Scholars' Mine Doctoral Dissertations Student Theses and Dissertations Summer 2015 Computational intelligence based complex adaptive system-of- systems architecture evolution strategy Siddharth Agarwal Follow this and additional works at: https://scholarsmine.mst.edu/doctoral_dissertations Part of the Computer Sciences Commons, Statistics and Probability Commons, and the Systems Engineering Commons Department: Engineering Management and Systems Engineering Recommended Citation Agarwal, Siddharth, "Computational intelligence based complex adaptive system-of-systems architecture evolution strategy" (2015). Doctoral Dissertations. 2401. https://scholarsmine.mst.edu/doctoral_dissertations/2401 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]. COMPUTATIONAL INTELLIGENCE BASED COMPLEX ADAPTIVE SYSTEM-OF- SYSTEMS ARCHITECTURE EVOLUTION STRATEGY by SIDDHARTH AGARWAL A DISSERTATION Presented to the Faculty of the Graduate School of the MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY in SYSTEMS ENGINEERING 2015 Approved Cihan H. Dagli, Advisor David Enke Abhjit Gosavi Ruwen Qin Robert Paige 2015 Siddhartha Agarwal All Rights Reserved iii ABSTRACT The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving SoS architecture based on the wave model through three key features namely; meta- architecture generation, architecture assessment and architecture implementation. Meta- architectures are generated using evolutionary algorithms and assessed using type II fuzzy nets. The approach can accommodate diverse stakeholder views and convert them to key performance parameters (KPP) and use them for architecture assessment. On the other hand, it is not possible to implement such architecture without persuading the systems to participate into the meta-architecture. To address this issue a negotiation model is proposed which helps the SoS manger to adapt his strategy based on system owners behavior. This work helps in capturing the varied differences in the resources required by systems to prepare for participation. The viewpoints of multiple stakeholders are aggregated to assess the overall mission effectiveness of the overarching objective. A search and rescue mission (SAR) SoS example problem illustrates application of the method. Also a dynamic programing approach can be used for generating meta- architectures based on the wave model. iv ACKNOWLEDGMENTS Foremost, I would like to express my gratitude to my advisor Dr. Cihan H. Dagli for the continuous support of my Ph.D. study and research, for his patience and immense knowledge. Besides my advisor, I would like to thank the rest of my thesis committee: Dr. David Enke, Dr. Abhijit Gosavi, Dr. Ruwen Qin, and Dr. Robert Paige and Louie E. Pape for their encouragement, insightful comments, and hard questions. Most importantly, I would like to thank my wife Neha. Her friendship, care, support, encouragement, patience and unwavering love were undeniably the bedrock upon which the past four years of my life stand. Her immense devotion and love needs no testimony. She has been my motivation and strength since I met her. I am and will forever remain deeply indebted to my parents Mr. Niraj Agarwal and Mrs. Annu Agarwal, for giving birth to me at the first place, for their immeasurable sacrifices, supporting me spiritually and financially throughout my life. They have inculcated in me the right set of values and helped me become a better human being in all walks of life. No achievement for me whether big or small can ever be measured without their greater share of contribution. I wish to thank, my brother-in-law Manjit and his wife Priyanka, my in-laws Shri. Sunil Koolwal and Smt. Kalpana Koolwal, who have brought great joy and satisfaction to my life. Finally I would like to thank my maternal grandmother late Mrs. Usha Mittal for always encouraging and believing in me against all odds. I believe my paternal grandparents late Shri. Prem Chand Agarwal and Smt. Shanta Agarwal have always loved me more than anyone else. Thanks and good wishes to all my SMART lab mates for the stimulating discussions, sleepless nights, and the fun we had. At the same time I would like to thank everyone who has touched me in some way, knowingly or unknowingly, and assisted me reach this mark and I wish them all the best in their future endeavors. This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Systems Engineering Research Center (SERC) under Contract H98230-08-D-0171. SERC is a federally funded University Affiliated Research Center managed by Stevens Institute of Technology. v TABLE OF CONTENTS Page ABSTRACT ....................................................................................................................... iii ACKNOWLEDGMENTS ................................................................................................. iv LIST OF ILLUSTRATIONS ........................................................................................... viii LIST OF TABLES ............................................................................................................. xi SECTION 1. INTRODUCTION ...................................................................................................... 1 1.1 BACKGROUND & MOTIVATION ................................................................... 1 1.2. IMPACT OF THE RESEARCH ......................................................................... 5 1.2.1. Contribution to the State of Systems Engineering Knowledge. ............... 6 1.3. DISSERTATION ORGANIZATION ................................................................ 6 2. LITERATURE REVIEW ON SOS & AUTOMATED NEGOTIATION ................. 8 2.1. SYSTEM-OF-SYSTEMS ................................................................................... 8 2.1.1. Types of System-of-Systems. ................................................................. 10 2.2. LITERATURE REVIEW OF SOS PROJECTS ............................................... 12 2.3. SOS ACQUISITION PROCESS ...................................................................... 15 2.4. ASSESSING SYSTEMS ARCHITECTURE ................................................... 16 2.5. HANDLING MANY OBJECTIVES ................................................................ 17 2.6. AUTOMATED NEGOTIATIONS ................................................................... 19 2.7. IDENTIFYING GAPS IN LITERATURE ....................................................... 22 3. THE INTEGRATED MODEL ................................................................................. 27 3.1. DEFINING THE SOS PROBLEM ................................................................... 27 3.2. INTEGRATED MODEL VARIABLES AND PARAMETERS .................... 30 3.2.1. Wave Model Processes ........................................................................... 31 3.2.2. Flexible Intelligent & Learning Architectures. ...................................... 32 3.3. META-ARCHITECTURE FORMULATION AND GENERATION ............. 37 3.3.1. The Pareto-Box Problem. ....................................................................... 37 3.4. META-ARCHITECTURE ASSESSMENT AND SELECTION .................... 43 3.4.1. Introduction – Fuzzy Logic. ................................................................. .45 vi 3.4.1.1 Type-I fuzzy logic system. ..........................................................46 3.4.1.2 Type-2 fuzzy sets. .......................................................................51 3.5. SOS NEGOTIATION APPROACH................................................................. 55 3.5.1. General Negotiation Protocol ................................................................. 57 3.5.2. Modeling the Opponent .......................................................................... 58 3.5.2.1 Hierarchical clustering. ...............................................................60 3.5.2.2 K-means clustering algorithm. ....................................................62 3.5.2.3 Elbow method. ............................................................................63 3.5.2.4 Training a LVQ network. ............................................................64 3.5.2.5 Radial basis function network. ....................................................66 3.5.3. Making a Decision Based on Current Round of Negotiation ................. 67 3.5.3.1 Ordered weighted averaging operator. ........................................69 3.5.4. Proposing an Offer. ...............................................................................