Collaborative multi-objective optimization for distributed design of complex products
Collaborative multi-objective optimization for distributed design of complex products
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DOI：
10.1145/3205455.3205579
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
João A. Duro;Yiming Yan;R. Purshouse;P. Fleming
中科院分区：
文献类型：
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作者：
João A. Duro;Yiming Yan;R. Purshouse;P. Fleming
具有相互竞争的目标、涉及多个相互作用的组件的多学科设计优化问题可以称为复杂系统。如今，通常将复杂系统的优化问题划分为较小的子系统，每个子系统都有一个子问题，部分原因是一次性处理所有问题太困难了。这种方法适用于每个子系统都可以拥有自己的（专业）设计团队的大型组织。然而，这需要一个促进协作和决策的设计过程，在这样的环境中，团队可以交换有关自己设计的有限信息，并且设计团队以不同的速度工作，有不同的时间表，并且通常不在同一地点。描述了解决这些特征的多目标优化方法。子系统在点对点的基础上交换有关其自身最优解决方案的信息，并且该方法能够收敛到一组满足整个系统的最优解决方案。这在一个示例问题中得到了证明，其中该方法显示出与理想但“不切实际”的方法一样好，该方法一次性处理所有优化问题。
Multidisciplinary design optimization problems with competing objectives that involve several interacting components can be called complex systems. Nowadays, it is common to partition the optimization problem of a complex system into smaller subsystems, each with a subproblem, in part because it is too difficult to deal with the problem all-at-once. Such an approach is suitable for large organisations where each subsystem can have its own (specialised) design team. However, this requires a design process that facilitates collaboration, and decision making, in an environment where teams may exchange limited information about their own designs, and also where the design teams work at different rates, have different time schedules, and are normally not co-located. A multi-objective optimization methodology to address these features is described. Subsystems exchange information about their own optimal solutions on a peer-to-peer basis, and the methodology enables convergence to a set of optimal solutions that satisfy the overall system. This is demonstrated on an example problem where the methodology is shown to perform as well as the ideal, but "unrealistic" approach, that treats the optimization problem all-at-once.