Population-based discrete state transition algorithm with decomposition and knowledge guidance applied to electrolytic cell maintenance decision

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.