Initial selection of configurations based on information entropy for multi-level design optimization

Abstract

An efficient multi-level design optimization method is developed in this research based on information entropy to select the configurations with high potential to achieve the optimal design solution. The generic design is modeled by an AND-OR tree based on design requirements. A node in the AND-OR tree is used to describe partial design solution such as a component or an assembly with design parameters. The different design configurations are created from the AND-OR tree through tree-based search, and each design configuration is modeled by design parameters. Optimization is conducted at two levels. Parameter optimization is carried out to identify the optimal parameter values for each design configuration, while configuration optimization is carried out to identify the optimal configuration. Information entropy is employed to evaluate the partial configuration candidates modeled as branches in the AND-OR tree to eliminate the branches that are unlikely to lead to the optimal solution.