Niching Surrogate-Assisted Optimization for Simulation-Based Design Optimization of Cylinder Head Water Jacket

Many engineering design problems are associated with computationally expensive simulations for design evaluation, which makes the optimization process a time-consuming effort. In such problems, each candidate design should be selected carefully, even though it means extra algorithmic complexity. This study develops a niching-based surrogateassisted evolutionary algorithm that aims at handling both single-objective and multi-objective computationally expensive problems. A trust-region concept in the optimization context is proposed to control the evaluation error. At the same time, maximizing the information about specific regions of the search space is pursued by proper selection of new candidate solutions. The proposed method is evaluated and compared to a recently developed surrogate-assisted evolutionary algorithm on multi-objective test problems. Thereafter, a case study involving a multi-objective design optimization of the cylinder head water jacket of a vehicle engine is presented and discussed.

[1]  Juliane Müller,et al.  SOCEMO: Surrogate Optimization of Computationally Expensive Multiobjective Problems , 2017, INFORMS J. Comput..

[2]  Kalyanmoy Deb,et al.  Classifying Metamodeling Methods for Evolutionary Multi-objective Optimization: First Results , 2017, EMO.

[3]  Kalyanmoy Deb,et al.  A Generative Kriging Surrogate Model for Constrained and Unconstrained Multi-objective Optimization , 2016, GECCO.

[4]  Juliane Müller.  MISO: mixed-integer surrogate optimization framework , 2016 .

[5]  Taimoor Akhtar,et al.  Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection , 2016, J. Glob. Optim..

[6]  Kalyanmoy Deb,et al.  U-NSGA-III: A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives: Proof-of-Principle Results , 2015, EMO.

[7]  Jinglai Wu,et al.  Modeling and Multi-Objective Optimization of Double Suction Centrifugal Pump Based on Kriging Meta-models , 2015 .

[8]  Saúl Zapotecas Martínez,et al.  MOEA/D assisted by rbf networks for expensive multi-objective optimization problems , 2013, GECCO '13.

[9]  P. Bouillard,et al.  Hierarchical stochastic metamodels based on moving least squares and polynomial chaos expansion , 2011 .

[10]  ChengXiang Zhai,et al.  Exploration-exploitation tradeoff in interactive relevance feedback , 2010, CIKM '10.

[11]  Saúl Zapotecas Martínez,et al.  A multi-objective meta-model assisted memetic algorithm with non gradient-based local search , 2010, GECCO '10.

[12]  Anne Auger,et al.  Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009 , 2010, GECCO '10.

[13]  Thomas Bartz-Beielstein,et al.  Challenges for Contemporary Evolutionary Algorithms , 2010 .

[14]  Petros Koumoutsakos,et al.  A Method for Handling Uncertainty in Evolutionary Optimization With an Application to Feedback Control of Combustion , 2009, IEEE Transactions on Evolutionary Computation.

[15]  Marcel Mongeau.  Discontinuous Optimization , 2009, Encyclopedia of Optimization.

[16]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[17]  David W. Corne,et al.  Noisy Multiobjective Optimization on a Budget of 250 Evaluations , 2009, EMO.

[18]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

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

[20]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[21]  Nikolaos V. Sahinidis,et al.  Optimization under uncertainty: state-of-the-art and opportunities , 2004, Comput. Chem. Eng..

[22]  Marco Laumanns,et al.  Scalable multi-objective optimization test problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[23]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[24]  Søren Nymand Lophaven,et al.  DACE - A Matlab Kriging Toolbox , 2002 .

[25]  Lin Jiang,et al.  An evolutionary programming approach to mixed-variable optimization problems , 2000 .

[26]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

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

[28]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[29]  Ralph E. Steuer,et al.  An interactive weighted Tchebycheff procedure for multiple objective programming , 1983, Math. Program..