Principles for Architecting Adaptable Command and Control Systems

Optimization of architecture design has recently drawn research interest. System deployment optimization (SDO) refers to the process of optimizing systems that are being deployed to activities. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal solutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are minimized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm (HGA), as well as the combination of heuristic method and particle swarm optimization (HPSO). Experiment results show that the HPSO method generates solutions faster than particle swarm optimization (PSO) and genetic algorithm (GA) in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution.

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“…System adaptability is the ability of the system to adapt itself to the environment [24,25]. An SoS consists of logical and physical structures.…”

Section: Computation Of the Cost Of Changes In The System Structurementioning

confidence: 99%

System deployment optimization in architecture design

Zhang

Shu

Luo

et al. 2014

J. of Syst. Eng. Electron.

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“…Simply describing the characteristics, boundaries, expectations, or governance of a SoS is difficult, being fraught with no commonly accepted terms for the new area, little agreement on what constitutes success, nor even a good theory of SoS [24]. The acknowledged SoS that is the focus of this effort only acerbates these problems because of the inherent limits of the responsibility, authority and accountability between the SoS manager and the system program offices who participate in the SoS formation [8] [25]. The literature describing SoS engineering is growing in coverage, but it is still sparse.…”

Section: Representing and Assessing Sos Architecture Informationmentioning

confidence: 99%

“…The literature describing SoS engineering is growing in coverage, but it is still sparse. Pitsko and Verma [25] describe four principles to make a SoS more…”

Section: Representing and Assessing Sos Architecture Informationmentioning

confidence: 99%

An Advanced Computational Approach to System of Systems Analysis & Architecting Using Agent-Based Behavioral Model: Phase 2

Dağli¹

2013

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. REPORT DATE NOV 20132. REPORT TYPE SUPPLEMENTARY NOTES ABSTRACTThe goal of this research is to model the evolution of the architecture of an acknowledged Systems of Systems (SoS) that accounts for the ability and willingness of constituent systems to support the SoS capability development. Since DoD SoS development efforts do not typically follow the program of record acquisition process described in DoDI 5000.02, the Wave Model proposed by Dahmann and Rebovich is used as the basis for this research on SoS capability evolution. The Wave Process Model provides a framework for an agent-based modeling methodology, which is used to abstract the non-utopian behavioral aspects of the constituent systems and their interactions with the SoS. In particular, the research focuses on the impact of individual system behavior on the SoS capability and architecture evolution processes. This material has been approved for public release and unlimited distribution except as restricted below.Internal use:* Permission to reproduce this material and to prepare derivative works from this material for internal use is granted, provided the copyright and "No Warranty" statements are included with all reproductions and derivative works.External use:* This material may be reproduced in its entirety, without modification, and freely distributed in written or electronic form without requesting formal permission. Permission is required for any other external and/or commercial use. Requests for permission should be directed to the Systems Engineering Research Center at dschultz@stevens.edu * These restrictions do not apply to U.S. government entities. WHS TO 029, RT 044 UNCLASSIFIED Executive SummaryA major challenge to the successful planning and evolution of an Acknowledged System of Systems (SoS) is the current lack of understanding of the impact that the presence or absence of a set of constituent systems has on the overall SoS capability. Since the candidate elements of a SoS are fully functioning, stand-alone systems in their own right; they have goals and objectives of their own to satisfy, some of which may compete with those of the overarching SoS. These system-level concerns drive decisions to participate (or not) in the So...

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Principles for Architecting Adaptable Command and Control Systems

Cited by 2 publications

References 14 publications

System deployment optimization in architecture design

System deployment optimization in architecture design

An Advanced Computational Approach to System of Systems Analysis & Architecting Using Agent-Based Behavioral Model: Phase 2

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