Paper Info
Title
Multi-objective evolutionary optimization based on online perceiving Pareto front characteristics
Abstract
When solving multi-objective optimization problems with complex Pareto fronts characteristics, previous work generally ignores the information related to Pareto fronts provided by the population during the evolution, which is detrimental to efficiently tackle them. In order to taking full advantage of information associated with a population evolution, a multi-objective evolutionary optimization method based on online perceiving Pareto front characteristics is proposed in this study. To this end, the information associated with the Pareto front of an optimization problem is first extracted from the population. Following that, the characteristics of the Pareto front in concavity/convexity and continuity are perceived online. For the purpose of each sub-front containing only one characteristic, a Pareto front is divided based on the concavity/convexity and continuity. According to the characteristic of each sub-front, different reference points are selected to refine the distribution of reference vectors. Finally, a multi-objective evolutionary algorithm is designed targeting the characteristics of the Pareto front. The performance of the proposed method is evaluated by comparing it with 8 state-of-the-art optimizers on 31 test problems. Further, the experimental results demonstrate that the proposed method is competitive in handling multi-objective optimization problems with irregular Pareto fronts.
Year
DOI
Venue
2021
10.1016/j.ins.2021.10.007
Information Sciences
Keywords
DocType
Volume
Multi-objective evolutionary optimization,Irregular Pareto fronts,Perception,Continuity,Convexity/concavity
Journal
581
ISSN
Citations 
PageRank 
0020-0255
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Wenqing Feng100.34
Dun-Wei Gong289165.38
Zekuan Yu302.70