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A Multistage Evolutionary Algorithm for Better Diversity Preservation in Multiobjective Optimization
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DOI：10.1109/TSMC.2019.2956288.png)
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摘要
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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.
Keyword:
Sociology
Evolutionary computation
Portfolios
Pareto optimization
Current measurement
Diversity preservation
evolutionary algorithm
multiobjective optimization
Pareto front (PF)
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IF:
10.5
论文数:
1.1W
被引数:
5.0W
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