A Hybrid Particle Swarm Optimization Based on Symmetric Distribution and Simulated Annealing

DOI

10.2991/mmebc-16.2016.395How to use a DOI?

Keywords

particle swarm optimization; multimodal function; radial symmetric; simulated annealing

Abstract

The performance of particle swarm optimization (PSO) is limited by its local minima, defects and poor precision. To solve this problem, we present a hybrid adaptive particle swarm optimizationª. Two approaches, symmetric distribution and simulated annealing algorithm, have been used to improve the PSO algorithm. Firstly, the position of particle is updated by means of a radial symmetric function in the center. Secondly, the simulated annealing algorithm is employed to describe the mechanism. Finally, the improved PSO algorithm in our research is verified to be feasible and effective by comparing with the current well-known methods.

Copyright

© 2016, 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  - Xueyan Li
PY  - 2016/06
DA  - 2016/06
TI  - A Hybrid Particle Swarm Optimization Based on Symmetric Distribution and Simulated Annealing
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 1965
EP  - 1969
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.395
DO  - 10.2991/mmebc-16.2016.395
ID  - Li2016/06
ER  -