分类
数学优化
遗传算法
多目标优化
变量(数学)
整数(计算机科学)
过程(计算)
算法
计算机科学
帕累托原理
功能(生物学)
可变邻域搜索
数学
元启发式
生物
进化生物学
操作系统
数学分析
程序设计语言
作者
Yin JunHua,Cuimei Bo,Jun Li,Yan Huang
标识
DOI:10.1109/ccdc.2018.8407249
摘要
An enhanced non-dominated sorting genetic algorithm (NSGA-II) was applied to carry out the integer optimized design in this article. The steady simulation design was established based on the reaction kinetics analysis using Aspen Plus, and the feasible region of each optimized variable was calculated through sensitive analysis. Aiming at the total cost TAC as the objective function, the NSGA-II algorithm was used directly to obtain the Pareto optimal solutions under the constraints of feasible region of variables. The simulation results showed that the NSGA-II algorithm can effectively reduce the total cost of TAC.
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