Polymorphic optimization

The vast majority of engineering optimization literature focuses on one of two main aspects of the optimization process: algorithmic development and formulation development. Although researchers have recognized the interplay among different algorithms through mechanisms like hybrid algorithms, blackboard systems, and most recently agent-based optimization systems, relatively little attention focuses on the relationships among problem formulations. In this paper, we present a new view of the optimization process that distinguishes between model development and formulation development. Recognizing that each potential formulation represents a different form of the underlying model and that the model serves as a point of commonality among all formulations allows us to develop a framework wherein multiple formulations can coexist and seamlessly share problem and solution information. We term such an approach Polymorphic Optimization and present a sample implementation as part of a multi-agent optimization system. We illustrate this approach through the multi-objective optimization of microfluidic separation systems.