ESTECO modeFRONTIER 2020 R3 x64

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    ESTECO modeFRONTIER 2020 R3 x64

    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;
    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.

    File Size: 939.2 MB

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