A Distributed Agent-based Approach for Robust Optimization
A Distributed Agent-based Approach for Robust Optimization
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发表时间：
2010
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通讯作者：
V. V. Nguyen-V.;D. Hartmann;M. Baitsch;M. König
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作者：
V. V. Nguyen-V.;D. Hartmann;M. Baitsch;M. König
工程中的结构设计和优化解决了越来越多的非标准优化问题 (NSP)。这些问题的特点是优化空间的复杂拓扑条件（非线性、多模态、不连续性等）。因此，NSP只能通过计算机模拟的方式来解决。因此，所应用的相应数值方法通常往往是有噪声的。 NSP 的示例出现在鲁棒优化中，其中解决方案必须在以下方面具有鲁棒性：实施错误、生产公差、不确定的环境条件等。然而，始终同样有效地解决此类问题类型的普遍适用的策略是不可用的。为了改善这种情况，本文引入了一种基于分布式代理的优化方法来求解 NSP。详细阐述的方法由一个合作但也有竞争的策略代理网络组成，该网络包含使用不同搜索特征的各种优化方法（例如 SQP、DE、ES、PSO 等）。特别是，策略代理包含一个专家系统，通过高度抽象级别的规则和事实，对优化环境中的特定行为进行建模。为了有效管理使用 MAS 的 NSP 的复杂性，开发了一个模拟和实验平台。作为计算指导工具，它应用 MAS 技术并访问各种优化方法的网络。因此，可以进行优雅的交互式转向、量身定制的建模和结构优化过程的强大可视化。为了证明所提出方法的深远适用性，讨论了数值示例，包括函数和鲁棒优化问题。
Structural design and optimization in engineering address increasingly non-standard optimization problems (NSP). These problems are characterized by complex topology conditions of the optimization space (w.r.t. nonlinearity, multimodality, discontinuity etc.). By that, NSP can only be solved by means of computer simulations. Hereby, the corresponding numerical approaches applied often tend to be noisy. Examples for NSP occur in robust optimization, where the solution has to be robust with respect to e.g. implementation errors, production tolerances, uncertain environment conditions etc. However, a generally applicable strategy for solving such problem types always equally efficient is not available. To improve the situation, in this paper a distributed agent-based optimization approach for solving NSPs is introduced. The approach elaborated consists of a network of cooperating but also competing strategy agents that wrap various optimization methods (e.g. SQP, DE, ES, PSO etc.) using different search characteristics. In particular, the strategy agents contain an expert system modeling their specific behavior in an optimization environment by means of rules and facts on a highly abstract level. For managing the complexity of NSPs using MAS efficiently, a simulation and experimentation platform has been developed. Serving as a computational steering tool, it applies MAS technology and accesses a network of various optimization methods. As a consequence, an elegant interactive steering, a tailor-made modeling and a powerful visualization of structural optimization processes can be carried out. To demonstrate the far reaching applicability of the proposed approach, numerical examples are discussed, including function and robust optimization problems.