基于遗传算法的行星轮系混合离散变量优化

王盛; 龚宪生

doi:10.13433/j.cnki.1003-8728.2014.0808

基于遗传算法的行星轮系混合离散变量优化

doi: 10.13433/j.cnki.1003-8728.2014.0808

王盛¹,
龚宪生^1,2

1.
重庆大学机械工程学院, 重庆 400044;
2.
重庆大学机械传动国家重点实验室, 重庆 400044

基金项目:

国家自然科学基金项目(51175525)

重庆大学机械传动国家重点实验室自主研究基金项目(0301002109137)

国家高技术研究发展计划(863计划)项目(2007AA041802)资助

详细信息

作者简介:
王盛(1988-)，硕士研究生，研究方向为机械传动智能化设计及应用，wangsheng360429@163.com;龚宪生(联系人)，教授，博士，博士生导师，cqxsgong@cqu.edu.cn

王盛(1988-)，硕士研究生，研究方向为机械传动智能化设计及应用，wangsheng360429@163.com;龚宪生(联系人)，教授，博士，博士生导师，cqxsgong@cqu.edu.cn

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出版历程
- 收稿日期: 2013-01-31

Hybrid Discrete Variable Optimization Design of Planetary Gear Train Based on Genetic Algorithm

Wang Sheng¹,
Gong Xiansheng^1,2

1.
College of Mechanical Engineering, Chongqing University, Chongqing 400044;
2.
The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044

摘要

摘要: 行星轮系设计复杂,各参数受到配齿、可靠性和干涉等条件的限制。模数还受到国家标准的制约,只能选取一些离散值,用经典优化算法求解效果很差。遗传算法处理对象不是变量本身,而是经过编码后的基因,这种先天的优势能较好的处理这类变量类型多样的优化问题。通过对齿数和模数的处理,将无序的离散值转变为连续的整型数。提出了一种改进的实值编码方式,建立满足配齿、变位系数、干涉和强度等约束条件,以体积最小为目标函数的优化模型。合理选择遗传算法控制参数,编写了Matlab程序对所建的模型进行求解。研究结果表明:该改进算法全局寻优能力强,多次启动均能收敛于统一最优解。在满足各约束条件的情况下,优化后体积减小了21.056%。
- 行星轮系 /
- 混合离散变量 /
- 遗传算法 /
- 实值编码 /
- 优化模型
Abstract: The design of the planetary gear train is a complicated work,in which each parameter restricted by the conditions such as teeth,reliability and interference can not be chosen arbitrarily; and the modulus constrained by national standards must be only selected as a number of discrete values,so the classic optimization algorithm is poor to solve this work well. The processing object of genetic algorithm is encoded genes rather than the variable itself,and this innate advantage can deal with such optimization problem with diverse types of variables better. Unordered discrete values are changed into consecutive integers through processing the number of teeth and the modulus. An improved encoding way of real value is proposed and the optimization models for planetary gear train with volume smallest objective function is constructed,which can satisfy the conditions such as teeth,modification coefficient,interference and strength. The models are solved by means of Matlab programming language through reasonably choosing the control parameters of genetic algorithm. The results show that the improved algorithm has better global optimization capability and converges to an optimal point in several startups. The optimized volume has decreased21. 056% under the constraints above. This optimization design method has a certain value of economic and engineering applications.
- constrained optimization /
- flowcharting /
- gears /
- genetic algorithms /
- global optimization

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