Design optimization

The difference between single-objective and multi-objective optimization lies in how many goals you’re trying to optimize at once and how the platform handles trade-offs between them.

Single objective: typically, search methods are directed toward a single optimum, such as gradient‑based methods (SQP, BFGS) or metaheuristics tuned to find a single best solution (simulated annealing, single‑objective GAs).

Multi‑objective: strategies that explore and approximate a Pareto front, often using populations of solutions; classic examples are multi‑objective genetic algorithms (NSGA‑II, MOGA-II in general) or weighted sums that turn multiple objectives into a single one by varying weights/targets.

How to handle both? VOLTA integrates optimization workflows from modeFRONTIER, letting you:

• Switch between single and multi-objective modes easily within the same workflow.
• Visualize trade-offs using interactive charts and Pareto fronts.
• Compare scenarios to make data-driven design decisions collaboratively.

You can create a workflow with multiple objectives in modeFRONTIER by defining each target in the workflow editor and choosing a suitable algorithm from the optimization menu. After running the workflow, modeFRONTIER displays a Pareto front where each point represents a design with a unique trade-off between the objectives.

The best way to define constraints for design optimization is to separate what is truly non‑negotiable from what is merely desirable. Hard constraints should encode physics, safety, regulations, and absolute packaging limits: violating them would make the design infeasible, unsafe, or non‑compliant. Everything else can often be modeled more flexibly as objectives. This shift lets you explore trade‑offs instead of artificially shrinking the feasible space.

A practical workflow is to first write many candidate “constraints”, then explicitly ask for each: “Is this a must‑have or a preference?” Must‑haves stay as constraints; preferences become objectives that are minimized or maximized. In multi‑objective settings, you can even promote some traditional constraints (e.g., mass, NVH, efficiency margins) to full objectives to visualize their trade‑offs on the Pareto front and only later impose thresholds during decision making. This approach improves robustness, reduces infeasibility, and makes the design priorities much clearer.

Yes, modeFRONTIER can handle discrete and continuous variables in the same project. You can define integer, categorical and continuous design variables together and let the chosen algorithm work on a mixed encoding. In practice, discrete choices (e.g., material, gear number, topology option) are mapped to integers, while dimensions, angles or control parameters remain continuous. Evolutionary and other population‑based optimizers in modeFRONTIER are well suited for this mixed‑integer setting, and you can also build hybrid strategies where the global search explores discrete combinations while a local gradient‑based method refines the continuous variables for each configuration.

Yes, modeFRONTIER can absolutely optimize a process using Python scripts.
Python is fully integrated into the modeFRONTIER platform, making it a powerful component for both the automation of the simulation workflow and for advanced data analysis.

Two primary ways to use python in modeFRONTIER
modeFRONTIER provides dedicated tools to leverage Python for optimization and data handling:

1. Python in the workflow (process automation)
   You can use Python scripts as application nodes within the modeFRONTIER workflow, effectively making the script a part of the simulation chain that the optimizer controls.

• Custom simulation/analysis: the Python script can act as a solver for a custom model, perform data manipulation, or execute complex pre- or post-processing tasks that aren't handled by standard CAE tools.
• Driving external tools: a script can be used to launch and control third-party software that doesn't have a direct modeFRONTIER node, making it a wrapper for the external program.
• Input/output handling: In the workflow, the Python script node receives the design variables (inputs) from the modeFRONTIER scheduler, executes its logic, and then outputs the calculated values (objectives and constraints) back to the optimizer.

2. pyCONSOLE and Python APIs (advanced data & analysis)
   modeFRONTIER also offers a dedicated pyCONSOLE and Python APIs to use Python's extensive scientific and machine learning libraries directly within the platform's analytical environment.

By combining Python's flexibility with modeFRONTIER's robust optimization algorithms (like MOGA-II, NSGA-II, etc.), you gain a powerful framework for multi-objective and multidisciplinary design optimization (MDO) on processes that rely on custom code.