Surrogate Model-Driven Evolutionary Algorithms: Theory and Applications
- Publisher:
- Springer International Publishing
- Publication Type:
- Chapter
- Citation:
- Evolution in Action: Past, Present and Future, 2020, pp. 435-451
- Issue Date:
- 2020
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Pages from 2020_Book_EvolutionInActionPastPresentAn.pdf | Published version | 506.9 kB |
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Engineering optimization problems are challenging to solve mainly due to their numerical modeling and analysis complexities. This chapter deals with the efficient use of surrogate model-driven evolutionary algorithms, built hierarchically for the solution of large-scale computation intensive optimization problems. In most optimization problems, the majority of computation is involved in repetitive function calls to evaluate the system response/bahaviour under consideration. The quality solutions depends on the system response estimation, and in most cases high fidelity models are used to get accurate results. Conventional evolutionary algorithms require a great number of such high fidelity function calls. Here, we use low cost surrogate models or metamodels, which approximate the original model mathematically, but significantly reduce the computation cost for a desired accuracy level. The surrogate model training requires a small amount of evaluations of the original model at support points. The hierarchical surrogate model-based PSO algorithms we propose are tested on a range of large-scale design optimization problems and compared with other well-known surrogate modeling techniques.
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