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• W4313830094 endingPage "109996" @default.
• W4313830094 startingPage "109996" @default.
• W4313830094 abstract "With continuous scale expansion and higher safety requirements in modern aluminum electrolysis, it is more and more necessary to realize an intelligent decision-making for shutting-down and running of aluminum reduction cells (SRARC). However, this realization needs to solve a special single-objective constrained integer optimization problem (SCIOP), where the key challenge is a high requirement for the constraint-handling ability. In this paper, based on a high-scalability intelligent optimization algorithm called discrete state transition algorithm (DSTA), a population-based DSTA with decomposition and knowledge guidance (PDSTA/D-S) is proposed. This PDSTA/D-S improves the constraint-handling ability of DSTA from three aspects. Firstly, a hybrid framework combining DSTA with genetic algorithm is proposed. Secondly, a decomposition-based multi-objective optimization for constrained problems with uniformly-angled weighted sum vector is proposed. Thirdly, the manual decision-making is transferred to a knowledge-based transformation operator of DSTA. Therefore, a high-level performance of decision-making for SRARC can be obtained. The related experiments on a SRARC which is built from the practical production have demonstrated that the proposed PDSTA/D-S not only makes an effective improvement of DSTA from three aspects, but also has a more advanced performance compared with other existing high-performance intelligent optimization algorithms." @default.
• W4313830094 created "2023-01-09" @default.
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• W4313830094 date "2023-03-01" @default.
• W4313830094 modified "2023-09-26" @default.
• W4313830094 title "Population-based discrete state transition algorithm with decomposition and knowledge guidance applied to electrolytic cell maintenance decision" @default.
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• W4313830094 doi "https://doi.org/10.1016/j.asoc.2023.109996" @default.
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