Assembly Sequence Planning Utilizing Chaotic Adaptive Ant Colony Optimization Algorithm
The chaotic adaptive ant colony optimization algorithm (CAACO) is proposed to seek the optimal or near-optimal assembly sequences of mechanical products. Different from the general AACO algorithm, the parameter p denoting the global evaporation rate of the AACO algorithm is not specified by the designers, but is generated with the chaotic operators in the optimization process. An example is used to validate the capability of the CAACO algorithm, and the results show that the robustness of the CAACO algorithm is enhanced and more ants in the ant colony can find their own optimal or nearoptimal assembly sequences than those of the general AACO algorithm.
Assembly sequence planning adaptive ant colony optimization chaotic operator
Yong Wang De Tian Jihong Liu
Renewable Energy School, North China Electric Power University, Beijing, China School of Mechanical Engineering and Automation, Beihang University, China
国际会议
2010 International Conference on Advanced Mechanical Engineering(2010年先进机械工程国际学术会议 AME 2010)
洛阳
英文
391-396
2010-09-04（万方平台首次上网日期，不代表论文的发表时间）