Adaptive PBI for Massively Parallel MOEA/D in a Distributed Memory Environment
Adaptive PBI for Massively Parallel MOEA/D in a Distributed Memory Environment
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DOI：
10.1109/cec55065.2022.9870272
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Yuji Sato;Tomoya Hirayama;Ryo Ikami
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文献类型：
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作者：
Yuji Sato;Tomoya Hirayama;Ryo Ikami
本文提出了一种分布式存储环境下大规模并行MOEA/D的自适应PBI。分布式存储环境中的大规模并行化有效地加速了实际应用问题的进化多目标优化算法。另一方面，当MOEA/D通过关注目标函数空间中的参考向量来进行并行划分时，T-邻域被划分，并且在划分区域的边界附近出现解分布变得稀疏的问题。这里，我们提出了一种改进方法，通过使用拉普拉斯分布等分布函数，根据到参考向量的距离自适应地控制PBI函数中的罚值，从而改善T邻居划分和解分布变得稀疏的问题。通过与使用单CPU执行的比较，证明了该方法的有效性。
This paper proposes an adaptive PBI for massively parallel MOEA/D in a distributed memory environment. Massively parallelization in a distributed memory environment effectively speeds up evolutionary multi-objective optimization algorithms for practical application problems. On the other hand, when MOEA/D is divided for parallelization by focusing on the reference vector in the objective function space, the T-neighbor is divided and the problem that the solution distribution becomes sparse near the boundary of the divided region arises. Here, we propose a method to improve the problem that the T-neighbor is divided and the solution distribution becomes sparse by adaptively controlling the penalty value in the PBI function according to the distance from the reference vector using a distribution function such as Laplace distribution. The effectiveness of the proposed method is shown by comparison with execution using a single CPU.