Doctoral Dissertations

Keywords and Phrases

Artificial Intelligence; Evolutionary Algorithms; Genetic Algorithms; Heuristic Optimization; Multiobjective Optimization; Nondominated Sorting

Abstract

“Applicable to most real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set of Pareto optimal solutions wherein no solution unanimously optimizes all objectives. Evolutionary algorithms have emerged as a standard approach to determine a set of these Pareto optimal solutions, from which a decision-maker can select a vetted alternative. While easy to implement and having demonstrated great efficacy, these evolutionary approaches have been criticized for their runtime complexity when dealing with many alternatives or a high number of objectives, effectively limiting the range of scenarios to which they may be applied. This research introduces mechanisms to improve the runtime complexity of many multiobjective evolutionary algorithms, achieving state-of-the-art performance, as compared to many prominent methods from the literature. Further, the investigations here presented demonstrate the capability of multiobjective evolutionary algorithms in a complex, large-scale optimization scenario. Showcasing the approach’s ability to intelligently generate well-performing solutions to a meaningful optimization problem.

These investigations advance the concept of multiobjective evolutionary algorithms by addressing a key limitation and demonstrating their efficacy in a challenging real-world scenario. Through enhanced computational efficiency and exhibited specialized application, the utility of this powerful heuristic strategy is made more robust and evident”--Abstract, page iv.

Advisor(s)

Long, Suzanna, 1961-
Kwasa, Benjamin J.

Committee Member(s)

Dagli, Cihan H., 1949-
Corns, Steven
Nadendla, V. Sriram Siddhardh

Department(s)

Engineering Management and Systems Engineering

Degree Name

Ph. D. in Systems Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2022

Journal article titles appearing in thesis/dissertation

  • A Geometrically-Based Method for Efficient Many-Objective Decision-Making
  • Ideal Sort: A Terminable, Efficient Nondominated Sorting Algorithm
  • Disaster Recovery Strategy Generation via Multiobjective Heuristic Optimization

Pagination

xiv, 180 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2022 Samuel Alexander Vanfossan, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12139

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