Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)

An improved Stud Genetic Algorithm using the Opposition-based Strategy

Authors
Hongwei Xu
Corresponding Author
Hongwei Xu
Available Online April 2016.
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/).

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Volume Title
Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
Series
Advances in Engineering Research
Publication Date
April 2016
ISBN
978-94-6252-188-9
ISSN
2352-5401
DOI
10.2991/ameii-16.2016.6How to use a DOI?
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/).

Cite this article

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  -