UNAPREÐENJE HIBRIDIZACIJOM METAHEURISTIKA INTELIGENCIJE ROJEVA ZA REšAVANJE PROBLEMA GLOBALNE OPTIMIZACIJE

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UNAPREÐENJE HIBRIDIZACIJOM METAHEURISTIKA INTELIGENCIJE ROJEVA ZA REšAVANJE PROBLEMA GLOBALNE OPTIMIZACIJE

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dc.contributor.advisor Tuba, Milan
dc.contributor.author Bačanin Džakula, Nebojša
dc.date.accessioned 2016-07-29T07:40:28Z
dc.date.available 2016-07-29T07:40:28Z
dc.date.issued 2015-06
dc.identifier.uri http://hdl.handle.net/123456789/4245
dc.description.abstract Hard optimization problems that cannot be solved within acceptable computational time by deterministic mathematical methods have been successfully solved in recent years by population-based stochastic metaheuristics, among which swarm intelligence algorithms represent a prominent class. This thesis investigates improvements of the swarm intelligence metaheuristics by hybridization. During analysis of the existing swarm intelligence metaheuristics in some cases de ciencies and weaknesses in the solution space search mechanisms were observed, primarily as a consequence of the mathematical model that simulates natural process as well as inappropriate balance between intensi cation and diversi cation. The thesis examines whether existing swarm intelligence algorithms for global optimization could be improved (in the sense of obtaining better results, faster convergence, better robustness) by hybridization with other algorithms. A number of hybridized swarm intelligence metaheuristics were developed and implemented. Considering the fact that good hybrids are not created as a random combination of individual functional elements and procedures from di erent algorithms, but rather established on comprehensive analysis of the functional principles of the algorithms that are used in the process of hybridization, development of the hybrid approaches was preceded by thorough research of advantages and disadvantages of each involved algorithm in order to determine the best combination that neutralizes disadvantages of one approach by incorporating the strengths of the other. Developed hybrid approaches were veri ed by testing on standard benchmark sets for global optimization, with and without constraints, as well as on well-known practical problems. Comparative analysis with the state-of-the-art algorithms from the literature demonstrated quality of the developed hybrids and con rmed the hypothesis that swarm intelligence algorithms can be successfully improved by hybridization. en_US
dc.description.provenance Submitted by Slavisha Milisavljevic (slavisha) on 2016-07-29T07:40:28Z No. of bitstreams: 1 phdBacaninNebojsa.pdf: 3813668 bytes, checksum: 69bd89acd386ebe4f83c91be184b873d (MD5) en
dc.description.provenance Made available in DSpace on 2016-07-29T07:40:28Z (GMT). No. of bitstreams: 1 phdBacaninNebojsa.pdf: 3813668 bytes, checksum: 69bd89acd386ebe4f83c91be184b873d (MD5) Previous issue date: 2015-06 en
dc.language.iso sr en_US
dc.publisher Beograd en_US
dc.title UNAPREÐENJE HIBRIDIZACIJOM METAHEURISTIKA INTELIGENCIJE ROJEVA ZA REšAVANJE PROBLEMA GLOBALNE OPTIMIZACIJE en_US
mf.author.birth-date 1983-01-13
mf.author.birth-place Beograd en_US
mf.author.birth-country Srbija en_US
mf.author.residence-state Srbija en_US
mf.author.citizenship Srpsko en_US
mf.author.nationality Srbin en_US
mf.subject.area Računarstvo en_US
mf.subject.keywords metaheuristike inteligencije rojeva, hibridni algoritmi, globalna optimizacija en_US
mf.subject.subarea Veštačka inteligencija en_US
mf.contributor.committee Živković, Miodrag
mf.contributor.committee Dugošija, Đorđe
mf.contributor.committee Yang, Xin-She
mf.university.faculty Mathematical Faculty en_US
mf.document.references 173 en_US
mf.document.pages 225 en_US
mf.document.location Beograd en_US
mf.document.genealogy-project No en_US
mf.university Belgrade University en_US

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