Bayes approach to solving T.E.A.M. benchmark problems 22 and 25 and its comparison with other optimization techniques
| Autor: | Pavel Karban, Václav Kotlan, Ivo Doležel, Petr Kropík |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
010302 applied physics
Mathematical optimization business.industry Heuristic (computer science) Computer science Applied Mathematics Multiphysics Suite 010103 numerical & computational mathematics Machine learning computer.software_genre 01 natural sciences Domain (software engineering) Computational Mathematics Bayes' theorem 0103 physical sciences Benchmark (computing) Code (cryptography) Optimization methods Artificial intelligence 0101 mathematics business computer |
| Zdroj: | Applied Mathematics and Computation. 319:681-692 |
| ISSN: | 0096-3003 |
| DOI: | 10.1016/j.amc.2017.07.043 |
| Popis: | The Bayes approach is used for solution of benchmark problems 22 and 25. The main purpose of the paper is to evaluate its applicability for solving complex technical problems (up to now, this technique was only very rarely used in the domain of such tasks). The parameters of this approach are compared with characteristics of several other heuristic and deterministic optimization techniques implemented in commercial code COMSOL Multiphysics and own open-source application Agros Suite. The results confirm that the Bayes approach is superior in a number of aspects and for the solution of real-life tasks it represents a powerful and prospective alternative to existing optimization methods |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect