TY - GEN
T1 - An MOEA/D with multiple differential evolution mutation operators
AU - Li, Yang
AU - Zhou, Aimin
AU - Zhang, Guixu
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/16
Y1 - 2014/9/16
N2 - In evolutionary algorithms, the reproduction operators play an important role. It is arguable that different operators may be suitable for different kinds of problems. Therefore, it is natural to combine multiple operators to achieve better performance. To demonstrate this idea, in this paper, we propose an MOEA/D with multiple differential evolution mutation operators called MOEA/D-MO. MOEA/D aims to decompose a multiobjective optimization problem (MOP) into a number of single objective optimization problems (SOPs) and optimize those SOPs simultaneously. In MOEA/D-MO, we combine multiple operators to do reproduction. Three mutation strategies with randomly selected parameters from a parameter pool are used to generate new trial solutions. The proposed algorithm is applied to a set of test instances with different complexities and characteristics. Experimental results show that the proposed combining method is promising.
AB - In evolutionary algorithms, the reproduction operators play an important role. It is arguable that different operators may be suitable for different kinds of problems. Therefore, it is natural to combine multiple operators to achieve better performance. To demonstrate this idea, in this paper, we propose an MOEA/D with multiple differential evolution mutation operators called MOEA/D-MO. MOEA/D aims to decompose a multiobjective optimization problem (MOP) into a number of single objective optimization problems (SOPs) and optimize those SOPs simultaneously. In MOEA/D-MO, we combine multiple operators to do reproduction. Three mutation strategies with randomly selected parameters from a parameter pool are used to generate new trial solutions. The proposed algorithm is applied to a set of test instances with different complexities and characteristics. Experimental results show that the proposed combining method is promising.
UR - https://www.scopus.com/pages/publications/84908570618
U2 - 10.1109/CEC.2014.6900339
DO - 10.1109/CEC.2014.6900339
M3 - 会议稿件
AN - SCOPUS:84908570618
T3 - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
SP - 397
EP - 404
BT - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Y2 - 6 July 2014 through 11 July 2014
ER -