Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

TIAN Ye; ZHANG Xingyi; HE Cheng; TAN Kay Chen; JIN Yaochu

doi:10.23919/cje.2022.00.100

Volume 32 Issue 1

Jan. 2023

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TIAN Ye, ZHANG Xingyi, HE Cheng, et al., “Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 111-129, 2023, doi: 10.23919/cje.2022.00.100

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TIAN Ye, ZHANG Xingyi, HE Cheng, et al., “Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 111-129, 2023, doi: 10.23919/cje.2022.00.100

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Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

doi: 10.23919/cje.2022.00.100

1.
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
2.
School of Artificial Intelligence, Anhui University, Hefei 230601, China
3.
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
4.
Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China
5.
Faculty of Technology, Bielefeld University, Bielefeld 33619, Germany

Funds: This work was supported in part by the National Key R&D Program of China (2018AAA0100100), the National Natural Science Foundation of China (61876162, 61906001, 61903178, 62136008, U20A20306, U21A20512), and an Alexander von Humboldt Professorship for Artificial Intelligence funded by the Federal Ministry of Education and Research, Germany

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Author Bio:
Ye TIAN was born in Anhui Province in 1991. He received the B.S., M.S., and Ph.D. degrees from Anhui University, Hefei, China, in 2012, 2015, and 2018, respectively. He is currently an Associate Professor with the Institutes of Physical Science and Information Technology, Anhui University, Hefei, China. His current research interests include evolutionary computation and its applications. (Email: field910921@gmail.com)

Xingyi ZHANG (corresponding author) was born in Anhui Province in 1982. He received the B.S. degree from Fuyang Normal College, Fuyang, China, in 2003, and the M.S. and Ph.D. degrees from Huazhong University of Science and Technology, Wuhan, China, in 2006 and 2009, respectively. He is currently a Professor with the School of Artificial Intelligence, Anhui University, Hefei, China. His current research interests include unconventional models and algorithms of computation, evolutionary multi-objective optimization, and logistic scheduling. (Email: xyzhanghust@gmail.com)

Cheng HE was born in Hubei Province in 1989. He received the B.E. degree from the Wuhan University of Science and Technology, Wuhan, China, in 2012, and the Ph.D. degree from the Huazhong University of Science and Technology, Wuhan, China, in 2018. He is currently an Associate Professor with the School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, China. His current research interests include model-based evolutionary algorithms, multiobjective optimization, large-scale optimization, deep learning, and their applications. (Email: chenghehust@gmail.com)

Kay Chen TAN received the B.E. (First Class Hons.) and Ph.D. degrees from the University of Glasgow, Glasgow, UK, in 1994 and 1997, respectively. He is currently a Chair Professor of the Department of Computing at the Hong Kong Polytechnic University, Hong Kong SAR, China. (Email: kctan@polyu.edu.hk)

Yaochu JIN was born in 1966. He received the B.S., M.S., and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. degree from Ruhr University Bochum, Germany, in 2001. He is currently an Alexander von Humboldt Professor for artificial intelligence, Chair of Nature Inspired Computing and Engineering, Faculty of Technology, Bielefeld University, Germany. He is also a Distinguished Chair, Professor in computational intelligence, Department of Computer Science, University of Surrey, Guildford, UK. (Email: yaochu.jin@uni-bielefeld.de)
Received Date: 2022-04-23
Accepted Date: 2022-07-13

Available Online: 2022-07-22

Publish Date: 2023-01-05

Abstract

Abstract

A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed, new operators are automatically designed in this work, which are expected to be search space independent and thus exhibit robust performance on different problems. This work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. This work then deduces the generic form of translation, scale, and rotation invariant operators, and proposes a principled approach for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000 decision variables.
- Metaheuristics,
- Evolutionary computation,
- Swarm intelligence,
- Variation operator,
- Transformation invariance,
- Automated design

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