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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2017 - 11

Developing Automatic Multi-Objective Optimization Methods for Complex Actuators

CHIS, R. See more information about CHIS, R. on SCOPUS See more information about CHIS, R. on IEEExplore See more information about CHIS, R. on Web of Science, VINTAN, L. See more information about VINTAN, L. on SCOPUS See more information about VINTAN, L. on SCOPUS See more information about VINTAN, L. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,410 KB) | Citation | Downloads: 358 | Views: 1,346

Author keywords
actuators, computer aided engineering, machine learning, pareto optimization, response surface methodology

References keywords
optimization(11), design(9), systems(7), multi(7), vintan(5), computing(5), objective(4), multiobjective(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-11-30
Volume 17, Issue 4, Year 2017, On page(s): 89 - 98
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.04011
Web of Science Accession Number: 000417674300011
SCOPUS ID: 85035746256

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This paper presents the analysis and multiobjective optimization of a magnetic actuator. By varying just 8 parameters of the magnetic actuators model the design space grows to more than 6 million configurations. Much more, the 8 objectives that must be optimized are conflicting and generate a huge objectives space, too. To cope with this complexity, we use advanced heuristic methods for Automatic Design Space Exploration. FADSE tool is one Automatic Design Space Exploration framework including different state of the art multi-objective meta-heuristics for solving NP-hard problems, which we used for the analysis and optimization of the COMSOL and MATLAB model of the magnetic actuator. We show that using a state of the art genetic multi-objective algorithm, response surface modelling methods and some machine learning techniques, the timing complexity of the design space exploration can be reduced, while still taking into consideration objective constraints so that various Pareto optimal configurations can be found. Using our developed approach, we were able to decrease the simulation time by at least a factor of 10, compared to a run that does all the simulations, while keeping prediction errors to around 1%.

References | Cited By  «-- Click to see who has cited this paper

[1] H. A. Calborean, "Multi-Objective Optimization of Advanced Computer Architectures using Domain- Knowledge," Ph.D. Thesis, "Lucian Blaga" University of Sibiu, Sibiu, 2011 (Ph.D. Supervisor: Prof. L. Vintan)

[2] L. Vintan, R. Chis, M. A. Ismail, C. Cotofana, "Improving Computing Systems Automatic Multiobjective Optimization Through Meta-Optimization, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems", Volume: 35, Issue: 7, July 2016,
[CrossRef] [Web of Science Times Cited 7]

[3] R. Chis, L. Vintan, "Multi-Objective Hardware-Software Co-Optimization for the SNIPER Multi-Core Simulator", Proceedings of 10th International Conference on Intelligent Computer Communication and Processing, pp. 3-9, Cluj-Napoca, September 2014

[4] G. Palermo, C. Silvano, V. Zaccaria, "ReSPIR: A Response Surface-Based Pareto Iterative Refinement for Application-Specific Design Space Exploration", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Volume: 28, Issue: 12, Dec. 2009,
[CrossRef] [Web of Science Times Cited 72]

[5] A. J. Keane, "Wing Optimization Using Design of Experiment, Response Surface, and Data Fusion Methods", Journal of Aircraft, Vol. 40, No. 4 (2003), pp. 741-750,
[CrossRef] [Web of Science Times Cited 73]

[6] P. V. Huong, N. N. Binh, "An approach to design embedded systems by multi-objective optimization", 2012 International Conference on Advanced Technologies for Communications (ATC), 10-12 Oct. 2012,

[7] H. Shim, H. Moon, S. Wang, K. Hameyer, "Topology Optimization for Compliance Reduction of Magnetomechanical Systems", IEEE Transactions on Magnetics 44(3): 346 - 351, April 2008,
[CrossRef] [Web of Science Times Cited 10]

[8] C. A. Borghi, P. Di Barba, M. Fabbri, A. Savini, "Loney’s Solenoid: A Multi-Ojective Optimization Problem" IEEE Trans. on Magnetics, vol. 35, no. 3, pp. 1706-1709, 1999

[9] H. Calborean, L. Vintan, "An Automatic Design Space Exploration Framework for Multicore Architecture Optimizations", Proceedings of the 9th IEEE RoEduNet International Conference, pp. 202-207, Sibiu, June 24-26, 2010

[10] J. J. Durillo, A. J. Nebro, "jMetal: A Java framework for multi-objective optimization," Adv. Eng. Softw., vol. 42, pp. 760-771, 2011

[11] R. Jahr, H. Calborean, L. Vintan, and T. Ungerer, "Boosting Design Space Explorations with Existing or Automatically Learned Knowledge," in Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance, 2012, pp. 221-235.

[12] E. Zitzler, L. Thiele, "Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach," Evol. Comput. IEEE Trans. On, vol. 3, no. 4, pp. 257-271, Nov. 1999

[13] E. Zitzler, "Evolutionary algorithms for multiobjective optimization: Methods and applications", vol. 63, Shaker Ithaca, 1999

[14] T. E. Carlson, W. Heirman, and L. Eeckhout, "Sniper: Exploring the level of abstraction for scalable and accurate parallel multi-core simulations," in International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Nov. 2011

[15] S. Uhrig, B. Shehan, R. Jahr, and T. Ungerer, "A Two-Dimensional Superscalar Processor Architecture," in Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, 2009. COMPUTATIONWORLD '09. Computation World: 2009, pp. 608-611

[16] "M-Sim: The Multithreaded Simulator." [Online] Available: Temporary on-line reference link removed - see the PDF document

[17] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," Evol. Comput. IEEE Trans. On, vol. 6, no. 2, pp. 182-197, Apr. 2002

[18] M. Abadi, P. Barham, J. Chen, Z. Chen et al., "TensorFlow: a system for large-scale machine learning", Proceeding OSDI'16 Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, Pages 265-283, Savannah, GA, USA - November 02 - 04, 2016

[19] C. Francois, "Keras", [Online] Available: Temporary on-line reference link removed - see the PDF document

[20] T. J. Santner, B. Williams, and W. Notz, "The Design and Analysis of Computer Experiments.", New York: Springer, 2003

[21] Stefan van der Walt, S. Chris Colbert and Gael Varoquaux, "The NumPy Array: A Structure for Efficient Numerical Computation", Computing in Science & Engineering, 13, 22-30 (2011),
[CrossRef] [Web of Science Times Cited 3703]

[22] D. P. Kingma, J. Ba, "Adam: A Method for Stochastic Optimization", 3rd International Conference for Learning Representations, San Diego, 2015, arXiv:1412.6980

[23] Deeplearning4j Development Team. Deeplearning4j: Open-source distributed deep learning for the JVM, Apache Software Foundation License 2.0. [Online] Available: Temporary on-line reference link removed - see the PDF document

References Weight

Web of Science® Citations for all references: 3,865 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 161 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-01-17 12:48 in 38 seconds.

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