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| Content Provider | ACM Digital Library |
|---|---|
| Author | Urselmann, Maren Tlatlik, Stephen Bäck, Thomas Janus, Tim Gottschalk, Axel Engell, Sebastian Emmerich, Michael T.M. Foussette, Christophe |
| Abstract | In this contribution a derivative-free memetic algorithm (MA) for the design optimization of chemical processes is introduced. Design optimization problems are characterized by nonlinear cost functions and highly constrained and multi-modal search spaces. The MA is a combination of an evolution strategy that addresses the global optimization of discrete and continuous design decisions and a derivative-free optimization method (DFO) that performs a local optimization of the continuous sub-problems that remain after fixing the discrete decisions. The MA calls a process simulation software to simulate the design alternatives, i.e. the evaluation of the objective and the constraints is a black box. In this contribution, the focus lies on the selection of a suitable DFO solver for efficiently solving the continuous constrained sub-problems. Based on latin-hypercube samplings of sub-problems of two instances of a real-world case study, surrogate models for the objective and the constraints were generated. A set of DFO methods was tested and compared on the surrogate models. The method that showed the best performance was coupled to the MA which was then applied to the real-world case study. |
| Starting Page | 1029 |
| Ending Page | 1036 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450342063 |
| DOI | 10.1145/2908812.2908874 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-07-20 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Simulation optimization Memetic algorithms (ma) Derivative-free optimization (dfo) Surrogate models Design optimization |
| Content Type | Text |
| Resource Type | Article |
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