TY - GEN
T1 - Adaptive collaborative optimization strategy based on genetic algorithm
AU - Liu, Jihong
AU - Jiang, Hao
AU - Xie, Qi
PY - 2011
Y1 - 2011
N2 - The genetic algorithm and the adaptive mechanism are adopted to tackle the inefficiency of optimization and the convergence difficulty of collaborative optimization (CO). Based on the further analysis of collaborative optimization process, the constraint conditions are converged into part of the optimization function. The system optimization model of CO has been reconstructed according to the adaptive penalty function which is based on the information of different disciplines and the transformation of system-level constraints. Therefore, the global and local search capabilities of optimization algorithm and searching efficiency of CO have been improved. Meanwhile, the difficulty of convergence caused by the internal definition of CO has been resolved. Finally, an example of speed reducer is demonstrated to verify the proposed method, showing that the convergence rate and search efficiency have been improved.
AB - The genetic algorithm and the adaptive mechanism are adopted to tackle the inefficiency of optimization and the convergence difficulty of collaborative optimization (CO). Based on the further analysis of collaborative optimization process, the constraint conditions are converged into part of the optimization function. The system optimization model of CO has been reconstructed according to the adaptive penalty function which is based on the information of different disciplines and the transformation of system-level constraints. Therefore, the global and local search capabilities of optimization algorithm and searching efficiency of CO have been improved. Meanwhile, the difficulty of convergence caused by the internal definition of CO has been resolved. Finally, an example of speed reducer is demonstrated to verify the proposed method, showing that the convergence rate and search efficiency have been improved.
KW - Adaptive
KW - Collaborative optimization
KW - Hybrid intelligent optimization algorithm
KW - Multidisciplinary design optimization
UR - https://www.scopus.com/pages/publications/80053038865
U2 - 10.4028/www.scientific.net/AMR.311-313.32
DO - 10.4028/www.scientific.net/AMR.311-313.32
M3 - 会议稿件
AN - SCOPUS:80053038865
SN - 9783037852149
T3 - Advanced Materials Research
SP - 32
EP - 36
BT - Advanced Materials and Processes
T2 - 2011 International Conference on Advanced Design and Manufacturing Engineering, ADME 2011
Y2 - 16 September 2011 through 18 September 2011
ER -