An Advanced Particle Swarm Optimization Method based on T-Distribution Random Process
Tianjia Zhang, Yongsheng Yang
Available Online April 2019.
- https://doi.org/10.2991/icmeit-19.2019.64How to use a DOI?
- optimization; PSO; t-distribution; reference set; benchmark functions.
- There are many real-life engineering problems that entail appropriate optimization methods. Although almost all the problems can be modeled into simple forms described by mathematical formula, it is hard to solve all the decisive problems by a single optimization method. Researchers have developed many effective optimization techniques to solve assorted problems. Among these particle swarm optimization (PSO) has played an important role in optimization of complex and high-dimensional problems. However, PSO suffers from premature convergence and low precision. For this purpose, the paper proposed a TPSO which adapts a stochastic process based on t-distribution and a mechanism of reference set. Subsequently simulations tested on 13 classical benchmark functions demonstrated that the TPSO can achieve faster convergence speed and higher accuracy. Finally, the application on the path planning problem of UAV evaluated the efficiency of the proposed algorithm.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Tianjia Zhang AU - Yongsheng Yang PY - 2019/04 DA - 2019/04 TI - An Advanced Particle Swarm Optimization Method based on T-Distribution Random Process PB - Atlantis Press SP - 388 EP - 402 SN - 2352-538X UR - https://doi.org/10.2991/icmeit-19.2019.64 DO - https://doi.org/10.2991/icmeit-19.2019.64 ID - Zhang2019/04 ER -