|Stable release||4.4.3 / March, 2013|
modeFRONTIER is a multi-objective optimization and design environment, written to couple CAD/computer aided engineering (CAE) tools, finite element structural analysis and computational fluid dynamics (CFD) software. It is developed by ESTECO SpA and provides an environment for product engineers and designers. modeFRONTIER is a GUI driven software written in Java that wraps around the CAE tool, performing the optimization by modifying the value assigned to the input variables, and analyzing the outputs as they can be defined as objectives and/or constraints of the design problem.
ESTECO was created in 1999 to transfer the knowledge acquired by its founders while working on a European Union sponsored project on Design Optimization (FRONTIER, started in 1996) into a commercial product, called modeFRONTIER. In 2001, modeFRONTIER version 2.4 become a global player among the MDO/PIDO tools, being one of the first to enable true multi-objective optimization through Pareto dominance criteria.
 Process integration
 Design optimization
modeFRONTIER includes design of experiments (DOE), optimization algorithms and robust design tools, that can be combined and blended to build up the most efficient strategy to solve complex multi-disciplinary problems.
 Design of experiments
- Different strategies are available, including random generator sequences, Factorial DOEs, Orthogonal and Iterative Techniques, as like as D-Optimal or Cross Validation. Monte Carlo and Latin hypercube are available for robustness analysis.
 Multi objective algorithms
- Among the others, different implementations of Genetic Algorithm, Game Theory, Simulated Annealing, Evolution strategies are able to manage continuous, discrete and mixed variable problems. More classical mono-objective algorithms are as well available, as like as Gradient-based methods or Simplex algorithm.
 Response surfaces
- Different Response surface methodology techniques are available to interpolate data and perform so called "virtual optimizations", particularly useful when the optimization applies to problems where every fitness function evaluation is time-expensive. Single Value Decomposition and Polynomial Responses are implemented, as well as the more sophisticated Kriging, Neural Network and Gaussian process ones.
 Data processing and multiple criteria decision making (MCDM)
- This set of tools enables the user to explore, filter and rank the set of optimal solutions of a multi-objective problem (the so-called Pareto frontier), to perform sensitivity analyses, robustness verifications and also to produce standard and customizable reports of the optimization project (RTF, PDF, HTML formats).
 Robust design optimization
This is the latest step of MDO towards 6 Sigma: optimizing a design taking into account uncertainties and tolerances. RSM techniques can be used to overcome the increase in time-expense due to this extensive statistic approach.