TY - GEN
T1 - Towards adaptive weight vectors for multiobjective evolutionary algorithm based on decomposition
AU - Jiang, Siwei
AU - Feng, Liang
AU - Yang, Dazhi
AU - Heng, Chen Kim
AU - Ong, Yew Soon
AU - Zhang, Allan Nengsheng
AU - Tan, Puay Siew
AU - Cai, Zhihua
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/14
Y1 - 2016/11/14
N2 - The decomposition method in multiobjective evolutionary algorithms (MOEA/D) is an effective approach to evolve solutions along predefined weight vectors for solving multiobjective optimization problems (MOPs). However, obtaining evenly distributed weight vectors for different types of MOPs is a challenge problem especially when the true Pareto fronts (PFs) are unknown before a MOEA/D starts. In this paper, a new MOEA/D with a fast hypervolume archive (called FV-MOEA/D) is proposed to adaptively adjust the weight vectors for various shapes of PFs. The core idea of FV-MOEA/D is to periodically adjust weight vectors based on solutions in the proposed archive, in which convergence and diversity are maintained by maximizing hypervolume. Experimental studies on 58 benchmark MOPs in jMetal demonstrate that the proposed FV-MOEA/D not only reached higher hypervolumes when compare to five classical MOEAs i.e., NSGAII, SPEA2, IBEA, FV-MOEA and MOEA/D, but also obtained well distributed weight vectors on PFs with different geometrical characteristics.
AB - The decomposition method in multiobjective evolutionary algorithms (MOEA/D) is an effective approach to evolve solutions along predefined weight vectors for solving multiobjective optimization problems (MOPs). However, obtaining evenly distributed weight vectors for different types of MOPs is a challenge problem especially when the true Pareto fronts (PFs) are unknown before a MOEA/D starts. In this paper, a new MOEA/D with a fast hypervolume archive (called FV-MOEA/D) is proposed to adaptively adjust the weight vectors for various shapes of PFs. The core idea of FV-MOEA/D is to periodically adjust weight vectors based on solutions in the proposed archive, in which convergence and diversity are maintained by maximizing hypervolume. Experimental studies on 58 benchmark MOPs in jMetal demonstrate that the proposed FV-MOEA/D not only reached higher hypervolumes when compare to five classical MOEAs i.e., NSGAII, SPEA2, IBEA, FV-MOEA and MOEA/D, but also obtained well distributed weight vectors on PFs with different geometrical characteristics.
UR - https://www.scopus.com/pages/publications/85008248288
U2 - 10.1109/CEC.2016.7743835
DO - 10.1109/CEC.2016.7743835
M3 - 会议稿件
AN - SCOPUS:85008248288
T3 - 2016 IEEE Congress on Evolutionary Computation, CEC 2016
SP - 500
EP - 507
BT - 2016 IEEE Congress on Evolutionary Computation, CEC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Part of 2016 IEEE World Congress on Computational Intelligence, WCCI 2016
Y2 - 24 July 2016 through 29 July 2016
ER -