Analysis of semi-asynchronous multi-objective evolutionary algorithm with different asynchronies
Analysis of semi-asynchronous multi-objective evolutionary algorithm with different asynchronies
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DOI：
10.1007/s00500-019-04071-7
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发表时间：
2019-05
期刊：
影响因子：
4.1
通讯作者：
Tomohiro Harada;K. Takadama
中科院分区：
文献类型：
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作者：
Tomohiro Harada;K. Takadama
提出了一种新的主从式并行进化算法，并对该算法在多目标优化问题中的应用进行了详细的分析。我们表示所提出的EA与不同的语义作为半-语义EA。一个半异步EA生成新的解决方案，每当预定义数量的解决方案完成评估，不像传统的同步EA等待所有解决方案的评估，以生成下一个人口。为了建立一个半异步的EA，本文引入了一个参数，用来决定有多少解决方案是等待产生新的解决方案。我们进行了一个实验，以验证所提出的半异步EA对基准问题的评估时间的几个变量的有效性。在实验中，我们将半异步EA应用于NSGA-II和NSGA-III，这是众所周知的多目标EA。在多目标优化基准问题上，比较了半异步NSGA-IIs和具有不同优先级的半异步NSGA-IIs。实验结果表明，半异步的方法与适当的时间有可能优于异步和同步的。此外，详细的分析表明，一个适当的搜索结果可能会有所不同，不仅取决于目标问题，而且取决于进化过程的程度。
This paper proposes a novel master–slave parallel evolutionary algorithm (EA) approach with different asynchrony and provides its detailed analyses on multi-objective optimization problems. We express the proposed EA with different asynchrony as asemi-asynchronousEA. A semi-asynchronous EA generates new solutions whenever evaluations of the predefined number of solutions complete, unlike a conventional synchronous EA waits for evaluations of all solutions to generate the next population. To establish a semi-asynchronous EA, this paper introduces an asynchrony parameter that is used to decide how many solutions are waited to generate new solutions. We conduct an experiment to verify the effectiveness of the proposed semi-asynchronous EA on benchmark problems with the several variances of the evaluation time. In the experiment, we apply a semi-asynchronous EA to NSGA-II and NSGA-III, which are well-known multi-objective EAs. The semi-asynchronous NSGA-IIs and the semi-asynchronous NSGA-IIIs with different asynchronies are compared on multi-objective optimization benchmark problems. The experimental result reveals that semi-asynchronous approaches with an appropriate asynchrony have possibility to outperform the asynchronous and the synchronous ones. Additionally, detailed analysis reveals that an appropriate asynchrony may vary not only depends on a target problem but also depends on the degree of the evolution process.