A novel sequential approximate optimization approach using data mining for engineering design optimization

For most engineering design optimization problems, it is hard or even impossible to find the global optimum owing to the unaffordable computational cost. To overcome this difficulty, a sequential approximate optimization (SAO) approach that integrates the hybrid optimization algorithm with data mining and surrogate models is proposed to find the global optimum in engineering design optimization. The surrogate model is used to replace expensive simulation analysis and the data mining is applied to obtain the reduced search space. Thus, the efficiency of finding and quality of the global optimum will be increased by reducing the search space using data mining. The validity and efficiency of the proposed SAO approach are examined by studying typical numerical examples.

[1]  S. Chandel,et al.  Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models , 2014 .

[2]  Hasan Kurtaran,et al.  Crashworthiness design optimization using successive response surface approximations , 2002 .

[3]  Andy J. Keane,et al.  On the Design of Optimization Strategies Based on Global Response Surface Approximation Models , 2005, J. Glob. Optim..

[4]  Zuomin Dong,et al.  Approximated Unimodal Region Elimination Based Global Optimization Method for Engineering Design , 2007, DAC 2007.

[5]  J. I. Nanavati,et al.  Optimisation of machining parameters for turning operations based on response surface methodology , 2013 .

[6]  Eric R. Ziegel,et al.  Mastering Data Mining , 2001, Technometrics.

[7]  R. Regis.  Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points , 2014 .

[8]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[9]  Zuomin Dong,et al.  Hybrid and adaptive meta-model-based global optimization , 2012 .

[10]  K. Yamazaki,et al.  Sequential Approximate Optimization using Radial Basis Function network for engineering optimization , 2011 .

[11]  Sonja Kuhnt,et al.  Design and analysis of computer experiments , 2010 .

[12]  T. Simpson,et al.  Comparative studies of metamodeling techniques under multiple modeling criteria , 2000 .

[13]  T. Simpson,et al.  Comparative studies of metamodelling techniques under multiple modelling criteria , 2001 .

[14]  Jürgen Branke,et al.  Faster convergence by means of fitness estimation , 2005, Soft Comput..

[15]  Thiagarajan Krishnamurthy,et al.  Response Surface Approximation with Augmented and Compactly Supported Radial Basis Functions , 2003 .

[16]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[17]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[18]  Andrew D. Back,et al.  Radial Basis Functions , 2001 .

[19]  Ren-Jye Yang,et al.  Selection of initial designs for multi-objective optimization using classification and regression tree , 2013 .

[20]  Ernesto P. Adorio,et al.  MVF - Multivariate Test Functions Library in C for Unconstrained Global Optimization , 2005 .

[21]  Donald R. Jones,et al.  A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..

[22]  W. Light,et al.  Wavelets, subdivision algorithms, and radial basis functions , 1992 .

[23]  Y. Y. Huang,et al.  A surrogate-based optimization method with RBF neural network enhanced by linear interpolation and hybrid infill strategy , 2014, Optim. Methods Softw..

[24]  Tapabrata Ray,et al.  ENGINEERING DESIGN OPTIMIZATION USING A SWARM WITH AN INTELLIGENT INFORMATION SHARING AMONG INDIVIDUALS , 2001 .

[25]  G. G. Wang,et al.  Mode-pursuing sampling method for global optimization on expensive black-box functions , 2004 .

[26]  T. Y. Chen,et al.  Application of data mining in a global optimization algorithm , 2013, Adv. Eng. Softw..

[27]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[28]  Ting-Yu Chen,et al.  An efficient and practical approach to obtain a better optimum solution for structural optimization , 2013 .

[29]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[30]  T. Simpson,et al.  Use of Kriging Models to Approximate Deterministic Computer Models , 2005 .

[31]  Farrokh Mistree,et al.  Statistical Approximations for Multidisciplinary Design Optimization: The Problem of Size , 1999 .

[32]  Zongmin Wu,et al.  Compactly supported positive definite radial functions , 1995 .

[33]  Ian Witten,et al.  Data Mining , 2000 .

[34]  Ting-Yu Chen,et al.  Data-mining assisted structural optimization using the evolutionary algorithm and neural network , 2010 .

[35]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[36]  Hans-Martin Gutmann,et al.  A Radial Basis Function Method for Global Optimization , 2001, J. Glob. Optim..

[37]  Najdan Vukovic,et al.  A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation , 2013, Neural Networks.

[38]  C. Micchelli.  Interpolation of scattered data: Distance matrices and conditionally positive definite functions , 1986 .

[39]  Nikolaos V. Sahinidis,et al.  Derivative-free optimization: a review of algorithms and comparison of software implementations , 2013, J. Glob. Optim..

[40]  Tron Eid,et al.  Applying simulated annealing using different methods for the neighborhood search in forest planning problems , 2014, Eur. J. Oper. Res..

[41]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[42]  Shigeru Obayashi,et al.  Efficient global optimization (EGO) for multi-objective problem and data mining , 2005, 2005 IEEE Congress on Evolutionary Computation.

[43]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007, DAC 2006.

[44]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[45]  Tom Dhaene,et al.  Surrogate modeling of microwave structures using kriging, co‐kriging, and space mapping , 2013 .

[46]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

[47]  Sundaram Suresh,et al.  Sequential Projection-Based Metacognitive Learning in a Radial Basis Function Network for Classification Problems , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[48]  Karl Rihaczek,et al.  1. WHAT IS DATA MINING? , 2019, Data Mining for the Social Sciences.

[49]  Zuomin Dong,et al.  Approximated unimodal region elimination-based global optimisation method for engineering design , 2009 .

[50]  R. L. Hardy.  Multiquadric equations of topography and other irregular surfaces , 1971 .