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Optimization of the Complex-RFM Optimization Algorithm
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
2015 (English)In: Optimization and Engineering, ISSN 1389-4420, E-ISSN 1573-2924, Vol. 16, no 1, 27-48 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents and compares different modifications made to the Complex-RF optimization algorithm with the aim of improving its performance for computationally expensive models. The modifications reduces the required number of objective function evaluations by creating and using surrogate models of the objective function iteratively during the optimization process. The chosen surrogate model type is a second order response surface. The performance of the modified algorithm is compared with a number of existing algorithms and demonstrated for a few analytical and engineering problems.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2015. Vol. 16, no 1, 27-48 p.
Keyword [en]
Optimization, Surrogate models, Meta-optimization
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-84849DOI: 10.1007/s11081-014-9247-9ISI: 000351842300002OAI: oai:DiVA.org:liu-84849DiVA: diva2:562474
Note

This article status has been changed from Manuscript to Article in Journal.

Available from: 2012-10-24 Created: 2012-10-24 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Design and Optimization under Uncertainties: A Simulation and Surrogate Model Based Approach
Open this publication in new window or tab >>Design and Optimization under Uncertainties: A Simulation and Surrogate Model Based Approach
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis deals with development of complex products via modeling and simulation, and especially the use of surrogate models to decrease the computational efforts when probabilistic optimizations are performed. Many methods that can be used to perform probabilistic optimizations exist and this thesis strives to present and demonstrate the capabilities of a few of them. Hopefully, this information can be helpful for someone who wants to choose a method.

Knowledge about several different topics is required to perform a probabilistic optimization. First, it is necessary to incorporate the probabilistic behavior into the analysis by estimating how the uncertainties and variations in the model and its parameters are affecting the performance of the system. The focus in this thesis is on sampling based methods to estimate these probabilities. Secondly, an optimization algorithm should be chosen so that the computer can search for and present an optimal solution automatically.

The probabilistic optimization process can be computationally demanding since numerous simulations of the model are performed each time the value of the objective function is estimated. It is therefore desirable to speed up the process by incorporating computationally effective surrogate models. This is especially important if the simulated model is computationally demanding on its own, e.g. a finite element model with many nodes.

Each of these topics is presented in its own chapter of this thesis. A few  methods are presented and their performances demonstrated for each topic.

Surrogate models can also be used to improve the performances of optimization algorithms when the desire is to optimize computationally expensive objective functions. With this in mind, efforts have been made to improve the Complex-RF optimization algorithm. A modified algorithm is presented in this thesis and the main difference is that it creates and utilizes surrogate models iteratively during the optimization process. The modified algorithm is compared with Complex-RF and is demonstrated to be superior for computationally expensive models.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 81 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1556
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-84850 (URN)978-91-7519-753-1 (ISBN)
Presentation
2012-11-02, Mass, A-huset, Campus Valla, Linköpings universitet, Linköping, 10:15 (Swedish)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework Programme, 234344
Available from: 2012-10-24 Created: 2012-10-24 Last updated: 2012-10-24Bibliographically approved
2. Efficient Optimization of Complex Products: A Simulation and Surrogate Model Based Approach
Open this publication in new window or tab >>Efficient Optimization of Complex Products: A Simulation and Surrogate Model Based Approach
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis investigates how to use optimization efficiently when complex products are developed. Modelling and simulation are necessary to enable optimization of products, but here it is assumed that verified and validated models of the products and their subsystems are available for the optimization. The focus is instead on how to use the models properly for optimization.

Knowledge about several areas is needed to enable optimization of a wide range of products. A few methods from each area are investigated and compared. Some modifications to existing methods and new methods are also proposed and compared to the previous methods.

These areas include

  • Optimization algorithms to ensure that a suitable algorithm is used to solve the problem
  • Multi-Objective Optimization for products with conflicting objectives
  • Multi-Disciplinary Optimization when analyses from several models and/or disciplines are needed
  • Surrogate Models to enable optimization of computationally expensive models

Modern frameworks for optimization of complex products often include more than one of these areas and this is exemplified with the industrial applications that are presented in this thesis, including the design and optimization of industrial robots and aircraft systems.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. 88 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1655
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-115939 (URN)10.3384/diss.diva-115939 (DOI)978-91-7519-083-9 (ISBN)
Public defence
2015-04-24, ACAS, A-huset, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework Programme, Crescendo no. 234244VINNOVA, IMPOz no. 2013-03758
Available from: 2015-03-24 Created: 2015-03-24 Last updated: 2015-04-17Bibliographically approved

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Persson, JohanÖlvander, Johan

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