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Multi-objective optimization algorithms considering objective preferences and solution clusters
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Title
Multi-objective optimization algorithms considering objective preferences and solution clusters
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Taboada
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Heidi
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Heidi Taboada
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Coit
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David
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Advisory Committee
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David W. Coit
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Albin
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Susan
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Advisory Committee
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Susan L. Albin
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Luxhøj
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James
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Advisory Committee
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James T. Luxhøj
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Chaovalitwongse
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Wanpracha
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Advisory Committee
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Wanpracha Chaovalitwongse
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internal member
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Boile
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Maria
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Advisory Committee
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Maria Boile
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Rutgers University
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Graduate School - New Brunswick
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theses
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2007
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2007-10
Language
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English
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electronic
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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.
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
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Ph.D.
Note
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Includes bibliographical references (p. 211-224).
Subject
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Industrial and Systems Engineering
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Topic
Mathematical optimization
Subject
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Topic
Multiple criteria decision making
Subject
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Topic
Evolutionary computation
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17110
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ETD_307
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doi:10.7282/T3W66M56
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ETD doctoral
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The author owns the copyright to this work.
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Copyright protected
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Open
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Heidi Taboada Jimenez
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Rutgers University. Graduate School - New Brunswick
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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.
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