TY - GEN
T1 - Assembly sequence planning utilizing chaotic adaptive ant colony optimization algorithm
AU - Wang, Yong
AU - Tian, De
AU - Liu, Jihong
PY - 2010
Y1 - 2010
N2 - 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 ρ 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 near-optimal assembly sequences than those of the general AACO algorithm.
AB - 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 ρ 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 near-optimal assembly sequences than those of the general AACO algorithm.
KW - Adaptive ant colony optimization
KW - Assembly sequence planning
KW - Chaotic operator
UR - https://www.scopus.com/pages/publications/78650885831
U2 - 10.4028/www.scientific.net/AMM.26-28.391
DO - 10.4028/www.scientific.net/AMM.26-28.391
M3 - 会议稿件
AN - SCOPUS:78650885831
SN - 9780878492497
T3 - Applied Mechanics and Materials
SP - 391
EP - 396
BT - Advanced Mechanical Engineering
T2 - 2010 International Conference on Advanced Mechanical Engineering, AME 2010
Y2 - 4 September 2010 through 5 September 2010
ER -