modeling},
year = {2012},
volume = {9},
number = {2},
pages = {371--377},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {2617-8710},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/ijnam/634.html}
}