.png){kind=icon}
Return
A Multistage Evolutionary Algorithm for Better Diversity Preservation in Multiobjective Optimization
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2019.2956288.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Diversity preservation is a crucial technique in multiobjective evolutionary algorithms (MOEAs), which aims at evolving the population toward the Pareto front (PF) with a uniform distribution and a good extensity. In spite of many diversity preservation approaches in existing MOEAs, most of them encounter difficulties in tackling complex PFs. This article gives a detail introduction to existing diversity preservation approaches, as well as a revelation of the limitations of them. To address the limitations of existing diversity preservation approaches, this article proposes a multistage MOEA for better diversity performance. The proposed MOEA divides the optimization process into multiple stages according to the population in each generation, and updates the population by different steady-state selection schemes in different stages. According to the experimental results on 21 benchmark problems, the proposed MOEA exhibits better diversity performance than 11 existing MOEAs.
Keywords:
Sociology
Evolutionary computation
Portfolios
Pareto optimization
Current measurement
Diversity preservation
evolutionary algorithm
multiobjective optimization
Pareto front (PF)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
Organization
Cited Papers
ShareSaveShareSaveShareSaveDiversity Improvement by Non-Geometric Binary Crossover in Evolutionary Multiobjective Optimization
PREAI
ShareSaveIncidence of Hepatitis C Infection among Prisoners by Routine Laboratory Values during a 20-Year Period.png)
PLoS ONE
OAAI
PLoS ONEIF0
OAAIShareSaveShareSaveShareSave
View more