An Advanced Particle Swarm Optimization Method based on T-Distribution Random Process

DOI

10.2991/icmeit-19.2019.64How to use a DOI?

Keywords

optimization; PSO; t-distribution; reference set; benchmark functions.

Abstract

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.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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
BT  - Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SP  - 388
EP  - 402
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.64
DO  - 10.2991/icmeit-19.2019.64
ID  - Zhang2019/04
ER  -