ESTECO modeFRONTIER 是一个多目标优化、设计仿真过程自动化的集成平台。在不同的学科中为分析设计提供无缝集成与工程工具。modeFRONTIER优化平台广泛应用于工业领域。从航空航天到材料科学，从汽车到化学品，以及大学特点的专题研究中心（设计实验和统计工具的设计元素之间的相互作用）等。使用ESTECO modeFRONTIER 即使是再复杂的算法和再大的工作量也能以灵活和动态的方式解决多目标和多学科的问题。
主要特点：计算过程可以中断连续，具有并行、分布式计算功能；与众多CAE软件都有接口，方便集成优化；可与多种语言连接使用 (FORTRAN、C、Java)；独一无二的稳健优化设计功能，保证产品质量；丰富的算法库。算法精良，速度快等。
File Size: 996.2 MB
modeFRONTIER is an environment for solving problems of criteria-based and multi-criteria optimization, working with various CAD, CAE, CFD and other software systems. The environment has the ability to work in automatic design and optimization of products. Implemented data processing and analysis using various methods
Main technical characteristics:
Design of an experiment (DOE), distribution of the input population of variables, estimation of forecast accuracy
User DOE; Random; Sobol; Full factorial; Cubic-face-centered; Taguchi; Box-Benken; Montecarlo; Reduced Factorial; Latin Square; Latin Hypercube;
D-Optimal; Cross validation method; Constraint satisfaction problem
Decision making for multi-criteria optimization (MCDM):
Hurwicz criterion;
Linear algorithm;
GA algoriphm;
Minimax, savage mimimax regret criterion;
Algorithms, optimization methods:
DOE Sequence – direct enumeration of parameters;
MOGA II – genetic algorithm for multi-criteria optimization;
ARMOGA – genetic algorithm based on MOGA;
NSGA II – genetic algorithm for non-dominated sorting for multicriteria optimization;
NASH – an algorithm based on the Nash theory of non-cooperative games for multicriteria optimization;
B-BFGS – gradient algorithm;
SIMPLEX – search for a solution without the use of derivatives by the Nelder-Mead method;
Levenberg-Marquardt;
Simulated Annealing – model hardening algorithm (simulated annealing method);
1P1-ES – evolutionary strategy;
DES – evolutionary strategy for performing criterion optimization with continuous variables;
MMES – evolutionary strategy for multicriteria optimization with discrete and continuous variables;
FMOGA II – version of the MOGA algorithm with improved convergence;
FSIMPLEX – Simplex version with improved convergence and the ability to solve multicriteria problems;
MOSA – a version of simulated annealing with the ability to solve multicriteria problems;
MACK – an algorithm for approximating response surfaces;
NLPQLP – Sequential Quadratic Programming (SQP) algorithm;
NLPQLP-NBI – Normal Boundary Intersection method + NLPQLP (algorithm with the ability to solve multicriteria nonlinear problems);
Multi-Objective Particle Swarm.
Metamodels (response surface approximation, RSM, approximate mathematical models), construction methods:
K-Nearest (Shepard-a method);
SVD (singular value decomposition);
Kriging, a regression analysis technique based on the work of Daniel Krige;
Parametric surfaces, polynomial regression;
Gaussian Processes – an approach to solving problems of regression analysis based on the work of Bezier (Bayesian);
Artificial neural networks, radial basis function,
Meta model validation tools.
6 sigma, quality management, Design for Six Sigma (DFSS):
Sigma quality (six sigma quality);
Failure modes and analysis of their impact (discards analysis);
Ishikawa diagram.
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