惩罚法
适应度函数
遗传算法
适应度近似
地铁列车时刻表
数学优化
还原(数学)
跳跃
功率(物理)
功能(生物学)
进化算法
极限(数学)
过程(计算)
计算机科学
数学
量子力学
进化生物学
生物
几何学
操作系统
物理
数学分析
作者
Guang Liang Zhang,Zhang Wei Wang,Shi Hong Zhang
出处
期刊:Materials Science Forum
日期:2013-07-01
卷期号:762: 307-312
被引量:1
标识
DOI:10.4028/www.scientific.net/msf.762.307
摘要
A fast optimization approach is demonstrated for design optimization of the multi-pass wire drawing process with the multi-objective genetic algorithm, and with the aims at minimizing both power consumption and temperature, via optimizing the process parameters involving pass number, pass schedule, die angle, bearing length and loops on capstan etc. A jump fitness function and a penalty fitness function are proposed for the survival of good designs and killing the bad designs which temperature, die wear factor, delta factor, or ratio of drawing stress to yield stress exceed the limits during optimization. The numerical examples show that the optimizer with the penalty fitness function, when its parameter n ranges from 1 to 2, presents the best performance in finding the minimum power consumption with a limit in temperature. Compared with a reference design, a significant reduction in the total power consumption about 300W, with the well control in temperature, delta factor and die life, has been achieved by the optimization. The penalty fitness function presents the better performance in the reduction of the iteration generations and computational cost to the jump fitness function.
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