1. A Multistage Evolutionary Algorithm for Better Diversity Preservation in Multiobjective Optimization.
- Author
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Tian, Ye, He, Cheng, Cheng, Ran, and Zhang, Xingyi
- Subjects
BENCHMARK problems (Computer science) ,DISTRIBUTION (Probability theory) ,EVOLUTIONARY computation - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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