论文信息 - A Modified MOEAD with an Adaptive Weight Adjustment Strategy

A Modified MOEAD with an Adaptive Weight Adjustment Strategy

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is widely used to dispose multiobjective optimization problems (MOPs). The performance of MOEA/D is not ideal when deals with MOPs which are with degenerated curve. Aiming to this weakness, an adaptive weight adjustment strategy named BPO is proposed in this paper. In order to solve this problem, BPO strategy is based on the number of reference points referencing the archive. The results of tests show that MOEA/D with BPO strategy can obtain ideal performance. MOEA/D with BPO strategy can improves the weakness of MOEA/D effectively on MOPs which are with degenerated curve.

[1] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[2] Eckart Zitzler,et al. Indicator-Based Selection in Multiobjective Search , 2004, PPSN.

[3] Qingfu Zhang,et al. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[4] Jie Zhang,et al. Multiobjective optimization by decomposition with Pareto-adaptive weight vectors , 2011, ICNC.

[5] Fang Liu,et al. MOEA/D with Adaptive Weight Adjustment , 2014, Evolutionary Computation.

[6] Roman Neruda,et al. Incorporating User Preferences in MOEA/D through the Coevolution of Weights , 2015, GECCO.

[7] Y. Zhang,et al. Enhancing MOEA/D with uniform population initialization, weight vector design and adjustment using uniform design , 2015 .

[8] Ye Tian,et al. A multi-objective evolutionary algorithm based on an enhanced inverted generational distance metric , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[9] Fang Liu,et al. MOEA/D with biased weight adjustment inspired by user preference and its application on multi-objective reservoir flood control problem , 2016, Soft Comput..