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Addressing premature convergence with distance based parameter adaptation in SHADE

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dc.title Addressing premature convergence with distance based parameter adaptation in SHADE en
dc.contributor.author Viktorin, Adam
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Pluháček, Michal
dc.contributor.author Kadavý, Tomáš
dc.relation.ispartof 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP)
dc.identifier.issn 2157-8672 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-1-5386-6979-2
dc.date.issued 2018
utb.relation.volume 2018-June
dc.event.title 25th International Conference on Systems, Signals and Image Processing, IWSSIP 2018
dc.event.location Maribor
utb.event.state-en Slovenia
utb.event.state-cs Slovinsko
dc.event.sdate 2018-06-20
dc.event.edate 2018-06-22
dc.type conferenceObject
dc.language.iso en
dc.publisher IEEE Computer Society
dc.identifier.doi 10.1109/IWSSIP.2018.8439609
dc.relation.uri https://ieeexplore.ieee.org/document/8439609
dc.subject differential evolution en
dc.subject SHADE en
dc.subject Db_SHADE en
dc.subject parameter adaptation en
dc.subject premature convergence en
dc.description.abstract In this paper, an analysis of a distance based parameter adaptation in Success-History based Differential Evolution (SHADE) is presented in order to show that it can have a beneficial effect on the premature convergence of the algorithm. The premature convergence of SHADE is an issue mainly in higher dimensional decision spaces. Therefore, the tests are done on the basis of CEC2015 benchmark in 10D, 30D and 50D. The results are provided and discussed. © 2018 IEEE. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1008210
utb.identifier.obdid 43878841
utb.identifier.scopus 2-s2.0-85053147454
utb.identifier.wok 000451277200055
utb.source d-scopus
dc.date.accessioned 2018-10-03T11:13:03Z
dc.date.available 2018-10-03T11:13:03Z
dc.description.sponsorship Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST (European Cooperation in Science Technology) [Action CA15140]; Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO); High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) [Action IC1406]; A.I. Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin
utb.ou CEBIA-Tech
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Kadavý, Tomáš
utb.fulltext.affiliation Adam Viktorin, Roman Senkerik, Michal Pluhacek, Tomas Kadavy Faculty of Applied Informatics Tomas Bata University in Zlin T. G. Masaryka 5555, 760 01 Zlin, Czech Republic {aviktorin, senekrik, pluhacek, kadavy}@utb.cz
utb.fulltext.dates -
utb.fulltext.references [1] Storn, R., & Price, K. (1995). Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces (Vol. 3). Berkeley: ICSI. [2] Das, S., Mullick, S. S., & Suganthan, P. N. (2016). Recent advances in differential evolution–An updated survey. Swarm and Evolutionary Computation, 27, 1-30. [3] Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82. [4] Tanabe, R., & Fukunaga, A. (2013, June). Success-history based parameter adaptation for differential evolution. In Evolutionary Computation (CEC), 2013 IEEE Congress on (pp. 71-78). IEEE. [5] Tanabe, R., & Fukunaga, A. S. (2014, July). Improving the search performance of SHADE using linear population size reduction. In Evolutionary Computation (CEC), 2014 IEEE Congress on (pp. 1658-1665). IEEE. [6] Guo, S. M., Tsai, J. S. H., Yang, C. C., & Hsu, P. H. (2015, May). A selfoptimization approach for L-SHADE incorporated with eigenvectorbased crossover and successful-parent-selecting framework on CEC 2015 benchmark set. In Evolutionary Computation (CEC), 2015 IEEE Congress on (pp. 1003-1010). IEEE. [7] Awad, N. H., Ali, M. Z., Suganthan, P. N., & Reynolds, R. G. (2016, July). An ensemble sinusoidal parameter adaptation incorporated with LSHADE for solving CEC2014 benchmark problems. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 2958-2965). IEEE. [8] Brest, J., Maučec, M. S., & Bošković, B. (2017, June). Single objective real-parameter optimization: Algorithm jSO. In Evolutionary Computation (CEC), 2017 IEEE Congress on (pp. 1311-1318). IEEE. [9] Viktorin, A., Senkerik, R., Pluhacek, M., Kadavy, T., & Zamuda, A. (2017, November). Distance based parameter adaptation for differential evolution. In Computational Intelligence (SSCI), 2017 IEEE Symposium Series on (pp. 1-7). IEEE. [10] Chen, Q., Liu, B., Zhang, Q., Liang, J. J., Suganthan, P. N., & Qu, B. Y. (2014). Problem definition and evaluation criteria for CEC 2015 special session and competition on bound constrained single-objective computationally expensive numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, China and Nanyang Technological University, Singapore, Technical report. [11] Poláková, R., Tvrdík, J., Bujok, P., & Matoušek, R. (2016). Populationsize adaptation through diversity-control mechanism for differential evolution. In MENDEL, 22th International Conference on Soft Computing (pp. 49-56). [12] Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A densitybased algorithm for discovering clusters in large spatial databases with noise. In Kdd (Vol. 96, No. 34, pp. 226-231). [13] Gämperle, R., Müller, S. D., & Koumoutsakos, P. (2002). A parameter study for differential evolution. Advances in intelligent systems, fuzzy systems, evolutionary computation, 10, 293-298. [14] Liu, J., & Lampinen, J. (2002). On setting the control parameter of the differential evolution method. In Proceedings of the 8th international conference on soft computing (MENDEL 2002) (pp. 11-18). [15] Zhang, J., & Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. Evolutionary Computation, IEEE Transactions on, 13(5), 945-958. [16] Deza, M. M., & Deza, E. (2009). Encyclopedia of distances. In Encyclopedia of Distances (pp. 1-583). Springer, Berlin, Heidelberg.
utb.fulltext.sponsorship This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014), further by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2018/003. This work is also based upon support by COST (European Cooperation in Science & Technology) under Action CA15140, Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO), and Action IC1406, High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). The work was further supported by resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).
utb.wos.affiliation [Viktorin, Adam; Senkerik, Roman; Pluhacek, Michal; Kadavy, Tomas] Tomas Bata Univ Zlin, Fac Appl Informat, TG Masaryka 5555, Zlin 76001, Czech Republic
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, Zlin, 76001, Czech Republic
utb.fulltext.projects LO1303 (MSMT-7778/2014)
utb.fulltext.projects CZ.1.05/2.1.00/03.0089
utb.fulltext.projects IGA/CebiaTech/2018/003
utb.fulltext.projects CA15140
utb.fulltext.projects ImAppNIO
utb.fulltext.projects IC1406
utb.fulltext.projects cHiPSet
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