@inproceedings{d2d17c5540614bd0b0ea94a0c8204b46,
title = "Operator-Adapted Evolutionary Large-Scale Multiobjective Optimization for Voltage Transformer Ratio Error Estimation",
abstract = "Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications, and they are challenging for existing evolutionary algorithms due to their massive volume of search space. Despite that a number of large-scale multiobjective evolutionary algorithms (LSMOEAs) have been proposed in recent years, their effectiveness in solving LSMOPs remains unsatisfactory. One main reason is that most existing LSMOEAs may fail to balance convergence enhancement and diversity maintenance, especially for solving real-world problems. To address this issue, we propose to use a hybridized LSMOEA with adaptive operator selection (AOS) to handle real-world LSMOPs. Specifically, the proposed hybridized LSMOEA with AOS (AOS-LSMOEA) includes multiple different offspring generation and environmental selection strategies extracted from some existing LSMOEAs. Then it uses the AOS to adaptively determine the application rates of different offspring generation and environmental selection operators in an online manner. The proposed approach is capable of taking advantage of existing LSMOEAs, and the AOS enables the algorithm to choose suitable operators for solving different LSMOPs. In this study, the proposed algorithm is expected to solve the voltage transformer ratio error estimation (TREE) problems effectively. Experimental results show that AOS-LSMOEA achieves significant performance improvement due to the hybridization of different operators and the adoption of AOS method. {\textcopyright} 2021, Springer Nature Switzerland AG.",
keywords = "Adaptive operator selection, Large-scale optimization, Multiobjective optimization, Voltage transformer ratio error estimation",
author = "Changwu HUANG and Lianghao LI and Cheng HE and Ran CHENG and Xin YAO",
year = "2021",
doi = "10.1007/978-3-030-72062-9_53",
language = "English",
isbn = "9783030720612",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "672--683",
editor = "ISHIBUCHI, {Hisao } and ZHANG, {Qingfu } and CHENG, {Ran } and Ke LI and Hui LI and WANG, {Handing } and ZHOU, {Aimin }",
booktitle = "Evolutionary Multi-Criterion Optimization : 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings",
address = "Germany",
note = "11th International Conferenceon Evolutionary Multi-Criterion Optimization, EMO 2021 ; Conference date: 28-03-2021 Through 31-03-2021",
}