Staff View
Multi-objective optimization algorithms considering objective preferences and solution clusters

Descriptive

TitleInfo (displayLabel = Citation Title); (type = uniform)
Title
Multi-objective optimization algorithms considering objective preferences and solution clusters
Name (ID = NAME001); (type = personal)
NamePart (type = family)
Taboada
NamePart (type = given)
Heidi
DisplayForm
Heidi Taboada
Role
RoleTerm (authority = RUETD)
author
Name (ID = NAME002); (type = personal)
NamePart (type = family)
Coit
NamePart (type = given)
David
Affiliation
Advisory Committee
DisplayForm
David W. Coit
Role
RoleTerm (authority = RULIB)
chair
Name (ID = NAME003); (type = personal)
NamePart (type = family)
Albin
NamePart (type = given)
Susan
Affiliation
Advisory Committee
DisplayForm
Susan L. Albin
Role
RoleTerm (authority = RULIB)
internal member
Name (ID = NAME004); (type = personal)
NamePart (type = family)
Luxhøj
NamePart (type = given)
James
Affiliation
Advisory Committee
DisplayForm
James T. Luxhøj
Role
RoleTerm (authority = RULIB)
internal member
Name (ID = NAME005); (type = personal)
NamePart (type = family)
Chaovalitwongse
NamePart (type = given)
Wanpracha
Affiliation
Advisory Committee
DisplayForm
Wanpracha Chaovalitwongse
Role
RoleTerm (authority = RULIB)
internal member
Name (ID = NAME006); (type = personal)
NamePart (type = family)
Boile
NamePart (type = given)
Maria
Affiliation
Advisory Committee
DisplayForm
Maria Boile
Role
RoleTerm (authority = RULIB)
outside member
Name (ID = NAME007); (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (ID = NAME008); (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2007
DateOther (qualifier = exact); (type = degree)
2007-10
Language
LanguageTerm
English
PhysicalDescription
Form (authority = marcform)
electronic
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xx, 226 pages
Abstract
This thesis presents the development of new methods for the solution of multiple objective problems. One of the main contributions of this thesis is that it presents new approaches that provide a balance between the determination of single solutions and a set of Pareto-optimal solutions. Existing solution methodologies for multiple objective problems can generally be categorized as single solution methods or Pareto optimality methods. However, for many problems and decision-makers, a balanced approach is more appropriate, and this thesis provides new approaches to meet those needs. Other main contributions are that several novel multi-objective evolutionary algorithms are presented, which offer distinct advantages compared to existing algorithms.
Two different new approaches are introduced which can efficiently determine an attractive Pareto set or organize and reduce the size of the Pareto-optimal set. This makes it easier for the decision-maker to comparatively analyze a smaller set of solutions, and finally, select the most desirable one for system implementation.
In the first approach, the developed algorithm has the capability to automatically identify an optimal number of clusters in the Pareto-optimal set and provide the decision-maker with representative solutions of each cluster. The second approach is a method that yields efficient results for any user who can prioritize the objective functions. In this method, the objective functions are ranked ordinally based on their importance to the decision-maker, and a reduced Pareto set is determined based on randomly generated weight sets, reflecting the decision-maker preferences.
Different new multiple objective evolutionary algorithms (MOEAs) are designed as the result of this research and they are described and tested. New ideas have been incorporated into these MOEAs to provide the research community with new alternatives. One of the developed MOEAs is MoPriGA, a multi-objective prioritized genetic algorithm. MoPriGA incorporates the knowledge of the decision-maker objective function preferences directly within the evolutionary algorithm. The idea behind this algorithm is to more intensely focus on the region of the Pareto set of interest to the decision-maker.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references (p. 211-224).
Subject (ID = SUBJ1); (authority = RUETD)
Topic
Industrial and Systems Engineering
Subject (ID = SUBJ2); (authority = ETD-LCSH)
Topic
Mathematical optimization
Subject (ID = SUBJ3); (authority = ETD-LCSH)
Topic
Multiple criteria decision making
Subject (ID = SUBJ4); (authority = ETD-LCSH)
Topic
Evolutionary computation
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17110
Identifier
ETD_307
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3W66M56
Genre (authority = ExL-Esploro)
ETD doctoral
Back to the top

Rights

RightsDeclaration (AUTHORITY = GS); (ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
AssociatedEntity (AUTHORITY = rulib); (ID = 1)
Name
Heidi Taboada Jimenez
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
RightsEvent (AUTHORITY = rulib); (ID = 1)
Type
Permission or license
Detail
Non-exclusive ETD license
AssociatedObject (AUTHORITY = rulib); (ID = 1)
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Back to the top

Technical

Format (TYPE = mime); (VERSION = )
application/x-tar
FileSize (UNIT = bytes)
3637760
Checksum (METHOD = SHA1)
1fd3800042f2ac6d89724bd6d4d4bd2475732e4a
ContentModel
ETD
CompressionScheme
other
OperatingSystem (VERSION = 5.1)
windows xp
Format (TYPE = mime); (VERSION = NULL)
application/x-tar
Back to the top
Version 8.4.8
Rutgers University Libraries - Copyright ©2023