ABSTRACT

This article presents a method for multiobjective optimization of a complex system, modelling it as a collection of components and resource flows between them. Constraints can be imposed on a component basis or system-wide, based on the resource flows. Optimization is performed by a genetic algorithm utilizing a variable-length genome. This specialized genome enables a more open-ended design capability than previous similar frameworks. Systems are evaluated through a user-defined simulation, and results can be presented in any trade space of interest based on the performance in the simulation. The framework is then applied to the design of a table as a simple proof of concept. In this problem, the framework was found to identify a design within 4% of the theoretical optimum 80% of the time, and within 8% of the theoretical optimum the remaining 20% of the time.

摘要

本文介绍了一种复杂系统的多目标优化方法，将其建模为组件和它们之间的资源流的集合。可以基于资源流在组件基础上或系统范围内施加约束。优化是通过使用可变长度基因组的遗传算法进行的。这种专门的基因组比以前的类似框架具有更开放的设计能力。系统通过用户定义的模拟进行评估，结果可以根据模拟中的性能在任何感兴趣的交易空间中呈现。然后将该框架应用于表格的设计，作为简单的概念证明。在这个问题中，发现该框架在 80% 的时间内确定了理论最佳值的 4% 以内的设计，