Computational Optimization, Modelling and Simulation: Recent Trends and Challenges

Modelling, simulation and optimization form an integrated part of modern design practice in engineering and industry. Tremendous progress has been observed for all three components over the last few decades. However, many challenging issues remain unresolved, and the current trends tend to use nature-inspired algorithms and surrogate-based techniques for modelling and optimization. This 4th workshop on Computational Optimization, Modelling and Simulation (COMS 2013) at ICCS 2013 will further summarize the latest developments of optimization and modelling and their applications in science, engineering and industry. In this review paper, we will analyse the recent trends in modelling and optimization, and their associated challenges. We will discuss important topics for further research, including parameter-tuning, large-scale problems, and the gaps between theory and applications.

[1]  Andy J. Keane,et al.  Optimization using surrogate models and partially converged computational fluid dynamics simulations , 2006, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[2]  Xin-She Yang,et al.  Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization , 2010, NICSO.

[3]  Andy J. Keane,et al.  Efficient Multipoint Aerodynamic Design Optimization Via Cokriging , 2011 .

[4]  Xin-She Yang,et al.  Engineering Optimization: An Introduction with Metaheuristic Applications , 2010 .

[5]  Xin-She Yang,et al.  Bat algorithm: a novel approach for global engineering optimization , 2012, 1211.6663.

[6]  Slawomir Koziel,et al.  Computational Optimization, Methods and Algorithms , 2016, Computational Optimization, Methods and Algorithms.

[7]  Natalio Krasnogor,et al.  Nature‐inspired cooperative strategies for optimization , 2009, Int. J. Intell. Syst..

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

[9]  Xin-She Yang,et al.  Simulation-Driven Design Optimization and Modeling for Microwave Engineering , 2013 .

[10]  Slawomir Koziel,et al.  Multi-fidelity design optimization of transonic airfoils using physics-based surrogate modeling and shape-preserving response prediction , 2010, J. Comput. Sci..

[11]  P. A. Newman,et al.  Optimization with variable-fidelity models applied to wing design , 1999 .

[12]  Slawomir Koziel,et al.  Surrogate-based modeling and optimization : applications in engineering , 2013 .

[13]  Xin-She Yang.  Introduction to Mathematical Optimization: From Linear Programming to Metaheuristics , 2008 .

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

[15]  Xin-She Yang,et al.  Chaos-enhanced accelerated particle swarm optimization , 2013, Commun. Nonlinear Sci. Numer. Simul..

[16]  M.C.E. Yagoub,et al.  Advanced microwave modeling framework exploiting automatic model generation, knowledge neural networks and space mapping , 2002, 2002 IEEE MTT-S International Microwave Symposium Digest (Cat. No.02CH37278).

[17]  Leifur Leifsson,et al.  Surrogate-Based Methods , 2011, Computational Optimization, Methods and Algorithms.

[18]  Francine Berman,et al.  Overview of the Book: Grid Computing – Making the Global Infrastructure a Reality , 2003 .

[19]  A. E. Eiben,et al.  Parameter tuning for configuring and analyzing evolutionary algorithms , 2011, Swarm Evol. Comput..

[20]  Vaibhav Yelne,et al.  Green Computing , 2013 .

[21]  John W. Bandler,et al.  Quality assessment of coarse models and surrogates for space mapping optimization , 2008 .

[22]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[23]  Xin-She Yang.  Introduction to Computational Mathematics , 2008 .

[24]  Rajkumar Buyya,et al.  Cloud Computing Principles and Paradigms , 2011 .

[25]  J.W. Bandler,et al.  Space mapping: the state of the art , 2004, IEEE Transactions on Microwave Theory and Techniques.