TY - GEN
T1 - Dynamic constrained multi-objective model for solving constrained optimization problem
AU - Zeng, Sanyou
AU - Chen, Shizhong
AU - Zhao, Jiang
AU - Zhou, Aimin
AU - Li, Zhengjun
AU - Jing, Hongyong
PY - 2011
Y1 - 2011
N2 - Constrained optimization problem (COP) is skillfully converted into dynamic constrained multi-objective optimization problem (DCMOP) in this paper. Then dynamic constrained multi-objective evolutionary algorithms (DCMOEAs) can be used to solve the COP problem by solving the DCMOP problem. Seemingly, a complex DCMOEA algorithm is used to solve a relatively simple COP problem. However, the DCMOEA algorithm can adopt Pareto domination to achieve a good tradeoff between fast converging and global searching, and therefore a DCMOEA algorithm can effectively solve a COP problem by solving the DCMOP problem. An instance of DCMOEA was used to to solve 13 widely used constraint benchmark problems, The experimental results suggest it outperforms or performs similarly to other state-of-the-art algorithms referred to in this paper. The efficient performance of the DCMOEA algorithm shows, to some extend, the DCMOP model works well.
AB - Constrained optimization problem (COP) is skillfully converted into dynamic constrained multi-objective optimization problem (DCMOP) in this paper. Then dynamic constrained multi-objective evolutionary algorithms (DCMOEAs) can be used to solve the COP problem by solving the DCMOP problem. Seemingly, a complex DCMOEA algorithm is used to solve a relatively simple COP problem. However, the DCMOEA algorithm can adopt Pareto domination to achieve a good tradeoff between fast converging and global searching, and therefore a DCMOEA algorithm can effectively solve a COP problem by solving the DCMOP problem. An instance of DCMOEA was used to to solve 13 widely used constraint benchmark problems, The experimental results suggest it outperforms or performs similarly to other state-of-the-art algorithms referred to in this paper. The efficient performance of the DCMOEA algorithm shows, to some extend, the DCMOP model works well.
KW - Constrained optimization
KW - Dynamic multi-objective optimization
KW - Dynamic optimization
KW - Evolutionary algorithm
KW - Multi-objective optimization
UR - https://www.scopus.com/pages/publications/80051961502
U2 - 10.1109/CEC.2011.5949866
DO - 10.1109/CEC.2011.5949866
M3 - 会议稿件
AN - SCOPUS:80051961502
SN - 9781424478347
T3 - 2011 IEEE Congress of Evolutionary Computation, CEC 2011
SP - 2041
EP - 2046
BT - 2011 IEEE Congress of Evolutionary Computation, CEC 2011
T2 - 2011 IEEE Congress of Evolutionary Computation, CEC 2011
Y2 - 5 June 2011 through 8 June 2011
ER -