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Scalable distributed evolutionary algorithm orchestration using Docker containers

机译:使用Docker容器的可扩展分布式进化算法编排

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In smart factories, integrated optimisation of manufacturing process planning and scheduling leads to better results than a traditional sequential approach but is computationally more expensive and thus difficult to be applied to real-world manufacturing scenarios. In this paper, a working approach for cloud-based distributed optimisation for process planning and scheduling is presented. Three managers dynamically governing the creation and deletion of subpopulations (islands) evolved by a multi-objective genetic algorithm are proposed, compared and contrasted. A number of test cases based on two real-world manufacturing scenarios are used to show the applicability of the proposed solution. (C) 2019 Elsevier B.V. All rights reserved.
机译:在智能工厂中,与传统的顺序方法相比,对制造过程计划和调度的集成优化可带来更好的结果,但在计算上更为昂贵,因此难以应用于现实世界的制造场景。本文提出了一种基于云的分布式优化的工作计划和调度工作方法。提出,比较和对比了三个由多目标遗传算法动态管理亚种群(岛)的创建和删除的管理者。基于两个实际制造场景的大量测试用例用于说明所提出解决方案的适用性。 (C)2019 Elsevier B.V.保留所有权利。

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