Scout Particle Swarm Optimization

Basit Öğe Kaydı
dc.contributor.authorKoyuncu, Hasan
dc.contributor.authorCeylan, Rahime
dc.date.accessioned2020-03-26T19:07:00Z
dc.date.available2020-03-26T19:07:00Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description6th European Conference of the International-Federation-for-Medical-and-Biological-Engineering (MBEC) -- SEP 07-11, 2014 -- Dubrovnik, CROATIAen_US
dc.description.abstractParticle Swarm Optimization is a robust optimization algorithm proved itself in various technical areas like training of classifiers, image classification and function optimization, etc. It simulates the foraging behaviour of bird swarms. While doing that, it uses velocity and position metrics for directing its particles to food. Concerning this, it has various advantages like high convergence, speedy process capability and a few parameters to be adjusted. But it has a significant disadvantage restricting the performance. This handicap is regeneration of the particle which couldn't improve itself along iterations. Moreover, Artificial Bee Colony Optimization (ABC) is a valuable optimization algorithm imitating the foraging behaviour like PSO. However, ABC uses honey bees grouped as employed bees, onlooker bees and scout bees. The employed bee and onlooker bee phases do the same work with velocity and position concepts in PSO. But, scout bee phase regenerates the useless particles in order to achieve higher performance by upgrading diversity. Therefore, it's seen that addition of scout bee phase into PSO looks like a smart idea. So, in this study, Scout PSO (ScPSO) algorithm is designed which is more effective and useful than PSO. For performance analysis of ScPSO, it was used in training of NN classifier. Furthermore, ScPSO-NN is compared with NN and PSO-NN methods on medical pattern classification. For this purpose, Wisconsin Breast Cancer-Original (WBC), Pima Indian Diabetes (PID), Heart Statlog (HS) and Bupa Liver Disorders (BLD) datasets are used and test process is realized by 10-fold cross validation method. As a result, ScPSO-NN achieves classification accuracies as 97.51% (WBC), 78.13% (PID), 86.30% (HS) and 75.07% (BLD).en_US
dc.description.sponsorshipCroatian Med & Biol Engn Soc, Int Federat Med & Biol Engn, Minist Sci Educ & Sports Republ Croatia, Minist Hlth Republ Croatia, Univ Zagreb, Fac Elect Engn & Comp, European Alliance Med & Biol Engn & Sci, European Cooperat Sci & Technolen_US
dc.identifier.doi10.1007/978-3-319-11128-5_21en_US
dc.identifier.endpage85en_US
dc.identifier.isbn978-3-319-11127-8
dc.identifier.issn1680-0737en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage82en_US
dc.identifier.urihttps://dx.doi.org/10.1007/978-3-319-11128-5_21
dc.identifier.urihttps://hdl.handle.net/20.500.12395/32528
dc.identifier.volume45en_US
dc.identifier.wosWOS:000349454200021en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER-VERLAG BERLINen_US
dc.relation.ispartof6TH EUROPEAN CONFERENCE OF THE INTERNATIONAL FEDERATION FOR MEDICAL AND BIOLOGICAL ENGINEERINGen_US
dc.relation.ispartofseriesIFMBE Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectScout particle swarm optimizationen_US
dc.subjectparticle swarm optimizationen_US
dc.subjectartificial bee colony optimizationen_US
dc.subjecthybrid classifieren_US
dc.titleScout Particle Swarm Optimizationen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu