Parno, M. ; Fowler, K. ; Hemker, Thomas (2011)
Applicability of Surrogates to Improve Efficiency of Particle Swarm Optimization for Simulation-based Problems.
In: Engineering Optimization
doi: 10.1080/0305215X.2011.598521
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Particle swarm optimization (PSO) is a population-based, heuristic technique based on social behaviour that performs well on a variety of problems including those with non-convex, non-smooth objective functions with multiple minima. However, the method can be computationally expensive in that a large number of function calls is required. This is a drawback when evaluations depend on an off-the-shelf simulation program, which is often the case in engineering applications. An algorithm is proposed which incorporates surrogates as a stand-in for the expensive objective function, within the PSO framework. Numerical results are presented on standard benchmarking problems and a simulation-based hydrology application to show that this hybrid can improve efficiency. A comparison is made between the application of a global PSO and a standard PSO to the same formulations with surrogates. Finally, data profiles, probability of success, and a measure of the signal-to-noise ratio of the the objective function are used to assess the use of a surrogate.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2011 |
| Autor(en): | Parno, M. ; Fowler, K. ; Hemker, Thomas |
| Art des Eintrags: | Bibliographie |
| Titel: | Applicability of Surrogates to Improve Efficiency of Particle Swarm Optimization for Simulation-based Problems |
| Sprache: | Deutsch |
| Publikationsjahr: | 2011 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Engineering Optimization |
| DOI: | 10.1080/0305215X.2011.598521 |
| Kurzbeschreibung (Abstract): | Particle swarm optimization (PSO) is a population-based, heuristic technique based on social behaviour that performs well on a variety of problems including those with non-convex, non-smooth objective functions with multiple minima. However, the method can be computationally expensive in that a large number of function calls is required. This is a drawback when evaluations depend on an off-the-shelf simulation program, which is often the case in engineering applications. An algorithm is proposed which incorporates surrogates as a stand-in for the expensive objective function, within the PSO framework. Numerical results are presented on standard benchmarking problems and a simulation-based hydrology application to show that this hybrid can improve efficiency. A comparison is made between the application of a global PSO and a standard PSO to the same formulations with surrogates. Finally, data profiles, probability of success, and a measure of the signal-to-noise ratio of the the objective function are used to assess the use of a surrogate. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik > Simulation, Systemoptimierung und Robotik 20 Fachbereich Informatik |
| Hinterlegungsdatum: | 20 Jun 2016 23:26 |
| Letzte Änderung: | 16 Mai 2018 08:07 |
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