Change search
ReferencesLink to record
Permanent link

Direct link
Performance differences between multi-objective evolutionary algorithms in different environments
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The time required to find the optimal solution to a problem increases exponentially as thesize and amount of parameters increases. Evolutionary algorithms tackle this problemheuristically by generating better solutions over time. When there is more than oneobjective in a problem, algorithms must generate multiple solutions to fit any preference inspecific objectives. As the amount of objectives increases, the effort required to generategood sets of solutions increases.This study investigated how increasing the amount of objectives impacted fourmulti-objective evolutionary algorithms differently. The algorithms were tested againsttwo different sets of problems with each problem being tested in twenty seven differentcircumstances. The results of these tests were summarized into two different statisticsbased on ranking used to determine if there was any performance changes.The results indicate that some multi-objective evolutionary algorithms havebetter performance against problems with more objectives. The underlying cause andmagnitude in performance difference was not identified.

Abstract [sv]

När storleken och antalet parametrar växer för ett problem växer tidens som krävs för att hitta den optimala lösningen exponentiellt. Evolutionära algoritmer löser detta problem med heuristik genom att generera bättre lösningar iterativt. När problemen har mer än ett mål måste algoritmerna generera flera lösningar för att passa olika preferenser i specifika mål. Mängden arbete som krävs för att generera bra lösningsmängder växer när antalet mål växer för dessa problem.

Denna studie undersökte om ökningen av antalet mål påverkade fyra olika multiobjektiva evolutionära algoritmer olika. Algoritmerna testades mot två olika mängder problem varav varje problem testades under tjugosju olika inställningar. För dessa tester sammanfattades resultat i två olika mätningar baserade algoritmernas rangordning i ett antal mätningar för att komma fram till om det var några skillnader i prestanda.

Resultaten påpekade att vissa multiobjektiva evolutionära algoritmer har bättre prestanda hos problem med fler mål. Den underliggande anledningen och storleken på prestandaskillnaden kartlagdes inte.

Place, publisher, year, edition, pages
National Category
Computer Science
URN: urn:nbn:se:kth:diva-186336OAI: diva2:926967
Available from: 2016-05-18 Created: 2016-05-10 Last updated: 2016-05-18Bibliographically approved

Open Access in DiVA

fulltext(1110 kB)20 downloads
File information
File name FULLTEXT01.pdfFile size 1110 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 20 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 35 hits
ReferencesLink to record
Permanent link

Direct link