Adaptive multiobjective evolutionary algorithm for large-scale transformer ratio error estimation

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

Research output: Journal PublicationsJournal Article (refereed)peer-review

2 Citations (Scopus)


As a typical large-scale multiobjective optimization problem extracted from real-world applications, the voltage transformer ratio error estimation (TREE) problem is challenging for existing evolutionary algorithms (EAs). Due to the large number of decision variables in the problems, existing algorithms cannot solve TREE problems efficiently. Besides, most EAs may fail to balance the convergence enhancement and diversity maintenance, leading to the trap in local optima even at the early stage of the evolution. This work proposes an adaptive large-scale multiobjective EA (LSMOEA) to handle the TREE problems with thousands of decision variables. Generally, multiple efficient offspring generation and environmental selection strategies selected from some representative LSMOEAs are included. Then an adaptive selection strategy is used to determine which offspring generation and environmental selection operators are used in each generation of the evolution. Thus, the search behavior of the proposed algorithm evolves along with the evolution process, the balance between convergence and diversity is maintained, and the proposed algorithm is expected to solve TREE problems effectively and efficiently. Experimental results show that the proposed algorithm achieves significant performance improvement due to the adaptive selection of different operators, providing an effective and efficient approach for large-scale optimization problems. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Original languageEnglish
Pages (from-to)237-251
Number of pages15
JournalMemetic Computing
Issue number2
Early online date4 May 2022
Publication statusPublished - Jun 2022
Externally publishedYes

Bibliographical note

This work was supported by the National Natural Science Foundation of China (Nos. U20A20306, 61903178, and 61906081), the Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515110575), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), and the Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531 and RCBS20200714114817264)


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


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