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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 ales.zamuda@um.si | |
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 |