Evolutionary many-objective optimisation based on scalarisation

Khan, Muhammad Burhan 2018, Evolutionary many-objective optimisation based on scalarisation, Ph.D thesis, Institute for Intelligent Systems Research and Innovation, Deakin University.

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Title Evolutionary many-objective optimisation based on scalarisation
Author Khan, Muhammad BurhanORCID iD for Khan, Muhammad Burhan orcid.org/0000-0002-1765-935X
Institution Deakin University
School Institute for Intelligent Systems Research and Innovation
Degree type Research doctorate
Degree name Ph.D
Thesis advisor Johnstone MichaelORCID iD for Johnstone Michael orcid.org/0000-0002-3005-8911
Hanoun, SamerORCID iD for Hanoun, Samer orcid.org/0000-0002-8697-1515
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Date submitted 2018-11-07
Summary This study overcomes the three major difficulties experienced by the existing multi-objective evolutionary algorithms; computational cost, convergence rate, and loss of diversity of solutions, when applied to the optimisation problems with more than three objectives. The outcome comparisons clearly indicate the significance of the proposed work over numerous state-of-the-art multi-objective evolutionary algorithms.
Language eng
Field of Research 091099 Manufacturing Engineering not elsewhere classified
080108 Neural, Evolutionary and Fuzzy Computation
010303 Optimisation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
Description of original 191 p.
Restricted until 2021-01-07
Copyright notice ┬ęThe author
Persistent URL http://hdl.handle.net/10536/DRO/DU:30115793

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Created: Wed, 28 Nov 2018, 13:40:13 EST by Bayne Christine

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