Parno, M. D. ; Fowler, K. R. ; Hemker, Thomas (2009)
Framework for Particle Swarm Optimization with Surrogate Functions.
Report, Bibliographie
Kurzbeschreibung (Abstract)
Particle swarm optimization (PSO) is a population-based, heuristic minimization technique that is based on social behavior. The method has been shown to perform well on a variety of problems including those with nonconvex, nonsmooth objective functions with multiple local minima. However, the method can be computationally expensive in that a large number of function calls is required to advance the swarm at each optimization iteration. This is a significant drawback when function evaluations depend on output from an off-the-shelf simulation program, which is often the case in engineering applications. To this end, we propose an algorithm which incorporates surrogate functions, which serve as a stand-in for the expensive objective function, within the PSO framework. We present numerical results to show that this hybrid approach can improve algorithmic efficiency.
| Typ des Eintrags: | Report |
|---|---|
| Erschienen: | 2009 |
| Autor(en): | Parno, M. D. ; Fowler, K. R. ; Hemker, Thomas |
| Art des Eintrags: | Bibliographie |
| Titel: | Framework for Particle Swarm Optimization with Surrogate Functions |
| Sprache: | Englisch |
| Publikationsjahr: | 2009 |
| (Heft-)Nummer: | TUD-CS-2009-0139 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Particle swarm optimization (PSO) is a population-based, heuristic minimization technique that is based on social behavior. The method has been shown to perform well on a variety of problems including those with nonconvex, nonsmooth objective functions with multiple local minima. However, the method can be computationally expensive in that a large number of function calls is required to advance the swarm at each optimization iteration. This is a significant drawback when function evaluations depend on output from an off-the-shelf simulation program, which is often the case in engineering applications. To this end, we propose an algorithm which incorporates surrogate functions, which serve as a stand-in for the expensive objective function, within the PSO framework. We present numerical results to show that this hybrid approach can improve algorithmic efficiency. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Simulation, Systemoptimierung und Robotik |
| Hinterlegungsdatum: | 20 Jun 2016 23:26 |
| Letzte Änderung: | 27 Nov 2018 12:11 |
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