Design exploration vs. design optimization

Automating the search for solutions to engineering problems can take either of two broad approaches: design exploration or design optimization. Practitioners making technology choices need to understand which tools do one, which do the other, and which approach best fits their needs.

How exploration and optimization relate to each other, and how they differ, is the subject of an illuminating post in BYU’s Design Exploration Research Lab blog by associate professor of mechanical engineering Christopher A. Mattson:

“Design Exploration is a particular way of arriving at an optimal design solution. To be formal, design exploration is the human-driven, often computer-assisted, divergent/convergent process used to evolve and investigate multidisciplinary design space with the intent of design discovery and to inform decision making throughout the design process.

“The essential difference between design optimization and design exploration is the method for characterizing the outcome. Design optimization strategies have two distinct parts; formulate and converge. Here it is assumed that the problem can be formulated before the search and convergence begins. Design exploration strategies, on the other hand, are based on the belief that the problem formulation evolves during the process of searching and converging, thus ultimately leading to a more informed optimal solution. In this way, design exploration is both divergent and convergent.”

What does this mean for how the problem is formulated and solved?

“Design Optimization depends on a well-posed optimization problem formulation, which generally includes (i) a well-defined objective function, (ii) inequality and equality constraints, and (iii) the expression of stakeholder preference, all of which are likely to be multidisciplinary in nature. In an arguably real way, such a problem formulation predefines the optimum solution, thereby allowing the mathematical rigor of the optimization to lead to the optimum design by an iterative, computational search.

“Design Exploration, on the other hand, assumes that the optimal design is initially unknown and initially uncharacterizable. The process of design exploration discovers design conditions and little by little (often through some form of experimentation) characterizes what an optimal design looks like. Once this is known, the final solution can then be found through a convergent design optimization algorithm.”

Most CAE vendors today offer a greater or lesser degree of optimization capability as part of their mainstream product lines. Through strategic acquisitions and development, some now boast notably broad and deep offerings. At the same time, some of the best-regarded tools continue to come from smaller vendors focused exclusively on optimization.

While not as many companies offer design exploration software, top-tier products are available from several major CAE vendors as well as a healthy number of providers dedicated to this space. Engineering organizations contemplating a purchase should weigh their functional requirements against considerations of existing technology investments and vendor relationships.

Below are software products we’ve identified in each of these domains and classified according to our understanding of their primary function. It bears emphasizing that varying degrees of optimization capability are provided by most design exploration tool suites, even though we have not repeated the names of those products in the design optimization category following.

DESIGN EXPLORATION

DESIGN OPTIMIZATION

In future articles we’ll look at the role in design exploration and optimization of systems modeling technology from Comet Solutions, Maplesoft, The MathWorks, the Modelica Association, Modelon, the NPSS Consortium, OMG SysML, OpenModelica and Vitech, and 0D/1D simulation tools from AVL, Ricardo and others.

TECHNOLOGY BUSINESS STRATEGY FOR 21ST-CENTURY ENGINEERING PRACTICE