Artificial Intelligence - Machine Learning Problem Solving Environment for Complex Design in Science and Engineering

 

INTRODUCTION

Machine learning, a cornerstone of artificial and computational intelligence defined loosely as a means to build approximations from data, has played a key role in reducing the impact of the aforementioned challenge. The existing methods in the literature have been successfully applied in a host of practical domains spanning natural language processing, computer vision, recommender systems, etc. In complex design, machine learning is predominantly used for approximating the expensive physics-based simulations or multiagent-based simulations using supervised regression models, or more commonly, surrogate models. Despite the promising outcomes achieved thus far, it is deemed that the full capabilities of machine learning are yet to be fully unveiled and exploited in this domain.

Often, many calls to the analysis or simulation codes are often required to locate a near optimal solution. Optimization problems in which the evaluation of solutions is expensive arise in a variety of contexts. The reasons for the high cost of evaluation and their effect on how many design assessments can be afforded differ widely from one problem to another, as the following three examples may illustrate. (i) When evolving controllers for a simulated collective of robots, the fidelity of the physics simulator, the noise/stochasticity in the system, and the desire to obtain robots that are robust to rare events may all play a part in making simulation times very long. (ii) When evolving a novel protein for a specific binding target by synthesis of proteins in vitro and their subsequent screening, thousands of proteins may be synthesised in parallel but each further "generation" will take another 12 hours to process and will also have financial implications. (iii) When evolving a basic conceptual design for a new building, an architect evaluating the designs will suffer fatigue after several hours and will eventually have to stop.

To circumvent the abovementioned problems, some common practices have been investigated in this project: 1) the use of approximation models in lieu of the exact analysis code, and 2) parallelization of the analysis code evaluations. Approximation models are used to replace calls to the computationally expensive codes as often as possible in the evolutionary search process. These approximation models are commonly known as surrogate models or metamodels. Using approximation models, the computational burden can be greatly reduced since the efforts involved in building the surrogate model and optimization using it is much lower than the standard approach of directly coupling the simulation codes with the optimizer. Nevertheless, this approach does not always perform well when the approximation models are not managed properly. Inaccuracy of the models constructed is one of the many problems faced by most engineers and designers due to lack of data or curse of dimensionality. Hence, there is a need for methodologies to efficiently and effectively use approximation methods in optimization in the presence of such problems.

Further, it is noted that in current practices approximation models are typically built from scratch assuming zero prior knowledge, only relying on data sampled from the ongoing target problem of interest. However, it is contended that any practically useful intelligent system in an industrial setting will be faced with a large number of problems over a lifetime, with the problems likely sharing domain specific overlaps. With this in mind, we have also investigated three relatively advanced machine learning technologies that are especially developed to enable automatic knowledge transfer as a means of improving upon existing tabula rasa efforts. Our aim is to unveil meta-machine learning as a promising approach to enhance the efficiency of aircraft design and facilitate the realization of a more agile design process. Specifically, improving design procedures and methodologies so as to allow for rapid adaptation to change, lower development costs, and shorter time-to-market.

 

Approximation of Computational Expensive Analysis or Simulation Models

 

 

Aerodynamic Airfoil Wing Design

 

Discovery of Isomers in H2O(n) Using 1st Principal Methods

 

One of the well-known strength of stochastic optimization is also in the ability to partition the population of individuals among multiple computing nodes. Doing so allows sub-linear speedup in computation and even super-linear speedup if possible algorithmic speed-up is also considered. When applied to small scale dedicated and homogeneous computing nodes, this seems to be a very formidable solution. In real-life situation, there are many cases where heterogeneity exists, e.g. in a Grid/Cloud computing environment, which emphasizes on the seamless sharing of computing resources across laboratories and even geographical boundaries, heterogeneity of the resources in the sharing pool is inevitable. In addition to that, function evaluation time can vary in many cases, for instance, in the case where the objective function is a variable-fidelity function. In such situation a conventional parallelization without taking into account the heterogeneity of computing resources, might lead the EA to be ineffective. Hence, a suitable parallel optimization framework that fit in a heterogeneous computing environment while maintaining (or improving) the good search property of stochastic optimization is developed.

Parallel Hierarchical Genetic Algorithm on The Grid/Cloud

 
REFERENCES

W. M. Tan, Y. S. Ong, A. Gupta and C. K. Goh, "Multi-Problem Surrogates: Transfer Evolutionary Multiobjective Optimization of Computationally Expensive Problems", IEEE Transactions on Evolutionary Computation, In Press 2018. Available here: PDF file.

A. Gupta, Y. S. Ong and L. Feng, "Insights on Transfer Optimization: Because Experience is the Best Teacher", IEEE Transactions on Emerging Topics in Computational Intelligence, In Press 2017. Available here: PDF file.

H. Liu, J. F. Cai and Y. S. Ong, "Remarks on Multi-Output Gaussian Process Regression", Knowledge-Based Systems, In Press, 2018. Available here as PDF file.

H. Liu, Y. S. Ong and J. F. Cai, “A Survey of Adaptive Sampling for Global Metamodeling in support of Simulation-based Complex Engineering Design”, Structural and Multidisciplinary Optimization, In Press, 2017.

H. Liu, J. F. Cai and Y. S. Ong “An Adaptive Sampling Approach for Kriging Metamodeling by Maximizing Expected Prediction Error”, Computers and Chemical Engineering, In Press, 2017.

W. M. Tan, R. Sagarna, A. Gupta, Y. S. Ong, and C. K. Goh, , “Knowledge Transfer through Machine Learning in Aircraft Design”, IEEE Computational Intelligence Magazine, In Press, 2017, Available here as PDF file.

A. Kattan, A. Agapitos,Y. S. Ong, A. A. Alghamedi and M. O'Neill, “GP Made Faster with Semantic Surrogate Modelling”, Information Sciences, Vol. 355-356, pps. 169-185, 2016.

M. N. Le, Y. S. Ong, S. Menzel, Y. Jin and B. Sendhoff, "Evolution by Adapting Surrogates", Evolutionary Computation Journal, Accepted and In Press 2012.

S. D. Handoko, C. K. Kwoh and Y. S. Ong, "Feasibility Structure Modeling: An Effective Chaperon for Constrained Memetic Algorithms", IEEE Transactions on Evolutionary Computation, Accepted August 2009 and In Press. Available here as PDF file.

H. Soh, Y. S. Ong, Q. C. Nguyen, Q. H. Nguyen, M. S. Habibullah, T. Hung and J.-L. Kuo, “Discovering Unique, Low-Energy Pure Water Isomers: Memetic Exploration, Optimization and Landscape Analysis”, IEEE Transactions on Evolutionary Computation, Vol. 14, No. 3, pp. 419-437, Jun 2010. Available here as PDF file.

D. Lim, Y. Jin, Y. S. Ong and B. Sendhoff, "Generalizing Surrogate-assisted Evolutionary Computation", IEEE Transactions on Evolutionary Computation, Vol. 14, No. 3, pp. 329-355, Jun 2010. Available here as PDF file. *Source code Download*.

Q. C. Nguyen, Y. S. Ong, H. Soh and J.-L. Kuo, "Multiscale Approach to Explore the Potential Energy Surface of Water Clusters (H2O)8 n<=8", Journal of Phys. Chem. A, Vol. 112, No. 28, pp. 6257 - 6261, 2008.

Y. S. Ong, K. Y. Lum and P. B. Nair, “Evolutionary Algorithm with Hermite Radial Basis Function Interpolants for Computationally Expensive Adjoint Solvers”, Computational Optimization and Applications, Accepted 2006, Vol. 39, No. 1, January 2008, pp. 97-119 . Available here as PDF file.

D. Lim, Y. S. Ong, Y. Jin and B. Sendhoff, 'A Study on Metamodeling Techniques, Ensembles, and Multi-Surrogates in Evolutionary Computation', Genetic and Evolutionary Computation Conference. London, UK, pp. 1288 - 1295, 2007, ACM Press. Available here as PDF file or from ACM Press

Z. Z. Zhou, Y. S. Ong, M. H. Lim and B. S. Lim, "Memetic Algorithm using Multi-Surrogates for Computationally Expensive Optimization Problems", Soft Computing Journal, Vol. 11, No. 10,  pp. 957-971, August 2007. Available here as PDF file or from Springer.

H. Soh, Y. S. Ong, M. Salahuddin, T. Hung and B. S. Lee, ‘Playing in the Objective Space: Coupled Approximators for Multi-Objective Optimization’, IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making, Apr 2007. Available here as PDF file.

Z. Z. Zhou, Y. S. Ong, P. B. Nair, A. J. Keane and K. Y. Lum, “Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization”, IEEE Transactions On Systems, Man and Cybernetics - Part C, Vol. 37, No. 1, Jan. 2007, pp. 66-76. Available here as PDF file.

Y. S. Ong, P. B. Nair and K. Y. Lum, “Max-Min Surrogate-Assisted Evolutionary Algorithm for Robust Aerodynamic Design”, IEEE Transactions on Evolutionary Computation, Vol. 10, No. 4, pp. 392-404, August 2006. Available here as PDF file.

Y. S. Ong, Z. Z. Zong and D. Lim, “Curse and Blessing of Uncertainty in Evolutionary Algorithm Using Approximation”, IEEE Congress on Evolutionary Computation, July 16-21, CEC 2006, Sheraton Vancouver Wall Centre, Vancouver, BC, Canada. Available here as PDF file.

D. Lim, Y. S. Ong, Y. Jin and B. Sendhoff, “Trusted Evolutionary Algorithm”, IEEE Congress on Evolutionary Computation, July 16-21, CEC 2006, Sheraton Vancouver Wall Centre, Vancouver, BC, Canada. Available here as PDF file.

Z. Z. Zhou, Y. S. Ong, M. H. Nguyen and D. Lim, “A Study on Polynomial Regression and Gaussian Process Global Surrogate Model in Hierarchical Surrogate-Assisted Evolutionary Algorithm”, Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE'05), IEEE Congress on Evolutionary Computation, Edinburgh, United Kingdom, September 2-5, 2005. Available here as PDF file.

D. Lim, Y. S. Ong and B. S. Lee, “Inverse Multi-Objective Robust Evolutionary Design Optimization in the Presence of Uncertainty”, GECCO 2005, Washington D.C. USA, June 25-29, 2005. Available here as PDF file.

Y. S. Ong, P. B. Nair, A. J. Keane and K. W. Wong,  “Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems”, Knowledge Incorporation in Evolutionary Computation, editor: Y. Jin, Studies in Fuzziness and Soft Computing Series, Springer Verlag, pp. 307 - 331, 2004. Available here as PDF file

Z. Z. Zhou, Y. S. Ong and P. B. Nair, “Hierarchical Surrogate-Assisted Evolutionary Optimization Framework”, IEEE Congress on Evolutionary Computation, Special Session on Learning and Approximation in Design Optimization, Portland, USA, June 20-23, 2004. Available here as PDF file.

Y. S. Ong, P.B. Nair and A.J. Keane, “Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling”, American Institute of Aeronautics and Astronautics Journal, 2003, Vol. 41, No. 4, pp. 687-696. Available here as PS file.

Y. S. Ong, A.J. Keane and P.B. Nair, “Surrogate-Assisted Coevolutionary Search”, 9th International Conference on Neural Information Processing, Special Session on Trends in Global Optimization, pp. 2195-2199, 18-22 November 2002.  Available here as PS file.

Y. S. Ong and A.J. Keane, “A domain knowledge based search advisor for design problem solving environments”, Engineering Applications of Artificial Intelligence, 2002, Vol. 15, No. 1, pp. 105-116. Available here as PS file.

Y. S. Ong and A.J. Keane, “An Automated Optimization System for Aircraft Wing Design”, Seventh International Conference on Artificial Intelligence in Design, Cambridge, UK, July 2002.