IEICE TRANSACTIONS on Information
A Simple but Efficient Ranking-Based Differential Evolution
Summary :
Differential Evolution (DE) algorithm is simple and effective. Since DE has been proposed, it has been widely used to solve various complex optimization problems. To further exploit the advantages of DE, we propose a new variant of DE, termed as ranking-based differential evolution (RDE), by performing ranking on the population. Progressively better individuals in the population are used for mutation operation, thus improving the algorithm's exploitation and exploration capability. Experimental results on a number of benchmark optimization functions show that RDE significantly outperforms the original DE and performs competitively in comparison with other two state-of-the-art DE variants.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.1 pp.189-192
- Publication Date
- 2022/01/01
- Publicized
- 2021/10/05
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021EDL8053
- Type of Manuscript
- LETTER
- Category
- Biocybernetics, Neurocomputing
Authors
Jiayi LI
University of Toyama
Lin YANG
University of Toyama
Junyan YI
Beijing University of Civil Engineering and Architecture
Haichuan YANG
University of Toyama
Yuki TODO
Kanazawa University
Shangce GAO
University of Toyama
Keyword
Latest Issue
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Jiayi LI, Lin YANG, Junyan YI, Haichuan YANG, Yuki TODO, Shangce GAO, "A Simple but Efficient Ranking-Based Differential Evolution" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 1, pp. 189-192, January 2022, doi: 10.1587/transinf.2021EDL8053.
Abstract: Differential Evolution (DE) algorithm is simple and effective. Since DE has been proposed, it has been widely used to solve various complex optimization problems. To further exploit the advantages of DE, we propose a new variant of DE, termed as ranking-based differential evolution (RDE), by performing ranking on the population. Progressively better individuals in the population are used for mutation operation, thus improving the algorithm's exploitation and exploration capability. Experimental results on a number of benchmark optimization functions show that RDE significantly outperforms the original DE and performs competitively in comparison with other two state-of-the-art DE variants.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDL8053/_p
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@ARTICLE{e105-d_1_189,
author={Jiayi LI, Lin YANG, Junyan YI, Haichuan YANG, Yuki TODO, Shangce GAO, },
journal={IEICE TRANSACTIONS on Information},
title={A Simple but Efficient Ranking-Based Differential Evolution},
year={2022},
volume={E105-D},
number={1},
pages={189-192},
abstract={Differential Evolution (DE) algorithm is simple and effective. Since DE has been proposed, it has been widely used to solve various complex optimization problems. To further exploit the advantages of DE, we propose a new variant of DE, termed as ranking-based differential evolution (RDE), by performing ranking on the population. Progressively better individuals in the population are used for mutation operation, thus improving the algorithm's exploitation and exploration capability. Experimental results on a number of benchmark optimization functions show that RDE significantly outperforms the original DE and performs competitively in comparison with other two state-of-the-art DE variants.},
keywords={},
doi={10.1587/transinf.2021EDL8053},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - A Simple but Efficient Ranking-Based Differential Evolution
T2 - IEICE TRANSACTIONS on Information
SP - 189
EP - 192
AU - Jiayi LI
AU - Lin YANG
AU - Junyan YI
AU - Haichuan YANG
AU - Yuki TODO
AU - Shangce GAO
PY - 2022
DO - 10.1587/transinf.2021EDL8053
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2022
AB - Differential Evolution (DE) algorithm is simple and effective. Since DE has been proposed, it has been widely used to solve various complex optimization problems. To further exploit the advantages of DE, we propose a new variant of DE, termed as ranking-based differential evolution (RDE), by performing ranking on the population. Progressively better individuals in the population are used for mutation operation, thus improving the algorithm's exploitation and exploration capability. Experimental results on a number of benchmark optimization functions show that RDE significantly outperforms the original DE and performs competitively in comparison with other two state-of-the-art DE variants.
ER -
English
