Summary
Over the last decade, Evolutionary Algorithms (EAs) have emerged as a powerful paradigm for global optimization of multimodal functions. More recently, there has been significant interest in applying EAs to engineering design problems. However, in many complex engineering design problems where high-fidelity analysis models are used, each function evaluation may require a Computational Structural Mechanics (CSM), Computational Fluid Dynamics (CFD) or Computational Electro-Magnetics (CEM) simulation costing minutes to hours of supercomputer time. Since EAs typically require thousands of function evaluations to locate a near optimal solution, the use of EAs often becomes computationally prohibitive for this class of problems. In this chapter, we present frameworks that employ surrogate models for solving computationally expensive optimization problems on a limited computational budget. In particular, the key factors responsible for the success of these frameworks are discussed. Experimental results obtained on benchmark test functions and real-world complex design problems are presented.
Keywords
- Engineering Design Problem
- Computational Budget
- Traditional Genetic Algorithm
- Convergence Trend
- Radial Basis Func
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Ong, Y.S., Nair, P.B., Keane, A.J., Wong, K.W. (2005). Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems. In: Jin, Y. (eds) Knowledge Incorporation in Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44511-1_15
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