prikaz prve stranice dokumenta Usporedba rada evolucijskog algoritma i algoritma roja na standardnim testnim funkcijama
  Download
PDF 2.89 MB
undergraduate thesis
Usporedba rada evolucijskog algoritma i algoritma roja na standardnim testnim funkcijama
Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2017. urn:nbn:hr:235:633835
Čehulić, Lovro
University of Zagreb
Faculty of Mechanical Engineering and Naval Architecture

Institutional repository: Repository of Faculty of Mechanical Engineering and Naval Architecture University of Zagreb

Cite this document

Čehulić, L. (2017). Usporedba rada evolucijskog algoritma i algoritma roja na standardnim testnim funkcijama (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:235:633835

Čehulić, Lovro. "Usporedba rada evolucijskog algoritma i algoritma roja na standardnim testnim funkcijama." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2017. https://urn.nsk.hr/urn:nbn:hr:235:633835

Čehulić, Lovro. "Usporedba rada evolucijskog algoritma i algoritma roja na standardnim testnim funkcijama." Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, 2017. https://urn.nsk.hr/urn:nbn:hr:235:633835

Čehulić, L. (2017). 'Usporedba rada evolucijskog algoritma i algoritma roja na standardnim testnim funkcijama', Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, accessed 17 November 2024, https://urn.nsk.hr/urn:nbn:hr:235:633835

Čehulić L. Usporedba rada evolucijskog algoritma i algoritma roja na standardnim testnim funkcijama [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture; 2017 [cited 2024 November 17] Available at: https://urn.nsk.hr/urn:nbn:hr:235:633835

L. Čehulić, "Usporedba rada evolucijskog algoritma i algoritma roja na standardnim testnim funkcijama", Undergraduate thesis, University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Zagreb, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:235:633835

Metadata
TitleUsporedba rada evolucijskog algoritma i algoritma roja na standardnim testnim funkcijama
Title (english)Comparison of evolutionary and swarm algorithm on standard benchmark test functions
AuthorLovro Čehulić
MentorPetar Ćurković (mentor)
GranterUniversity of Zagreb
Faculty of Mechanical Engineering and Naval Architecture
Zagreb
Defense date and country2017-03-03, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Mechanical Engineering
Abstract
Genetski algoritam i optimizacija rojem čestica su metaheuristički optimizacijski alati. Genetski algoritam pripada području evolucijskog računarstva, a inspiriran je evolucijom na genetskoj razini. Optimizacija rojem čestica pripada području inteligencije roja i inspirirana je rojevima kukaca i jatima ptica čija je specijalizacija problem traženja. U ovome radu, ova dva optimizacijska algoritma testirana su na raznim testnim funkcijama te su dobivena dobra rješenja uz određena ograničenja. Odabrane testne funkcije su uglavnom multimodalni, višedimenzijski minimizacijski problemi. Optimizacijom rojem čestica postignuti su rezultati visoke točnosti za probleme koji su uspješno optimizirani. Genetski algoritam dao je rezultate koji nisu tako visoke točnosti, ali su njime pronađena rješenja onih funkcija za koje Optimizacija rojem čestica nije dala rezultate.
Abstract (english)
Genetic Algorithm and Particle Swarm Optimization are Metaheuristic optimization tools. Genetic Algorithm belongs to the field of evolutionary computing, and it is inspired by evolution on genetic level. Particle Swarm Optimization belong to the field of swarm inteligence inspired by swarms of insects and flocks of birds whose specialty is search problem. In this thesis, these two optimization algorithms were tested on various (benchmark) test functions and have obtained good solutions with certain restrictions. The selected test functions are mainly multimodal, multidimensional minimization problems. Particle Swarm Optimization results have been achieved with high precision on the successfully optimize d problems. Genetic Algorithm has provided results with not as high accuracy, but it found solutions for those functions that Particle Swarm Optimization did not.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:235:633835
Study programmeTitle: Mechanical Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka strojarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2019-05-22 19:04:41