Addressing premature convergence with distance based parameter adaptation in SHADE

Viktorin, Adam; Šenkeřík, Roman; Pluháček, Michal; Kadavý, Tomáš

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