摘要
将基于模糊C均值聚类改进的多目标优化算法(A fuzzy c-means clustering based evolutionary algorithm, FCEA)与高价单目标优化算法(Efficient global optimization,EGO)进行融合,基于Kriging模型提出了一种改进的多目标优化算法(FCEA-EGO)。在FCEA-EGO算法寻优过程中,利用模糊C均值聚类算法从整个种群中选择相似个体进行遗传操作,引导算法进行寻优;基于EGO算法的校正点选择机制,逐步修正校正点,提高Kriging模型精度。实验结果表明,FCEA-EGO算法相对于典型的高价多目标优化算法MOEA/D-EGO、ParEGO、SMS-EGO具有更优异的求解能力。最后,基于FCEA-EGO算法对某轻型飞机的齿轮减速器进行了优化设计,验证了其求解实际工程优化问题的能力。
A fuzzy C-Means Clustering Based Evolutionary Algorithm (FCEA) is merged with Efficient Global Optimization (EGO), and a Kriging model is proposed to develop a multi-objective optimization algorithm (FCEA-EGO). In the optimization process of FCEA-EGO algorithm, the fuzzy C-means clustering algorithm is used to select similar individuals from the whole population for genetic operation, and the algorithm is guided to be optimized;Based on the EGO algorithm′s correction point selection mechanism, the correction points are gradually corrected to improve the accuracy of the Kriging model. The experimental results show that the FCEA-EGO algorithm has better solving ability than the typical high-price multi-objective optimization algorithms MOEA/D-EGO, ParEGO and SMS-EGO. Finally, based on the FCEA-EGO algorithm, the gear reducer of a light aircraft is optimized, and its ability to solve practical engineering optimization problems is verified.
作者
余竹玛
李梅
Yu Zhuma;Li Mei(Center of College Student Quality Education, Three Gorges University, Hubei Yichang 443002, China)
出处
《机械科学与技术》
CSCD
北大核心
2019年第6期977-984,共8页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(71501110)资助
关键词
KRIGING模型
多目标优化设计
校正点选择
齿轮减速器
Kriging model
Multi-objective optimization design
correction point selection
gear reducer
fuzzy C-means clustering algorithm
efficient global optimization
