Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83265
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAwad, Noor Hussien Alien
dc.date.accessioned2019-04-11T02:52:05Zen
dc.date.accessioned2019-12-06T15:18:45Z-
dc.date.available2019-04-11T02:52:05Zen
dc.date.available2019-12-06T15:18:45Z-
dc.date.issued2019en
dc.identifier.citationAwad, N. H. A. (2019). Numerical optimization using differential evolution. Doctoral thesis, Nanyang Technological University, Singapore.en
dc.identifier.urihttps://hdl.handle.net/10356/83265-
dc.description.abstractEngineers and scientists from all disciplines often have to tackle numerous real- world applications. Developing efficient evolutionary algorithms for this target has attracted many researchers due to the fact that many real-world applications can be stated as optimization problems. Differential evolution (DE) has become one of the most effective metaheuristics during the last decade, due to its ability to solve complex optimization problems with diverse characteristics. In this thesis, novel efficient differential evolution variants that can be successfully applied to solve numerical optimization problems are studied. The aim is to develop new improved differential evolution algorithms through mitigating well- known problems that DE suffers from, such as easily getting stuck in local optima, and being easily influenced by the choice of its control parameters. Such improvements should empower these new variants to solve challenging optimization problems efficiently when compared to other existing state-of-the- art algorithms. Different ideas were employed in building such new variants such as: hybridizations that combine the strengths of different canonical algorithms, new ensemble control parameter settings, an improved crossover strategy that is used to build a suitable coordinate system during the search and an assistant surrogate model to mimic the response of the objective function. To validate the performance of the developed algorithms, different challenging test suites from recently developed IEEE-CEC benchmarks were used. Those benchmarks are among the widely used benchmarks by many researchers to test their developed algorithms. Each of them constitutes problems that are tested on different dimensionalities, with a various set of problem features and characteristics, including ruggedness, noise in fitness, multimodality, ill-conditioning, interdependence and non-separability. Moreover, a variety of real-world optimization problems taken from diverse fields are also used. The results of the comparative study statistically affirm the efficiency of the proposed approaches to obtain better results compared to other state-of-the-art algorithms from the literature.en
dc.format.extent219 p.en
dc.language.isoenen
dc.subjectDRNTU::Engineering::Computer science and engineering::Theory of computation::Analysis of algorithms and problem complexityen
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleNumerical optimization using differential evolutionen
dc.typeThesisen
dc.contributor.supervisorPonnuthurai N. Suganthanen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.description.degreeDoctor of Philosophyen
dc.contributor.researchIntelligent Systems Centreen
dc.identifier.doi10.32657/10220/48011en
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:EEE Theses
Files in This Item:
File Description SizeFormat 
Final Revised PhD Thesis _Noor Awad.pdfPhD thesis3.04 MBAdobe PDFThumbnail
View/Open

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.