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Distance based parameter adaptation for Success-History based Differential Evolution

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dc.title Distance based parameter adaptation for Success-History based 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 Swarm and Evolutionary Computation
dc.identifier.issn 2210-6502 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2019
utb.relation.volume 50
dc.type article
dc.language.iso en
dc.publisher Elsevier B.V.
dc.identifier.doi 10.1016/j.swevo.2018.10.013
dc.relation.uri https://www.sciencedirect.com/science/article/pii/S2210650218303043
dc.subject Differential Evolution en
dc.subject Distance based en
dc.subject Parameter adaptation en
dc.subject Success-History 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 real-parameter single objective optimization (CEC2015 and CEC2017) benchmark sets in 10, 30, 50, and 100 dimensions for all SHADE, L-SHADE (SHADE with linear decrease of population size), and jSO algorithms. The proposed distance based parameter adaptation is designed to address the premature convergence of SHADE–based algorithms in higher dimensional search spaces to maintain a longer exploration phase. This design effectiveness is supported by presenting a population clustering analysis, along with a population diversity measure. Also, the new distance based algorithm versions (Db_SHADE, DbL_SHADE, and DISH) have obtained significantly better optimization results than their canonical counterparts (SHADE, L_SHADE, and jSO) in 30, 50, and 100 dimensional functions. © 2018 Elsevier B.V. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1009431
utb.identifier.obdid 43880003
utb.identifier.scopus 2-s2.0-85057032118
utb.identifier.wok 000497252300020
utb.source j-scopus
dc.date.accessioned 2019-11-20T10:30:42Z
dc.date.available 2019-11-20T10:30:42Z
dc.description.sponsorship Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [101303 (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]; Slovenian Research AgencySlovenian Research Agency - Slovenia [P2-0041]; COST (European Cooperation in Science & Technology), Improving Applicability of NatureInspired Optimization by Joining Theory and Practice (ImAppNIO) [CA15140]; COST (European Cooperation in Science & Technology), High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) [IC1406]
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.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 ), supported by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 , and supported by the Internal Grant Agency of Tomas Bata University under the Project No. IGA/CebiaTech/2018/003 . 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 Optimization by Joining Theory and Practice (ImAppNIO), and Action IC1406 , High-Performance Modelling and Simulation for Big Data Applications (cHiPSet), and further supported by resources of the 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; [Zamuda, Ales] Univ Maribor, Fac Elect Engn & Comp Sci, Koroska Cesta 46, Maribor 2000, Slovenia; [Senkerik, Roman] Ton Duc Thang Univ, Fac Elect & Elect Engn, Modeling Evolutionary Algorithms Simulat & Artifi, Ho Chi Minh City, Vietnam
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, Zlin, 760 01, Czech Republic; Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška Cesta 46, Maribor, 2000, Slovenia; Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
utb.fulltext.projects LO1303
utb.fulltext.projects MSMT-7778/2014
utb.fulltext.projects CZ.1.05/2.1.00/03.0089
utb.fulltext.projects IGA/CebiaTech/2018/003
utb.fulltext.projects P2-0041
utb.fulltext.projects CA15140
utb.fulltext.projects ImAppNIO
utb.fulltext.projects IC1406
utb.fulltext.projects cHiPSet
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