TY - GEN
T1 - On the use of random weights in MOEA/D
AU - Li, Hui
AU - Ding, Min
AU - Deng, Jingda
AU - Zhang, Qingfu
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/10
Y1 - 2015/9/10
N2 - MOEA/D is a decomposition-based multiobjective evolutionary algorithm that has attracted much attention in recent years. Its performance depends on the setting of weight vectors which are used for defining subproblems. In the case of irregular Pareto fronts (e.g, disconnected or degenerated), fixed setting of weight vectors in MOEA/D may not work well. In this paper, we propose an improved MOEA/D with both random and fixed weight vectors. Moreover, an external archive based on a modified ϵ-dominance strategy is used for storing nondominated solutions found by the proposed algorithm and assisting the generation of random weight vectors. Some experiments have been conducted to verify the efficiency and effectiveness of the improved MOEA/D on benchmark multiobjective test problems with irregular Pareto fronts. The experimental results show that the overall performance of the proposed algorithm is better than baseline MOEA/D and NSGA-II.
AB - MOEA/D is a decomposition-based multiobjective evolutionary algorithm that has attracted much attention in recent years. Its performance depends on the setting of weight vectors which are used for defining subproblems. In the case of irregular Pareto fronts (e.g, disconnected or degenerated), fixed setting of weight vectors in MOEA/D may not work well. In this paper, we propose an improved MOEA/D with both random and fixed weight vectors. Moreover, an external archive based on a modified ϵ-dominance strategy is used for storing nondominated solutions found by the proposed algorithm and assisting the generation of random weight vectors. Some experiments have been conducted to verify the efficiency and effectiveness of the improved MOEA/D on benchmark multiobjective test problems with irregular Pareto fronts. The experimental results show that the overall performance of the proposed algorithm is better than baseline MOEA/D and NSGA-II.
UR - https://www.scopus.com/pages/publications/84963532903
U2 - 10.1109/CEC.2015.7256996
DO - 10.1109/CEC.2015.7256996
M3 - 会议稿件
AN - SCOPUS:84963532903
T3 - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
SP - 978
EP - 985
BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Congress on Evolutionary Computation, CEC 2015
Y2 - 25 May 2015 through 28 May 2015
ER -