An improved Stud Genetic Algorithm using the Opposition-based Strategy

DOI

10.2991/ameii-16.2016.6How to use a DOI?

Keywords

Opposition-based Strategy, Stud Genetic Algorithm, Optimization Algorithm

Abstract

This paper proposed an improved Stud Genetic Algorithm using the Opposition-based strategy (SGAO) to improve the performance of the traditional SGA and accelerate its convergence speed. In SGAO, we use opposition-based approach to initialize the population and to perform mutation with the aim to improve the quality of solutions. In experiments, we use some benchmark functions to the show the performance of the proposed approach and compare it with other algorithms such as genetic algorithm, different evolutionary, particle swarm optimization and stud genetic algorithm. Results show that SGAO has faster convergence speed and higher solution precision.

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/).

Download article (PDF)

TY  - CONF
AU  - Hongwei Xu
PY  - 2016/04
DA  - 2016/04
TI  - An improved Stud Genetic Algorithm using the Opposition-based Strategy
BT  - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
PB  - Atlantis Press
SP  - 32
EP  - 37
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-16.2016.6
DO  - 10.2991/ameii-16.2016.6
ID  - Xu2016/04
ER  -