MOEA/D with Adaptive Weight Adjustment

625 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility (2018, methodology): “In MOEA/D-AWA [41], an archive is used to provide a guidance of adding and removing reference points for better population diversity.”
A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization (2017, methodology): “From this point of view, VaEA shares a similar idea as in some decomposition algorithms [43]–[45], where the weight vectors are adaptively adjusted according to the distribution of the current population.”
Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, background): “1) Qi et al. [45] proposed an improved MOEA/D with the adaptive weight vector adjustment (MOEA/D-AWA), which mainly enhances MOEA/D in two aspects.”
A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition (2017, methodology): “[24] argued that uniformly distributed weight vec-”
A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization (2018, methodology): “ment [32], external archive guided MOEA/D [33], MOEA/D with a distance-based updating [34], and online diversity metric based MOEA/D [35].”