A Novel Differential Evolution Algorithm Based on Local Fitness Landscape Information for Optimization Problems
Summary :
The selection of mutation strategy greatly affects the performance of differential evolution algorithm (DE). For different types of optimization problems, different mutation strategies should be selected. How to choose a suitable mutation strategy for different problems is a challenging task. To deal with this challenge, this paper proposes a novel DE algorithm based on local fitness landscape, called FLIDE. In the proposed method, fitness landscape information is obtained to guide the selection of mutation operators. In this way, different problems can be solved with proper evolutionary mechanisms. Moreover, a population adjustment method is used to balance the search ability and population diversity. On one hand, the diversity of the population in the early stage is enhanced with a relative large population. One the other hand, the computational cost is reduced in the later stage with a relative small population. The evolutionary information is utilized as much as possible to guide the search direction. The proposed method is compared with five popular algorithms on 30 test functions with different characteristics. Experimental results show that the proposed FLIDE is more effective on problems with high dimensions.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.601-616
- Publication Date
- 2023/05/01
- Publicized
- 2023/02/13
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022DLP0010
- Type of Manuscript
- Special Section PAPER (Special Section on Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications)
- Category
- Core Methods
Authors
Jing LIANG
Zhengzhou University
Ke LI
Zhengzhou University
Kunjie YU
Zhengzhou University
Caitong YUE
Zhengzhou University
Yaxin LI
Zhengzhou University
Hui SONG
RMIT University
Keyword
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Jing LIANG, Ke LI, Kunjie YU, Caitong YUE, Yaxin LI, Hui SONG, "A Novel Differential Evolution Algorithm Based on Local Fitness Landscape Information for Optimization Problems" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 5, pp. 601-616, May 2023, doi: 10.1587/transinf.2022DLP0010.
Abstract: The selection of mutation strategy greatly affects the performance of differential evolution algorithm (DE). For different types of optimization problems, different mutation strategies should be selected. How to choose a suitable mutation strategy for different problems is a challenging task. To deal with this challenge, this paper proposes a novel DE algorithm based on local fitness landscape, called FLIDE. In the proposed method, fitness landscape information is obtained to guide the selection of mutation operators. In this way, different problems can be solved with proper evolutionary mechanisms. Moreover, a population adjustment method is used to balance the search ability and population diversity. On one hand, the diversity of the population in the early stage is enhanced with a relative large population. One the other hand, the computational cost is reduced in the later stage with a relative small population. The evolutionary information is utilized as much as possible to guide the search direction. The proposed method is compared with five popular algorithms on 30 test functions with different characteristics. Experimental results show that the proposed FLIDE is more effective on problems with high dimensions.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022DLP0010/_p
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@ARTICLE{e106-d_5_601,
author={Jing LIANG, Ke LI, Kunjie YU, Caitong YUE, Yaxin LI, Hui SONG, },
journal={IEICE TRANSACTIONS on Information},
title={A Novel Differential Evolution Algorithm Based on Local Fitness Landscape Information for Optimization Problems},
year={2023},
volume={E106-D},
number={5},
pages={601-616},
abstract={The selection of mutation strategy greatly affects the performance of differential evolution algorithm (DE). For different types of optimization problems, different mutation strategies should be selected. How to choose a suitable mutation strategy for different problems is a challenging task. To deal with this challenge, this paper proposes a novel DE algorithm based on local fitness landscape, called FLIDE. In the proposed method, fitness landscape information is obtained to guide the selection of mutation operators. In this way, different problems can be solved with proper evolutionary mechanisms. Moreover, a population adjustment method is used to balance the search ability and population diversity. On one hand, the diversity of the population in the early stage is enhanced with a relative large population. One the other hand, the computational cost is reduced in the later stage with a relative small population. The evolutionary information is utilized as much as possible to guide the search direction. The proposed method is compared with five popular algorithms on 30 test functions with different characteristics. Experimental results show that the proposed FLIDE is more effective on problems with high dimensions.},
keywords={},
doi={10.1587/transinf.2022DLP0010},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - A Novel Differential Evolution Algorithm Based on Local Fitness Landscape Information for Optimization Problems
T2 - IEICE TRANSACTIONS on Information
SP - 601
EP - 616
AU - Jing LIANG
AU - Ke LI
AU - Kunjie YU
AU - Caitong YUE
AU - Yaxin LI
AU - Hui SONG
PY - 2023
DO - 10.1587/transinf.2022DLP0010
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2023
AB - The selection of mutation strategy greatly affects the performance of differential evolution algorithm (DE). For different types of optimization problems, different mutation strategies should be selected. How to choose a suitable mutation strategy for different problems is a challenging task. To deal with this challenge, this paper proposes a novel DE algorithm based on local fitness landscape, called FLIDE. In the proposed method, fitness landscape information is obtained to guide the selection of mutation operators. In this way, different problems can be solved with proper evolutionary mechanisms. Moreover, a population adjustment method is used to balance the search ability and population diversity. On one hand, the diversity of the population in the early stage is enhanced with a relative large population. One the other hand, the computational cost is reduced in the later stage with a relative small population. The evolutionary information is utilized as much as possible to guide the search direction. The proposed method is compared with five popular algorithms on 30 test functions with different characteristics. Experimental results show that the proposed FLIDE is more effective on problems with high dimensions.
ER -
English
