Distance based parameter adaptation for differential evolution

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

dc.title	Distance based parameter adaptation for differential evolution	en
dc.contributor.author	Viktorin, Adam
dc.contributor.author	Šenkeřík, Roman
dc.contributor.author	Pluháček, Michal
dc.contributor.author	Kadavý, Tomáš
dc.contributor.author	Zamuda, Aleš
dc.relation.ispartof	2017 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.identifier.isbn	978-1-5386-2725-9
dc.date.issued	2017
utb.relation.volume	2018-January
dc.citation.spage	1
dc.citation.epage	7
dc.event.title	IEEE Symposium Series on Computational Intelligence (IEEE SSCI)
dc.event.location	Honolulu
utb.event.state-en	Hawaii
utb.event.state-cs	Havaj
dc.event.sdate	2017-11-27
dc.event.edate	2017-12-01
dc.type	conferenceObject
dc.language.iso	en
dc.publisher	IEEE
dc.identifier.doi	10.1109/SSCI.2017.8280959
dc.relation.uri	https://ieeexplore.ieee.org/abstract/document/8280959/
dc.subject	differential evolution	en
dc.subject	shade	en
dc.subject	l-shade	en
dc.subject	parameter adaptation	en
dc.subject	scaling factor	en
dc.subject	crossover rate	en
dc.description.abstract	This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adaptation in Success-History based Adaptive Differential Evolution (SHADE), which can be used as a framework to all SHADE-based algorithms. The performance impact of the proposed method is shown on the CEC2015 benchmark set in 10 and 30 dimensions for both SHADE and L-SHADE (SHADE with linear decrease of population size) algorithms.	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1007914
utb.identifier.rivid	RIV/70883521:28140/17:63517060!RIV18-GA0-28140___
utb.identifier.obdid	43877059
utb.identifier.scopus	2-s2.0-85046115423
utb.identifier.wok	000428251402094
utb.source	d-wok
dc.date.accessioned	2018-05-18T15:12:07Z
dc.date.available	2018-05-18T15:12:07Z
dc.description.sponsorship	Grant Agency of the Czech Republic GACR [P103/15/06700S]; 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/2017/004]; Slovenian Research Agency [P2-0041]; COST (European Cooperation in Science Technology) [CA15140]; Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO); High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) [IC406]
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, senkerik, pluhacek, kadavy}@fai.utb.cz Aleš Zamuda Faculty of Electrical Engineering and Computer Science University of Maribor Smetanova 17, 2000 Maribor, Slovenia [email protected]
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] Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE transactions on evolutionary computation, 10(6), 646-657. [4] Qin, A. K., Huang, V. L., & Suganthan, P. N. (2009). Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE transactions on Evolutionary Computation, 13(2), 398-417. [5] Das, S., Abraham, A., Chakraborty, U. K., & Konar, A. (2009). Differential evolution using a neighborhood-based mutation operator. IEEE Transactions on Evolutionary Computation, 13(3), 526-553. [6] Mininno, E., Neri, F., Cupertino, F., & Naso, D. (2011). Compact differential evolution. IEEE Transactions on Evolutionary Computation, 15(1), 32-54. [7] Mallipeddi, R., Suganthan, P. N., Pan, Q. K., & Tasgetiren, M. F. (2011). Differential evolution algorithm with ensemble of parameters and mutation strategies. Applied Soft Computing, 11(2), 1679-1696. [8] Brest, J., Korošec, P., Šilc, J., Zamuda, A., Boškoviü, B., & Mauþec, M. S. (2013). Differential evolution and differential ant-stigmergy on dynamic optimisation problems. International Journal of Systems Science, 44(4), 663-679. [9] 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. [10] Zhang, J., & Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. Evolutionary Computation, IEEE Transactions on, 13(5), 945-958. [11] 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. [12] Guo, S. M., Tsai, J. S. H., Yang, C. C., & Hsu, P. H. (2015, May). A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set. In Evolutionary Computation (CEC), 2015 IEEE Congress on (pp. 1003-1010). IEEE. [13] Awad, N. H., Ali, M. Z., Suganthan, P. N., & Reynolds, R. G. (2016, July). An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems. In Evolutionary Computation (CEC), 2016 IEEE Congress on (pp. 2958-2965). IEEE. [14] Brest, J., Mauþec, M. S., & Boškoviü, B. (2017, June). Single objective real-parameter optimization: Algorithm jSO. In Evolutionary Computation (CEC), 2017 IEEE Congress on (pp. 1311-1318). IEEE.
utb.fulltext.sponsorship	This work was supported by Grant Agency of the Czech Republic – GACR P103/15/06700S, further by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014). Also 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/2017/004. This work was also funded in part by the Slovenian Research Agency, Project No.: P2-0041. 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 IC406, High-Performance Modelling and Simulation for Big Data Applications (cHiPSet).
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; [Zamuda, Ales] Univ Maribor, Fac Elect Engn & Comp Sci, Smetanova 17, SLO-2000 Maribor, Slovenia
utb.fulltext.projects	GACR P103/15/06700S
utb.fulltext.projects	LO1303 (MSMT-7778/2014)
utb.fulltext.projects	CZ.1.05/2.1.00/03.0089
utb.fulltext.projects	IGA/CebiaTech/2017/004
utb.fulltext.projects	P2-0041
utb.fulltext.projects	CA15140
utb.fulltext.projects	IC406