Survey on RWOPs

MACODA Working Group: Survey on Real-World Optimization Problems

This working group was created in MACODA workshop.

Our goal is to clarify the characteristics of real-world optimization problems and generate a new benchmark test suite based on the survey. We believe such a test suite helps efficiently developing new optimization methods for real-world optimization problems.

In the first stage, we wish to collect characteristics of optimization problems encountered in industry and applied research. To this end, we would like to ask you for help by answering some questions about the structure of your optimization problem. We will release a statistical report on the webpage and send a notification if you write your email address at the end of the questionnaire.

We really appreciate your contribution to this survey. Please access the questionnaire with the button on the right.

Questionnaire

Our paper has been accepted as a poster paper at Real-World Application Track of GECCO2020. It is available on arXiv.org.
The poster presentation is available on Youtube.

[NEW] The book chapter has been published.

K. van der Blom, T. M. Deist, V. Volz, M. Marchi, Y. Nojima, B. Naujoks, A. Oyama, and T. Tušar, "Identifying properties of real-world optimisation problems through a questionnaire," Many-Criteria Optimization and Decision Analysis: State-of-the-Art, Present Challenges, and Future Perspectives, pp. 59--80, Springer International Publishing, 2023.
@inproceedings{BloEtAl23, author="van der Blom, Koen and Deist, Timo M. and Volz, Vanessa and Marchi, Mariapia and Nojima, Yusuke and Naujoks, Boris and Oyama, Akira and Tu\v{s}ar, Tea", editor="Brockhoff, Dimo and Emmerich, Michael and Naujoks, Boris and Purshouse, Robin", title="Identifying Properties of Real-World Optimisation Problems Through a Questionnaire", year="2023", isbn="978-3-031-25263-1", publisher="Springer International Publishing", address = "Cham", url="https://doi.org/10.1007/978-3-031-25263-1_3", doi="10.1007/978-3-031-25263-1_3", bookTitle="Many-Criteria Optimization and Decision Analysis: State-of-the-Art, Present Challenges, and Future Perspectives", pages="59--80",}

All valid responses from the questionnaire by July 16, 2020 are now available here. If you use this questionnaire data, please cite the above book chapter.

The questionnaire structure

There are 73 questions in total. According to variable types, existence of constraints, and the number of objectives, only 29 to 52 questions are actually posed.

The questionnaire has been modified on April 9, 2020. The number of questions in the previous questionnaire was 75. The GECCO2020 poster paper and the first report were prepared using the previous version. The main difference from the previous version is the modification of the questions' descriptions. The structure of the current version is the same as the previous one.

All the questions in the current questionnaire can be downloaded from here.

Members of this working group

Koen van der Blom (LIACS, Leiden University, Netherlands)

Timo Deist (Centrum Wiskunde Informatica, Netherlands)

Mariapia Marchi (ESTECO SpA, Italy)

Boris Naujoks (TH Köln, Germany)

Yusuke Nojima (Osaka Prefecture University, Japan)

Akira Oyama (Japan Aerospace Exploration Agency, Japan)

Tea Tušar (Jozef Stefan Institute, Slovenia)

Vanessa Volz (modl.ai, Denmark)

List of papers related to the answers we received by now.

T. Alderliesten, P. A. N. Bosman, and A. Bel, “Getting the most out of additional guidance information in deformable image registration by leveraging multi-objective optimization,” in Medical Imaging 2015: Image Processing, Orlando, Florida, USA, February 24-26, 2015, ser. SPIE Proceedings, S. Ourselin and M. A. Styner, Eds., vol. 9413. SPIE, 2015, p. 94131R. https://doi.org/10.1117/12.2081438
M. Awad and K. De Jong, "Optimization of spectral signatures selection using multi-objective genetic algorithms," in Proc. of 2011 IEEE Congress on Evolutionary Computation, New Orleans, USA, 5-8 June 2011. https://ieeexplore.ieee.org/document/5949809
M. Balvert and D. Craft, “Fast approximate delivery of fluence maps for IMRT and VMAT,” Physics in Medicine and Biology, vol. 62, no. 4, pp. 1225–1247, Jan 2017. https://iopscience.iop.org/article/10.1088/1361-6560/aa56b6
L. Bliek, S. Wahls, I. Visscher, C. Taddei, R. B. Timens, R. Oldenbeuving, C. Roeloffzen, M. Verhaegen, "Automatic delay tuning of a novel ring resonator-based photonic beamformer for a transmit phased array antenna," Journal of Lightwave Technology, vol. 37, no. 19, pp. 4976-4984, Oct. 2019. https://ieeexplore.ieee.org/document/8754808
K. van der Blom, “Multi-objective mixed-integer evolutionary algorithms for building spatial design,” 12 2019. https://openaccess.leidenuniv.nl/handle/1887/81789
C. Doerr, M. Gnewuch, and M. Wahlström, "Calculation of discrepancy measures and applications," In A Panorama of Discrepancy Theory, W. W. L. Chen, A. Srivastav, G. Travaglini (Eds.), pp. 621-678, Springer, 2014. https://link.springer.com/chapter/10.1007/978-3-319-04696-9_10
M. Ehrgott and D. M. Ryan, "Constructing robust crew schedules with bicriteria optimization," Journal of Multi-Criteria Decision Analysis, vol. 11, no. 3, pp. 139-150, May/June 2002. https://doi.org/10.1002/mcda.321
M. Emmerich and B. Naujoks, "Metamodel-assisted multiobjective optimization with implicit constraints and its application in airfoil design," Proc. of Design Optimization International Conference, 2004. http://velos0.ltt.mech.ntua.gr/ERCOFTAC/PROC04/fp/ERCODO2004_217.pdf
M. Erascu, F. Micota, and D. Zaharie, "Scalable optimal deployment in the cloud of component-based applications using optimization modulo theory, mathematical programming and symmetry breaking," arXiv preprint arXiv:2006.05401, June 2020. https://arxiv.org/abs/2006.05401
A. Gaspar-Cunha, and J. A. Covas, "The design of extrusion screws: An optimisation approach," International Polymer Processing, vol. 16, no. 3, pp. 229-240, Sept. 2001. https://www.hanser-elibrary.com/doi/abs/10.3139/217.1652
M. Geravand, P. Z. Korondi, C. Werner, K. Hauer, and A. Peer, “Human sit-to-stand transfer modeling towards intuitive and biologically-inspired robot assistance,” Auton. Robots, vol. 41, no. 3, pp. 575–592, 2017. https://doi.org/10.1007/s10514-016-9553-5
A. Glotić and A. Zamuda, "Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution," Applied Energy, vol. 141, pp. 42-561, March 2015. https://doi.org/10.1016/j.apenergy.2014.12.020
M. Kanazaki, T. Sato, and K. Matsushima, “Parametric airfoil representation toward efficient design knowledge discovery under various flow condition,” Transactions of the Japan Society for Aeronautical and Space Sciences, Aerospace Technology Japan, vol. 12, no. APISAT-2013, pp. a93–a98, 2014. https://doi.org/10.2322/tastj.12.a93
M. Kanazaki, Y. Yamada, and M. Nakamiya, “Multi-objective path optimization of a satellite for multiple active space debris removal based on a method for the travelling serviceman problem,” Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 479–488, 2018. https://dx.doi.org/10.25046/aj030656
Y. Kitagawa, K. Kitagawa, M. Nakamiya, M. Kanazaki, and T. Shimada, “Multi-stage hybrid rocket conceptual design for micro-satellites launch using genetic algorithm,” Transactions of the Japan Society for Aeronautical and Space Sciences, vol. 55, no. 4, pp. 229–236, 2012. https://doi.org/10.2322/tjsass.55.229
T. Kohira, H. Kemmotsu, A. Oyama, and T. Tatsukawa, “Proposal of benchmark problem based on real-world car structure design optimization,” in Proc. of 2018 Genetic and Evolutionary Computation Conference Companion, GECCO 2018, Kyoto, Japan, July 15-19, 2018, H. E. Aguirre and K. Takadama, Eds. ACM, 2018, pp. 183–184. https://doi.org/10.1145/3205651.3205702
W. B. Langdon, J. Petke, and R. Lorenz, "Evolving better RNAfold structure prediction," In: M. Castelli, L. Sekanina, M. Zhang, S. Cagnoni, P. García-Sánchez (eds) Genetic Programming. EuroGP 2018. Lecture Notes in Computer Science, vol 10781. Springer, Cham. pp. 220-236, 2018. https://doi.org/10.1007/978-3-319-77553-1_14
M. C. van der Meer, B. R. Pieters, Y. Niatsetski, T. Alderliesten, A. Bel, and P. A. N. Bosman, “Better and faster catheter position optimization in HDR brachytherapy for prostate cancer using multi-objective real-valued GOMEA,” in Proc. of 2018 Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, H. E. Aguirre and K. Takadama, Eds. ACM, 2018, pp. 1387–1394. https://doi.org/10.1145/3205455.3205505
M. Mlakar, D. Petelin, T. Tusar, and B. Filipic, “GP-DEMO: differential evolution for multiobjective optimization based on gaussian process models,” European Journal of Operational Research, vol. 243, no. 2, pp. 347–361, 2015. https://doi.org/10.1016/j.ejor.2014.04.011
B. Naujoks, W. Haase, J. Ziegenhirt, and T. Bäck, "Multi objective airfoil design using single parent populations," In Proc. of 2002 Genetic and Evolutionary Computation Conference, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 1156-1163, 2002. https://dl.acm.org/doi/10.5555/2955491.2955693
N. Namura, K. Shimoyama, S. Obayashi, Y. Ito, S. Koike, and K. Nakakita, “Multipoint design optimization of vortex generators on transonic swept wings,” Journal of Aircraft, vol. 56, no. 4, pp. 1291-1302, March 2019. https://arc.aiaa.org/doi/abs/10.2514/1.C035148
S. O'Hagan, W. B. Dunn, M. Brown, J. D. Knowles, and D. B. Kell, "Closed-loop, multiobjective optimization of analytical instrumentation: Gas chromatography/time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations," Analytical Chemistry, vol. 77, no. 1, pp. 290-303, 2005. https://pubs.acs.org/doi/abs/10.1021/ac049146x
M. Ohki, “Many-objective nurse scheduling using Pareto partial dominance with linear subset-size scheduling,” in Proc. of 10th International Joint Conference on Computational Intelligence (IJCCI 2018, Seville-Spain, 18-20, Sep. 2018) , ISSN: 2184-2825, ISBN: 978-989-758-327-8, Depósito Legal: 444817/18 , (2018, 9)
Y. Ohta and H. Sato, “Evolutionary optimization of air-conditioning schedule robust for temperature forecast errors,” in Proc. of 2019 IEEE Congress on Evolutionary Computation, pp.2482-2489, 2019. https://ieeexplore.ieee.org/document/8789972
J. Rohmer, M. Rousseau, A. Lemoine, R. Pedreros, J. Lambert, and A. Benki, “Source characterisation by mixing long-running tsunami wave numerical simulations and historical observations within a metamodel-aided abc setting,” Stochastic Environmental Research and Risk Assessment, vol. 32, no. 4, pp. 967–984, 2018. https://doi.org/10.1007/s00477-017-1423-y
R. Russo, A. Clarich, and M. Carriglio, “A multi-objective optimization of engine crankshaft design using modefrontier,” International Review of Mechanical Engineering (I.RE.M.E.), vol. 6, no. 3, pp. 574–577, 2012. http://connection.ebscohost.com/c/articles/82565328/multi-objective-optimization-engine-crankshaft-design-using-modefrontier
B. G. Small, B.W. McColl, R. Allmendinger, J. Pahle, G. Lopez-Castejon, N.J. Rothwell, J. Knowles, P. Mendes, D. Brough, and D.B. Kell, "Efficient discovery of anti-inflammatory small molecule combinations using evolutionary computing," Nature Chemical Biology, vol. 7, pp. 902-908, Oct. 2011. https://www.nature.com/articles/nchembio.689
M. Schlueter, "MIDACO software performance on interplanetary trajectory benchmarks," Advances in Space Research, vol. 54, no. 4, pp. 744-754, 2014. http://www.sciencedirect.com/science/article/pii/S0273117714002786
T. Sumimoto, K. Chiba, M. Kanazaki, T. Fujikawa, K. Yonemoto, and N. Hamada, "Evolutionary multidisciplinary design optimization of blended-wing-body-type flyback booster," in Proc. of 57th AIAA Aerospace Sciences Meeting, 13 pages, San Diego, California, USA, Jan. 7-11, 2019. https://arc.aiaa.org/doi/abs/10.2514/6.2019-0703
M. A. M. Teixeira, F. Goulart, and F. Campelo, “Evolutionary multiobjective optimization of winglets,” in Proc. of 2016 Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20 - 24, 2016, T. Friedrich, F. Neumann, and A. M. Sutton, Eds. ACM, 2016, pp. 1021–1028. https://doi.org/10.1145/2908812.2908848
T. Uchitane and T. Hatanaka, "Applying evolution strategies for biped locomotion learning in RoboCup 3D soccer simulation," in Proc. 2011 IEEE Congress of Evolutionary Computation, New Orleans, LA, 2011, pp. 179-185, 2011. https://ieeexplore.ieee.org/document/5949616
C. Volk, A. Chatterjee, F. Ansaloni, C. M. Marcus, and F. Kuemmeth, "Fast charge sensing of Si/SiGe quantum dots via a high-frequency accumulation gate," Nano Letters, vol. 19, no. 8, pp. 5628-5633, 2019. https://pubs.acs.org/doi/10.1021/acs.nanolett.9b02149
R. de Winter, B. van Stein, M. Dijkman, and T. Bäck, "Designing ships using constrained multi-objective efficient global optimization," In: G. Nicosia, P. Pardalos, G. Giuffrida, R. Umeton, V. Sciacca (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science, vol 11331. Springer, Cham, pp 191-203, 2019. https://link.springer.com/chapter/10.1007%2F978-3-030-13709-0_16
M. Zaefferer, B. Breiderhoff, B. Naujoks, M. Friese, J. Stork, A. Fischbach, O. Flasch, and T. Bartz-Beielstein, "Tuning multi-objective optimization algorithms for cyclone dust separators." In Proc. of 2014 Genetic and Evolutionary Computation Conference, New York, NY, USA, pp. 1223-1230, 2014. https://doi.org/10.1145/2576768.2598260
A. Zamuda and J. D. Hernández Sosa, "Success history applied to expert system for underwater glider path planning using differential evolution," Expert Systems with Applications, vol. 119, pp. 155-170, April 2019. https://doi.org/10.1016/j.eswa.2018.10.048
A. Zamuda and E. Lloret, "Optimizing data-driven models for summarization as parallel tasks," Journal of Computational Science, vol. 42, 41 pages, April 2020. https://doi.org/10.1016/j.jocs.2020.101101
A. Zamuda and J. Brest, "Vectorized procedural models for animated trees reconstruction using differential evolution," Information Sciences, vol. 278, pp. 1-21, 2014. https://doi.org/10.1016/j.ins.2014.04.037
C. Zavoianu, "Enhanced evolutionary algorithms for solving computationally-intensive multi-objective optimization problems," Jan. 2015. https://www.flll.jku.at/sites/default/files/u27/Zavoianu%20Ciprian%20-%20PhD%20Thesis.pdf

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