Abstract
This paper describes HIDER, a methodology that enables detailed simulation models to be used during the early stages of system design. HIDER uses a machine learning approach to form abstract models from the detailed models. The abstract models are used for multiple-objective optimization to obtain sets of non-dominated designs. The tradeoffs between design and performance attributes in the non-dominated sets are used to interactively refine the design space. A prototype design tool has been developed to assist the designer in easily forming abstract models, flexibly defining optimization problems, and interactively exploring and refining the design space. To demonstrate the practical applicability of this approach, the paper presents results from the application of HIDER to the system-level design of a wheel loader. In this demonstration, complex simulation models for cycle time evaluation and stability analysis are used together for early-stage exploration of design space.
Optimization of Complex Simulation Models with Stochastic Gradient Methods
