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• W4366128160 abstract "Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications. The large number of decision variables in LSMOP leads to a tremendous high-dimensional search space, which is still challenging for existing multiobjective evolutionary algorithms (MOEAs). The voltage transformer ratio error estimation (TREE) problem is a typical LSMOP in real-world applications. A number of large-scale multiobjective evolutionary algorithms (LSMOEAs) have been adopted for solving the TREE problem, but so far there is still considerable potential for improvement in their results. In this paper, we propose a informed initialization for solving the TREE problem. The physical properties of the TREE problem are used to construct an initial set of solutions with specific characteristics. The proposed initialization method is tested against the default initialization method on five TREE problems by being applied on six state-of-the-art LSMOEAs and a classical MOEA. The experimental results demonstrate that the initialization method has a significant effect on the performance of LSMOEAs in solving the TREE problems." @default.
• W4366128160 created "2023-04-19" @default.
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• W4366128160 date "2023-01-01" @default.
• W4366128160 modified "2023-09-25" @default.
• W4366128160 title "A Comparison of Large-Scale MOEAs with Informed Initialization for Voltage Transformer Ratio Error Estimation" @default.
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• W4366128160 doi "https://doi.org/10.1007/978-981-99-1549-1_18" @default.
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