Effective Evolutionary Principles for System-of-Systems: Insights from Agent-Based Modeling in Vehicular Networks
System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/12/3/98 |
| _version_ | 1827304934583828480 |
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| author | Junjie Liu Junxian Liu Mengmeng Zhang |
| author_facet | Junjie Liu Junxian Liu Mengmeng Zhang |
| author_sort | Junjie Liu |
| collection | DOAJ |
| description | System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV), serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies. |
| first_indexed | 2024-04-24T17:48:43Z |
| format | Article |
| id | doaj.art-b63aa2ae963a4809bc7a0b2ab4abdb84 |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-04-24T17:48:43Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-b63aa2ae963a4809bc7a0b2ab4abdb842024-03-27T14:05:44ZengMDPI AGSystems2079-89542024-03-011239810.3390/systems12030098Effective Evolutionary Principles for System-of-Systems: Insights from Agent-Based Modeling in Vehicular NetworksJunjie Liu0Junxian Liu1Mengmeng Zhang2National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaNational Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaNational Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, ChinaSystem-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV), serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.https://www.mdpi.com/2079-8954/12/3/98system-of-systemsevolutionary principleagent-based modelinternet of vehicles |
| spellingShingle | Junjie Liu Junxian Liu Mengmeng Zhang Effective Evolutionary Principles for System-of-Systems: Insights from Agent-Based Modeling in Vehicular Networks Systems system-of-systems evolutionary principle agent-based model internet of vehicles |
| title | Effective Evolutionary Principles for System-of-Systems: Insights from Agent-Based Modeling in Vehicular Networks |
| title_full | Effective Evolutionary Principles for System-of-Systems: Insights from Agent-Based Modeling in Vehicular Networks |
| title_fullStr | Effective Evolutionary Principles for System-of-Systems: Insights from Agent-Based Modeling in Vehicular Networks |
| title_full_unstemmed | Effective Evolutionary Principles for System-of-Systems: Insights from Agent-Based Modeling in Vehicular Networks |
| title_short | Effective Evolutionary Principles for System-of-Systems: Insights from Agent-Based Modeling in Vehicular Networks |
| title_sort | effective evolutionary principles for system of systems insights from agent based modeling in vehicular networks |
| topic | system-of-systems evolutionary principle agent-based model internet of vehicles |
| url | https://www.mdpi.com/2079-8954/12/3/98 |
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