Operator-Adapted Evolutionary Large-Scale Multiobjective Optimization for Voltage Transformer Ratio Error Estimation

Changwu HUANG, Lianghao LI, Cheng HE, Ran CHENG, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)

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. © 2021, Springer Nature Switzerland AG.
Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization : 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings
EditorsHisao ISHIBUCHI, Qingfu ZHANG, Ran CHENG, Ke LI, Hui LI, Handing WANG, Aimin ZHOU
PublisherSpringer Science and Business Media Deutschland GmbH
Pages672-683
Number of pages12
ISBN (Electronic)9783030720629
ISBN (Print)9783030720612
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event11th International Conferenceon Evolutionary Multi-Criterion Optimization - Shenzhen, China
Duration: 28 Mar 202131 Mar 2021

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12654
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameTheoretical Computer Science and General Issues
PublisherSpringer
ISSN (Print)2512-2010
ISSN (Electronic)2512-2029

Conference

Conference11th International Conferenceon Evolutionary Multi-Criterion Optimization
Abbreviated titleEMO 2021
Country/TerritoryChina
CityShenzhen
Period28/03/2131/03/21

Bibliographical note

This work was supported by the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2019A1515110575), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No. 2017ZT07X386), the Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531), the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008), and the National Natural Science Foundation of China (No. 61903178 and 61906081).

Keywords

  • Adaptive operator selection
  • Large-scale optimization
  • Multiobjective optimization
  • Voltage transformer ratio error estimation

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