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Applicability of Surrogates to Improve Efficiency of Particle Swarm Optimization for Simulation-based Problems

Parno, M. and Fowler, K. and Hemker, Thomas (2011):
Applicability of Surrogates to Improve Efficiency of Particle Swarm Optimization for Simulation-based Problems.
In: Engineering Optimization, pp. online, DOI: 10.1080/0305215X.2011.598521,
[Article]

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.

Item Type: Article
Erschienen: 2011
Creators: Parno, M. and Fowler, K. and Hemker, Thomas
Title: Applicability of Surrogates to Improve Efficiency of Particle Swarm Optimization for Simulation-based Problems
Language: German
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.

Journal or Publication Title: Engineering Optimization
Divisions: 20 Department of Computer Science > Simulation, Systems Optimization and Robotics Group
20 Department of Computer Science
Date Deposited: 20 Jun 2016 23:26
DOI: 10.1080/0305215X.2011.598521
Identification Number: parno2011
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