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Framework for Particle Swarm Optimization with Surrogate Functions

Parno, M. D. and Fowler, K. R. and Hemker, Thomas (2009):
Framework for Particle Swarm Optimization with Surrogate Functions.
(TUD-CS-2009-0139), [Report]

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.

Item Type: Report
Erschienen: 2009
Creators: Parno, M. D. and Fowler, K. R. and Hemker, Thomas
Title: Framework for Particle Swarm Optimization with Surrogate Functions
Language: English
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.

Number: TUD-CS-2009-0139
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Simulation, Systems Optimization and Robotics Group
Date Deposited: 20 Jun 2016 23:26
Identification Number: Parno2009
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