Genetic algorithm-based decomposition method for multidisciplinary design optimization
-
摘要: 通过分析现有的多学科设计优化中任务分解方法(枚举法、聚类识别法和分支定界法)的特点,指出了现有方法的不足.提出了将遗传算法应用于优化任务的分解问题,给出了具体的算法描述和详细的任务分解算法流程,并分析总结给出了该算法的优点:①遗传算法对搜索空间没有任何要求,因此对函数关系矩阵(FDT,Function Dependence Table)也没有任何要求;②遗传算法是一种随机迭代方法,不需要估计初值;③遗传算法同时对一组解进行搜索,大大提高了搜索速度,在保证计算精度的基础上得到全局最优解.最后还以齿轮减速器优化问题为例,将遗传算法应用于上述问题的任务分解过程,得到了较为满意的分解结果,并从计算方案次数的角度定量地比较了所提出方法与现有方法的区别,从而证明了该方法的正确性和优越性.Abstract: Based on the analyzing of current methods of solving task decomposition problem in MDO, such as enumerate algorithm, clustering identification method, branch and bound method(BBM), their disadvantages were pointed out. Then, the description and detailed flow were delivered. The advantages of GA were described as follows: first, there is no special request for search space and function dependence table (FDT); second, GA is a stochastic iterative method, so the initial value is unnecessary; third, GA searches from a group of solutions simultaneity, which improves the search speed and gets global optimal solution with higher accuracy. Finally, the gear reducer optimization problem was taking as an example and GA was used. The analyzing result was acceptable. The calculation quantity was reduced rapidly. The correctness and advantage of the GA were proved when compared the calculation quantity with the methods mentioned before.
-
[1] Papalambors P Y. Extending the optimization paradigm in engineering design Fourer R. Third Symposium on Tools and Method of Competitive Engineering. Netherlands: Delft University of Technology Press, 2000: 19-28 [2] 钟毅芳,陈柏鸿,王周宏.多学科综合优化设计原理与方法[M].武汉:华中科技大学出版社,2006:42-50 Zhong Yifang, Chen Bohong, Wang Zhouhong. Theory and application of multidisciplinary integrating optimization[M]. Wuhan: Huazhong University of Science & Technology Press, 2006:42-50(in Chinese) [3] Kusaik A, Chow W S. Efficient solving of the group technology problem[J]. Journal of Manufacturing System, 1987, 6(2):177-123 [4] Kusiak A, Cheng C. A branch-and-bound algorithm for solving group technology problems[J]. Annals of Operational Research, 1990, 26(2): 415-431 [5] 刘勇,康立山,陈毓屏.非数值并行算法——遗传算法[M].北京:科学出版社, 1994:26-32 Liu Yong, Kang Lishan, Chen Yuping. Non-numeric parallel arithmetic-genetic algorithm[M]. Beijing: Scientific Press, 1994: 26-32 (in Chinese) [6] Holland J H. Adaptation in natural and artificial systems[M]. Ann Arbor: University of Michigan Press, 1975: 56-68 [7] Goldberg D E. Genetic algorithms in search, optimization and machine learning[J]. MA: Addison-Wesley, 1989, 28(3):172-174 [8] Sutton R S, Butto A G. Reinforcement learning: introduction[M]. Cambridge: MIT Press, 1998:77-89 [9] 邢文训,谢金星.现代优化计算方法[M].北京:清华大学出版社, 2005: 113-147 Xing Wenxun, Xie Jinxing. Modern optimization algorithm[M]. Beijing: Tsinghua University Press, 2005: 113-147(in Chinese) -
点击查看大图
计量
- 文章访问数: 3626
- HTML全文浏览量: 201
- PDF下载量: 1119
- 被引次数: 0
下载:
