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Application of Computational Intelligence to Explore and Analyze System Architecture and Design Alternatives

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Application of Computational Intelligence to Explore and Analyze System Architecture and Design Alternatives Scholars' Mine Doctoral Dissertations Student Theses and Dissertations Fall 2019 Application of computational intelligence to explore and analyze system architecture and design alternatives Gene Lesinski Follow this and additional works at: https://scholarsmine.mst.edu/doctoral_dissertations Part of the Systems Engineering Commons Department: Engineering Management and Systems Engineering Recommended Citation Lesinski, Gene, "Application of computational intelligence to explore and analyze system architecture and design alternatives" (2019). Doctoral Dissertations. 2837. https://scholarsmine.mst.edu/doctoral_dissertations/2837 This thesis is brought to you by Scholars' Mine, a service of the Missouri S&T Library and Learning Resources. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [email protected]. APPLICATION OF COMPUTATIONAL INTELLIGENCE TO EXPLORE AND ANALYZE SYSTEM ARCHITECTURE AND DESIGN ALTERNATIVES by EUGENE JOSEPH LESINSKI III A DISSERTATION Presented to the Faculty of the Graduate Faculty of the MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY in SYSTEMS ENGINEERING 2019 Approved by: Steven Corns, PhD, Advisor Suzanna Long, PhD Cihan Dagli, PhD Stephen Raper, PhD Donald Wunsch, PhD John Farr, PhD 2019 Eugene Joseph Lesinski III All Rights Reserved iii PUBLICATION DISSERTATION OPTION This dissertation consists of four peer reviewed publications that have been published, accepted, or that will be submitted, which have been formatted in the style used by the Missouri University of Science & Technology. Paper I: Pages 13-29 is a paper that has been published in IEEE Conference on Evolutionary Computation. Paper II: Pages 30-52 is a paper that has been published in Complex Adaptive Systems Conference with Theme: Engineering Cyber Physical Systems. Paper III: Pages 53-80 is a paper that has been submitted to Industrial and Systems Engineering Review. Paper IV: Pages 81-99 is a paper that is pending submission to IEEE Transactions on Evolutionary Computation. iv ABSTRACT Systems Engineering involves the development or improvement of a system or process from effective need to a final value-added solution. Rapid advances in technology have led to development of sophisticated and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an expansive design parameter solution space. This research extends the existing knowledge base on multi-objective system design through the creation of a framework to explore and analyze system design alternatives employing computational intelligence. The first research contribution is a hybrid fuzzy-EA model that facilitates the exploration and analysis of possible SoS configurations. The second contribution is a hybrid neural network-EA in which the EA explores, analyzes, and evolves the neural network architecture and weights. The third contribution is a multi-objective EA that examines potential installation (i.e. system) infrastructure repair strategies. The final contribution is the introduction of a hierarchical multi-objective evolutionary algorithm (MOEA) framework with a feedback mechanism to evolve and simultaneously evaluate competing subsystem and system level performance objectives. Systems architects and engineers can utilize the frameworks and approaches developed in this research to more efficiently explore and analyze complex system design alternatives. v ACKNOWLEDGEMENTS First and foremost, I would like to thank my advisor, Dr. Steven Corns, for taking on the challenge of mentoring a distance student and for providing constant support and throughout this endeavor. His Computational Intelligence experience and knowledge were critical in guiding me through multiple hurdles over the course of this study. I would also like to say thanks to the faculty and staff of the Department of Engineering Management and Systems Engineering and members of my committee for their support. Thanks to Dr. Long for her critical insights and encouragement. I would like to thank Dr. Dagli and Dr. Wunsch for their exceptional instruction and innovative pedagogy which inspired me to pursue this line of research. Dr. Farr, thanks for being a great mentor and for challenging me to pursue this degree. I would like to thank my PhD student peers for their friendship, support and collaboration. I will be forever indebted to my wife, Kara, for being patient, enduring years of late nights, lost weekends, and her unquestioning support and encouragement. You were right, I should have done this 15 years ago! vi TABLE OF CONTENTS Page PUBLICATION DISSERTATION OPTION.................................................................... iii ABSTRACT ....................................................................................................................... iv ACKNOWLEDGEMENTS .................................................................................................v LIST OF ILLUSTRATIONS ...............................................................................................x LIST OF TABLES ............................................................................................................ xii NOMENCLATURE ........................................................................................................ xiv SECTION 1. INTRODUCTION ...................................................................................................1 1.1. EVOLUTIONARY ALGORITHMS .............................................................. 4 1.2. MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS ......................... 6 1.3. PARETO DOMINANCE BASED MOEA ..................................................... 6 1.4. NK LANDSCAPE .......................................................................................... 9 1.5. RESEARCH OBJECTIVES AND CONTRIBUTIONS ................................ 9 PAPER I. A FUZZY GENETIC ALGORITHM APPROACH TO GENERATE AND ASSESS META-ARCHITECTURES FOR NON-LINE OF SITE FIRES BATTLEFIELD CAPABILITY ............................................................................13 CHAPTER SUMMARY ..............................................................................................13 1. INTRODUCTION ..................................................................................................14 2. BACKGROUND ....................................................................................................17 vii 3. METHODOLOGY .................................................................................................20 4. RESULTS AND CONCLUSIONS.........................................................................26 REFERENCES ............................................................................................................28 II. MULTI-OBJECTIVE EVOLUTIONARY NEURAL NETWORK TO PREDICT GRADUATION SUCCESS AT THE UNITED STATES MILITARY ACADEMY .............................................................................................. 30 CHAPTER SUMMARY...................................................................................................... 30 1. INTRODUCTION ..................................................................................................31 1.1. RELATED RESEARCH .............................................................................. 32 1.1.1. Graduation Prediction Factors. ............................................................. 33 1.1.2. Graduation Prediction Models. ................................................................... 34 1.2. EVOLUTIONARY ALGORITHMS AND MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS ........................................................... 34 1.3. EVOLUTIONARY NEURAL NETWORKS ............................................... 36 1.4. CURRENT METHODOLOGY EMPLOYED ............................................. 37 2. DATA REQUIREMENTS AND PRE-PROCESSING ..........................................38 3. MODELING APPROACH .....................................................................................40 3.1. REPRESENTATION SCHEME .................................................................. 41 3.2. FITNESS EVALUATION ............................................................................ 42 3.3. SELECTION SCHEME................................................................................ 43 3.4. RECOMBINATION OPERATORS ............................................................. 44 3.5. ALGORITHM PSEUDO-CODE AND MODEL SUMMARY ................... 45 4. RESULTS ...............................................................................................................46 viii 4.1. ALGORITHM BEHAVIOR AND RESULTS ............................................. 47 5. CONCLUSIONS.....................................................................................................49 REFERENCES ............................................................................................................50 III. A PARETO BASED MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM APPROACH TO MILITARY INSTALLATION RAIL INFRASTRUCTURE INVESTMENT ..................................................................53 CHAPTER SUMMARY ..............................................................................................53
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