Computational Intelligence Based Complex Adaptive System-of-System Architecture Evolution Strategy

Agarwal, Suneeta; Dağli, Ci̇han H.; Pape, Louis

doi:10.1007/978-3-319-26109-6_9

Cited by 4 publications

(1 citation statement)

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“…SoS is a set of dedicated systems that have fetched their abilities to create a new complicated system, providing more performance and services than individual systems (Popper, Bankes et al 2004). Organizations are continually facing challenges to co-integrate new Component Systems (CS) and update existing systems over a while under threats, restricted budget, and uncertainty (Agarwal, Dagli et al 2016). Both complexity and uncertainty are inherent features of infrastructure SoS, which can drive the operation of such systems away from their intended purposes (Peculis and Shirvani 2017).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #3 of "Enhancing semantic belief function to handle decision conflicts in SoS using k-means clustering (v0.2)"

2021

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Background:The endeavouring to offer complex special functions from individual systems gave rise to what is known as the System of Systems (SoS). SoS co-integrating systems together while allowing for absorbing more systems in the future. SoS as an integrated system simplifies operations, reduces costs, and ensures efficiency. However, conflict may result while co-integrating systems, violating the main benefits of SoS. This paper is concerned with enhancing the time required to detect and solve such conflicts.Methods: We adopted the k-means clustering technique to enhance the detection and solving of conflict resulting while co-integrating new systems into an existing SoS. Instead of dealing with SoS as a single entity, we partition it into clusters. Each cluster contains nearby systems according to pre-specified criteria. We can consider each cluster a Sub SoS (S-SoS). By doing so, the conflict that may arise while co-integrating new systems can be detected and solved in a shorter time. We propose the Smart Semantic Belief Function Clustered System of Systems (SSBFCSoS) which is an enhancement of the Ontology Belief Function System of Systems (OBFSoS). Results:The proposed method proved the ability to rapidly detect and resolve conflicts. It showed the ability to accommodate more systems as well, therefore, achieving the objectives of SoS. In order to test the applicability of the SSBFCSoS and compare its performance with other approaches, two datasets were employed. They are (Glest & StarCraft Brood War). With each dataset, 15 test cases were examined. We achieved, on average, 89% in solving the conflict compared to 77% for other approaches. Moreover, it showed an acceleration of up to proportionality over previous approaches for about 16% in solving conflicts as well. Besides, it reduced the frequency of the same conflicts by approximately 23% better than the other method, not only in the same cluster but even while combining different clusters.

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Section: Introductionmentioning

confidence: 99%