遗传算法在电炉模型中的应用  

The Application of the Genetic Algorithms to Electric Furnace Model

作　　者：付中华 黄其明 艾磊[2] 刘春霆[2] 胡燕 

机构地区：[1]重庆赛迪冶炼装备系统集成工程技术研究中心有限公司,重庆401122 [2]中冶赛迪工程技术股份有限公司,重庆400013

出　　处：《工业加热》2014年第6期68-69,共2页Industrial Heating

摘　　要：遗传算法是实现目标优化的重要算法工具,用于目标函数最小或最大化的优化计算,将遗传算法应用到电炉模型中,实现电炉原料优化配置和电炉生产成本最小化,是非常有意义的方法。阐述了如何使用遗传算法对电炉模型中的成本进行优化,使电炉中各物料合理配置,实现电炉炼钢生产的低成本(或者趋于最小化)。Genetic algorithm is an important tool to realize aim optimazation, it is used for optimization calculation of minimal or maximal aim function. It is a valuable way to use genetic algorithm on electric furnace model, which can achieve goals of configuration optimized raw material in electric furnace and minimum cost of electric furnace production. It is expatiated how to make use of genetic algorithm to optimize the electric furnace cost in the model and configure each raw material suitably. In result, electric furnace steelmaking production could carry out with low cost or minimum cost.

关 键 词：遗传算法 电炉模型 物料配置 成本优化 

分 类 号：TF769[冶金工程—钢铁冶金]