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望目特性连续型参数稳健设计遗传优化方法
引用本文:沈玲,曾强,宋红娜,吴立云.望目特性连续型参数稳健设计遗传优化方法[J].计算机工程与应用,2013(23):215-220,251.
作者姓名:沈玲  曾强  宋红娜  吴立云
作者单位:[1]河南理工大学安全科学与工程学院,河南焦作454000 [2]河南理工大学能源科学与工程学院,河南焦作454000
基金项目:国家自然科学基金(No.51104056);河南省教育厅科学技术研究项目(No.128120005).
摘    要:针对一类可通过理论计算得到输出特性值的望目特性连续型参数稳健设计问题,提出了一种遗传进化方法。描述了研究的问题;提出了望目特性连续型参数稳健设计遗传进化方法的技术思路:以密集抽样取代离散化处理,以个体取代试验方案,以变化的种群取代固定的内表,通过遗传进化得到最优设计方案。提出并设计了一种望目特性连续型参数稳健设计遗传算法,阐述了算法的计算流程、个体编码、适应度、种群初始化、解码操作及遗传操作。通过案例分析验证了所提方法的有效性。

关 键 词:参数稳健设计  望目特性  连续型参数  优化方法  遗传算法

Optimization method based on genetic evolution for robust continuous parameter design in target being best
SHEN Ling,ZENG Qiang,SONG Hongna,WU Liyun.Optimization method based on genetic evolution for robust continuous parameter design in target being best[J].Computer Engineering and Applications,2013(23):215-220,251.
Authors:SHEN Ling  ZENG Qiang  SONG Hongna  WU Liyun
Affiliation:1.School of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo, Henan 454000, China 2.School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo, Henan 454000, China)
Abstract:Aiming at the problem of robust continuous parameter design in the target being best, in which the output value can be got by theoretical calculation, an optimization method based on genetic evolution is proposed. The researched problem in this paper ~s described. The technical idea of the optimization method based on genetic evolution for the researched problem is pre- sented. In the proposed method, the discretization is replaced by dense sampling, the experiment scheme is replaced by individual, the fixed internal table is replaced by transformable population, and the optimal design scheme is got through genetic evolution process. The genetic algorithm for robust continuous parameter design in the target being best is presented and designed. The cal- culation flow, the individual coding, the individual fitness, the population initialization, decoding operation and genetic opera- tion are described. The effectiveness of the proposed method is validated by case study.
Keywords:robust parameter design  target being best  continuous parameter  optimization method  genetic algorithm
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