Improved particle swarm optimization method directed by indirect surrogate modeling

An improved particle swarm optimization algorithm is proposed and tested for two different test cases: surface fitting of a wing shape and an inverse design of an airfoil in subsonic flow. The new algorithm emphasizes the use of an indirect design prediction based on a local surrogate modeling as a part of update equations in particle swarm optimization algorithm structure. For all the demonstration problems considered herein, remarkable reductions in the computational times have been accomplished.ÖzetBu çalışma kapsamında yeni bir Parçacık Sürü Optimizasyon (PSO) algoritması geliştirilmiş ve teklif edilen algoritma iki farklı test probleminde denenmiştir. Söz konusu test problemleri kanat yüzeyi modelleme ve sesaltı akış şartlarında kanat profilinin tersten tasarımıdır. Teklif edilen yeni algoritma dolaylı vekil model kullanımına dayalı olarak öngörülen aday çözümün PSO algoritmalarındaki temel güncelleme denklemlerine ilave edilmesini öngörmektedir. Çalışma dahilinde dikkate alınan problemlerin tümünde teklif edilen algoritmanın kayda değer hesaplama süresi azaltımları sağladığı görülmüştür.

[1]  Antony Jameson,et al.  Essential Elements of Computational Algorithms for Aerodynamic Analysis and Design , 1997 .

[2]  Raphael T. Haftka,et al.  Surrogate-based Analysis and Optimization , 2005 .

[3]  Pierre Sagaut,et al.  A surrogate-model based multidisciplinary shape optimization method with application to a 2D subsonic airfoil , 2007 .

[4]  Andy J. Keane,et al.  Statistical Improvement Criteria for Use in Multiobjective Design Optimization , 2006 .

[5]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[6]  Gerald Farin,et al.  The Bernstein Form of a Bézier Curve , 1993 .

[7]  Angel Cobo,et al.  Particle Swarm Optimization for Bézier Surface Reconstruction , 2008, ICCS.

[8]  Xiaodong Li,et al.  Swarm heuristic for identifying preferred solutions in surrogate-based multi-objective engineering design , 2011 .

[9]  Xiaodong Li,et al.  Integrating User-Preference Swarm Algorithm and Surrogate Modeling for Airfoil Design , 2011 .

[10]  Ramana V. Grandhi,et al.  Mixed-Variable Optimization Strategy Employing Multifidelity Simulation and Surrogate Models , 2010 .

[11]  Wei Chen,et al.  Multiresponse and Multistage Metamodeling Approach for Design Optimization , 2009 .

[12]  Florent Duchaine,et al.  Computational-Fluid-Dynamics-Based Kriging Optimization Tool for Aeronautical Combustion Chambers , 2009 .

[13]  Yasin Volkan Pehlivanoglu,et al.  Hybrid Intelligent Optimization Methods for Engineering Problems , 2010 .

[14]  Régis Duvigneau,et al.  Low cost PSO using metamodels and inexact pre-evaluation: Application to aerodynamic shape design , 2009 .

[15]  Armando Vavalle,et al.  Iterative Response Surface Based Optimization Scheme for Transonic Airfoil Design , 2007 .

[16]  Wei Shyy,et al.  Shape optimization of supersonic turbines using global approximation methods , 2002 .

[17]  J. Anderson,et al.  Fundamentals of Aerodynamics , 1984 .

[18]  Sergey Peigin,et al.  Robust optimization of 2D airfoils driven by full Navier–Stokes computations , 2004 .

[19]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[20]  Y. Volkan Pehlivanoglu,et al.  Aerodynamic design prediction using surrogate-based modeling in genetic algorithm architecture , 2012 .

[21]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[22]  Abdurrahman Hacioglu,et al.  Fast evolutionary algorithm for airfoil design via neural network , 2007 .

[23]  Angel Cobo,et al.  Bézier Curve and Surface Fitting of 3D Point Clouds Through Genetic Algorithms, Functional Networks and Least-Squares Approximation , 2007, ICCSA.

[24]  A. Keane,et al.  Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling , 2003 .

[25]  Y. Volkan Pehlivanoglu,et al.  Improved Particle Swarm Optimization Method in Inverse Design Problems , 2013, IWANN.

[26]  Bryan Glaz,et al.  Multiple-Surrogate Approach to Helicopter Rotor Blade Vibration Reduction , 2009 .

[27]  Andy J. Keane,et al.  A new hybrid updating scheme for an evolutionary search strategy using genetic algorithms and kriging , 2005 .

[28]  Oktay Baysal,et al.  Vibrational genetic algorithm enhanced with fuzzy logic and neural networks , 2010 .

[29]  李幼升,et al.  Ph , 1989 .

[30]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[31]  Manas Khurana,et al.  Airfoil Optimisation by Swarm Algorithm with Mutation and Artificial Neural Networks , 2009 .

[32]  Gerald Farin,et al.  Curves and surfaces for computer aided geometric design , 1990 .