Paper Info
Title
MOEA/D with a delaunay triangulation based weight adjustment
Abstract
MOEA/D decomposes a multi-objective optimization problem (MOP) into a set of scalar sub-problems with evenly spread weight vectors. Recent studies have shown that the fixed weight vectors used in MOEA/D might not be able to cover the whole Pareto front (PF) very well. Due to this, we developed an adaptive weight adjustment method in our previous work by removing subproblems from the crowded parts of the PF and adding new ones into the sparse parts. Although it performs well, we found that the sparse measurement of a subproblem which is determined by the m-nearest (m is the dimensional of the object space) neighbors of its solution can be more appropriately defined. In this work, the neighborhood relationship between subproblems is defined by using Delaunay triangulation (DT) of the points in the population.
Year
DOI
Venue
2014
10.1145/2598394.2598416
GECCO (Companion)
Keywords
Field
DocType
weight adjustment,delaunay triangulation,evolutionary multi-objective optimization,general,decomposition
Population,Mathematical optimization,Bowyer–Watson algorithm,Computer science,Scalar (physics),Multi-objective optimization,Optimization problem,Delaunay triangulation
Conference
Citations 
PageRank 
References 
0
0.34
2
Authors
5
Name
Order
Citations
PageRank
Yutao Qi11458.90
Xiaoliang Ma218218.51
Minglei Yin320.70
Fang Liu41188125.46
Jingxuan Wei5628.42