.png){kind=icon}
Return
A two-stage adaptive reference direction guided evolutionary algorithm with modified dominance relation for many-objective optimization
.png){kind=icon}
OA
AI
DOI：10.1016/j.swevo.2023.101272.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Traditional dominance-based multi-objective evolutionary algorithms cease to be effective as the number of objectives increases due to the non-dominated sorting mechanism. Accordingly, a novel clustering indicator founded on the penalty-based adaptive rectangular area (APA) between solution and reference direction is proposed to assist in non-dominated levels sorting to deal with this issue. However, directly predefined reference directions with uniform distribution usually cause deteriorated performance in solving multi-objective problems with irregular Pareto fronts. Thus, an adaptive adjustment method guided by the local population is implemented in this paper. At this rate, a two-stage adaptive reference point guided evolutionary algorithm with APA-based dominance relation for many-objective optimization problems (named AREA-APA) is proposed and tested for solving these multi-objective optimization problems (including constrained and unconstrained problems). The proposed algorithm is proven to achieve comparable performance on scalable benchmark problems compared with state-of-the-art algorithms.
Keywords:
Many-objective optimization
Irregular Pareto fronts APA
Dominance relation
Reference point self-adaption
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.5
Papers:
2.2K
Citations:
1.0W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveMultiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach
PREAI
ShareSave
ShareSaveShareSaveShareSave
View more