Paper Info
Title
A comparative study on decomposition-based multi-objective evolutionary algorithms for many-objective optimization.
Abstract
Many-objective optimization problems pose challenges to the Pareto-based multi-objective optimization algorithms. Recent studies have suggested that decomposition is a promising method to improve the performance of multi-objective evolutionary algorithms on many-objective optimization problem. Various methods based on decomposition have been developed to solve many-objective problems in recent years. However, the existing experimental comparative studies are usually limited to only a few methods based on decomposition. This paper offers a systematic comparison of seven representative decomposition-based approaches tested on two groups of widely used problems. The experimental results have demonstrated that none of the compared algorithms has a clear advantage over the others, although different algorithms are competitive on different test problems. Therefore, a careful selection of algorithms is necessary in handling a many-objective problem in hand.
Year
DOI
Venue
2016
10.1109/CEC.2016.7744096
CEC
Keywords
Field
DocType
MOEA/D,HYPERVOLUME,PERFORMANCE,OPTIMALITY
Mathematical optimization,Evolutionary algorithm,Computer science,L-reduction,Test functions for optimization,Evolutionary computation,Multi-objective optimization,Artificial intelligence,Imperialist competitive algorithm,Optimization problem,Machine learning,Metaheuristic
Conference
Citations 
PageRank 
References 
0
0.34
27
Authors
5
Name
Order
Citations
PageRank
Xiaoliang Ma118218.51
Junshan Yang2291.75
Nuosi Wu311.03
Zhen Ji413810.84
Zexuan Zhu598957.41