Paper Info
Title
MOEA/HD: A Multiobjective Evolutionary Algorithm Based on Hierarchical Decomposition.
Abstract
Recently, numerous multiobjective evolutionary algorithms (MOEAs) have been proposed to solve the multiobjective optimization problems (MOPs). One of the most widely studied MOEAs is that based on decomposition (MOEA/D), which decomposes an MOP into a series of scalar optimization subproblems, via a set of uniformly distributed weight vectors. MOEA/D shows excellent performance on most mild MOPs, but may face difficulties on ill MOPs, with complex Pareto fronts, which are pointed, long tailed, disconnected, or degenerate. That is because the weight vectors used in decomposition are all preset and invariant. To overcome it, a new MOEA based on hierarchical decomposition (MOEA/HD) is proposed in this paper. In MOEA/HD, subproblems are layered into different hierarchies, and the search directions of lower-hierarchy subproblems are adaptively adjusted, according to the higher-hierarchy search results. In the experiments, MOEA/HD is compared with four state-of-the-art MOEAs, in terms of two widely used performance metrics. According to the empirical results, MOEA/HD shows promising performance on all the test problems.
Year
DOI
Venue
2019
10.1109/TCYB.2017.2779450
IEEE transactions on cybernetics
Keywords
Field
DocType
Sociology,Statistics,Shape,Evolutionary computation,Optimization,Sorting,Companies
Mathematical optimization,Evolutionary algorithm,Scalar (physics),Evolutionary computation,Sorting,Invariant (mathematics),Multiobjective optimization problem,Pareto principle,Mathematics
Journal
Volume
Issue
ISSN
49
2
2168-2275
Citations 
PageRank 
References 
11
0.43
33
Authors
4
Name
Order
Citations
PageRank
Hang Xu1377.53
Wenhua Zeng213614.83
Defu Zhang365752.80
Xiangxiang Zeng458950.79