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

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Abstract

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.

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Notes

  1. In the original TREE problems, several constraints and the third objective are also designed. Since the topic of this work is unconstrained large-scale multiobjective optimization, the constraints and the third objective are ignored in this work. Moreover, we have constructed the TREE7 problem from a substation with the same topology as TREE6. With different decision variables and sampled data, TREE7 is the same as TREE6 in function formulation.

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Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Changwu Huang, Cheng He, Ran Cheng & Xin Yao

  2. Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Lianghao Li

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  1. Changwu Huang
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  3. Cheng He
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  4. Ran Cheng
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Correspondence to Cheng He.

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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)

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Huang, C., Li, L., He, C. et al. Adaptive multiobjective evolutionary algorithm for large-scale transformer ratio error estimation. Memetic Comp. 14, 237–251 (2022). https://doi.org/10.1007/s12293-022-00368-7

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