dc.creator | Čisar, Petar | |
dc.creator | Maravić Čisar, Sanja | |
dc.creator | Popović, Brankica | |
dc.creator | Kuk, Kristijan | |
dc.creator | Vuković, Igor | |
dc.date.accessioned | 2023-07-26T08:29:32Z | |
dc.date.available | 2023-07-26T08:29:32Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | http://jakov.kpu.edu.rs/handle/123456789/1446 | |
dc.description.abstract | This paper deals with the application of artificial immune networks in continuous
function optimizations. The performance of the immunological algorithms is analyzed using
the Optimization Algorithm Toolkit. It is shown that the CLIGA algorithm has, by far, the
fastest convergence and the best score - in terms of the number of required iterations, for the
analyzed continuous function. Also, based on the test results, it was concluded, that the lowest
total number of iterations for the defined run time was achieved with the opt-IA algorithm,
followed by the CLONALG and CLIGA algorithms. | sr |
dc.language.iso | en | sr |
dc.publisher | Budapest : Óbuda University | sr |
dc.rights | restrictedAccess | sr |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.source | Acta Polytechnica Hungarica | sr |
dc.subject | artificial immune networks | sr |
dc.subject | Optimization Algorithm Toolkit | sr |
dc.subject | continuous function optimization | sr |
dc.subject | performance | sr |
dc.title | Application of Artificial Immune Networks in Continuous Function Optimizations | sr |
dc.type | article | sr |
dc.rights.license | BY | sr |
dc.citation.volume | 19 | |
dc.citation.issue | 7 | |
dc.citation.spage | 153 | |
dc.citation.epage | 164 | |
dc.identifier.doi | 10.12700/APH.19.7.2022.7.8 | |
dc.identifier.scopus | 2-s2.0-85138697611 | |
dc.type.version | publishedVersion | sr |