This paper investigates the integration of a surrogate-assisted multi-objective evolutionary algorithm (MOEA) and a parallel computation scheme to reduce the computing time until obtaining the optimal solutions in evolutionary algorithms (EAs). A surrogate-assisted MOEA solves multi-objective optimization problems while estimating the evaluation of solutions with a surrogate function. A surrogate function is produced by a machine learning model. This paper uses an extreme learning surrogate-assisted MOEA/D (ELMOEA/D), which utilizes one of the well-known MOEA algorithms, MOEA/D, and a machine learning technique, extreme learning machine (ELM). A parallelization of MOEA, on the other hand, evaluates solutions in parallel on multiple computing nodes to accelerate the optimization process. We consider a synchronous and an asynchronous parallel MOEA as a master-slave parallelization scheme for ELMOEA/D. We carry out an experiment with multi-objective optimization problems to compare the synchronous parallel ELMOEA/D with the asynchronous parallel ELMOEA/D. In the experiment, we simulate two settings of the evaluation time of solutions. One determines the evaluation time of solutions by the normal distribution with different variances. On the other hand, another evaluation time correlates to the objective function value. We compare the quality of solutions obtained by the parallel ELMOEA/D variants within a particular computing time. The experimental results show that the parallelization of ELMOEA/D significantly reduces the computational time. In addition, the integration of ELMOEA/D with the asynchronous parallelization scheme obtains higher quality of solutions quicker than the synchronous parallel ELMOEA/D.

本文研究了代理辅助多目标进化算法（MOEA）和并行计算方案的集成，以减少计算时间，直到获得进化算法（EA）的最优解为止。代理辅助的MOEA解决了多目标优化问题，同时估计了具有代理功能的解决方案的评估。替代功能由机器学习模型产生。本文使用了一种极限学习替代辅助的MOEA / D（ELMOEA / D），它利用了一种著名的MOEA算法MOEA / D和一种机器学习技术极限学习机（ELM）。另一方面，MOEA的并行化可在多个计算节点上并行评估解决方案，以加快优化过程。我们将同步和异步并行MOEA视为ELMOEA / D的主从并行化方案。我们进行了一个具有多目标优化问题的实验，以比较同步并行ELMOEA / D与异步并行ELMOEA / D。在实验中，我们模拟了解决方案评估时间的两个设置。一个通过具有不同方差的正态分布确定解的评估时间。另一方面，另一个评估时间与目标函数值相关。我们比较了在特定计算时间内并行ELMOEA / D变体获得的解决方案的质量。实验结果表明，ELMOEA / D的并行化显着减少了计算时间。此外，