| During the process of electrolytic copper, because of the impact of slow disturbance, the entire production process may be deviated from the optimum position. The steady-state optimization is to find the optimum operating parameters to maintain the working conditions of industrial process. This paper does research in Multi-Objective Genetic Algorithm and its application in the process of electrolytic copper.Among numerous multi-objective optimization algorithms, the Non-dominated Sorting Genetic AlgorithmⅡ(NSGA_Ⅱ) is a representative algorithm. This method has good astringency, and high calculation efficiency. Based on NSGA_Ⅱ, this paper carried on careful analysis and research. To solve the discontented representation of NSGA_Ⅱwhen it is used in high dimensionality and non-dominated sorting in NSGA_Ⅱused more time, this paper proposed the corresponding improved strategy. This paper introduces a new calculating algorithm of crowding-distance, in this way, its diversity of solutions become better. This paper uses interactive non-dominated sorting to save the operation time. The simulation indicates that the improved NSGA_Ⅱhas better diversity of solutions and shorter calculation time than NSGAⅡ.For the purpose of getting better quality of electrolytic copper and saving the electrical energy, this paper analyzes the Mechanism of electrolytic copper, factors affecting the copper electrolyte composition, and factors affecting power consumption, and then establishes functions of Multi-objective optimization. According to Jiangxi electrolytic copper materials and process requirements, establishes the constraints. Finally obtains a multi-objective optimization model. The improved NSGA_Ⅱis applied to multi-objective optimization model. Then the Pareto set is obtained。This paper used the improved ideal point algorithm to find proper operational parameters in the Pareto set which has been obtained. This process will push the system go to optimum position step by step.
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