Primjena evolucijskih algoritama na teške optimizacijske probleme

undergraduate thesis

• Title (english): Solving Hard Optimization Problems Using Evolutionary Algorithms
• Author: Bruno Bandić
• Mentor: Marko Čupić (mentor)
• Committee member: Marko Čupić (predsjednik povjerenstva)
• Committee member: Rudolf Lovrenčić (član povjerenstva)
• Committee member: Karlo Knežević (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2020-08-27, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Rješavanje optimizacijskih problema je danas vrlo važno s obzirom na njihovu primjenu. Evolucijski algoritmi su dobri za njihovo rješavanje, jer su brzi te relativno lagani za implementirati. Evolucijski algoritmi oponašaju prirodne procese da bi došli do rješenja tih problema. Prirodne procese kod evolucijskih algoritama možemo implementirati na više načina, ovisno o problemu. Genetski algoritam je bazični evolucijski algoritam, te prati osnove evolucijskih algoritama. Diferencijski algoritam je nešto kompleksnije, ali je i zato bolje kod težih problema. Bolji je zbog svojeg svojstva da populaciju u početku više mijenja, te brže dolazi do boljih rješenja, a kad je blizu optimalnog rješenja on manje mijenja populaciju da ne bi previše divergirala od rješenja.

Abstract (english)

Solving optimization problem is of great importance today because of their many applications. Evolutionary algorithms are a good solution for solving them, because they are fast and are relatively easy to implement. Evolutionary algorithms mimic natural processes so we can solve optimization problems. Natural processes in evolutionary algorithms can be implemented in multiple ways, dependent on the specific problem. Genetic algorithm is a basic evolutionary algorithm, and it follows it basic principles. The differential algorithm is more complex, but better with more difficult problems. It's better because of it's property that it changes the population more in the beginning, and arrives faster to better solutions, but when it is near to the optimal solution it changes the population less, so it won't diverge.

• Keywords: Optimizacijski problemi; evolucijski algoritmi; genetski algoritam; diferencijski algoritam
• Keywords (english): Optimization problems; evolutinary algorithms; genetic algorithms; differential evolution
• Language: croatian
• URN:NBN: urn:nbn:hr:168:761458
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-14 08:22:51