## 2 Related Works
### The Taxonomy of the Methodologies/Methods
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Methodologies/Methods** & **Key Ideas and Its Usage** & **Refs.** \\ \hline
1 & & Ontology: A set of concepts of the problem or knowledge and the relationships between them, which is suitable for describing some domain of interest. & [1; 5; 6; 12; 14] \\  & & The aim here was to define a swarm ontology in terms of basic types of the elements in specific domains and their properties connected to each other by a formal language, such as SysML. & \\ \hline
2 & & Generic swarm architecture: An integrated swarm framework with a composable model abstraction of various specific properties depending on individual autonomous units or agents. & [2; 3; 4] \\  & & The aim here was to develop a method to design a swarm architecture from an initial mission and then various models to be iterated and verified to achieve the desired behavior of the swarm. & \\ \hline
3 & & CPS design paradigm: A new design technique to encompass physical and cyber components to embrace the most appropriate M\&S at the component level and at the overall abstraction level in which the system can address the general mission or specific problem. & [7; 8; 9; 10; 21] \\  & & The aim here was to use appropriate M\&S to facilitate all the phases of the CPS design in MBSE processes, ranging from the conception to the hardware and software design, and then conduct a massive deployment evaluation in a simulation or virtual/real environment. & [10] \\ \hline
4 & & Multi-Paradigm Modeling (MPM): A key approach for CPS design processes to support the coordination of different modeling paradigms, model transformation, and compositional modeling approaches to form the system from specification to verification and validation. & [10] \\  & & The aim here was to associate the conventional methods with model-based designs, in which the specification and design solution are executed and implemented as a domain-specific modeling. & \\ \hline
5 & & Behavior-based system: an AI-based system which forms as a result of the individual behavior of a set of physical components and/or cyber components (e.g., agents) and their interaction in a dynamic context. & \\  & & The aim here was to develop a behavior-based system at a variety of abstraction levels with semantical enrichment, to facilitate the top–down sharing of a set of common concepts and properties from an ontological model and the bottom–up incremental construction of the coupling of sensing and action through behaviors. & [5; 11] \\ \hline
6 & & Discrete Events System Specification: A mathematical formalism to describe hybrid systems including discrete and continuous behaviors, such as a CPS, which combine discrete events, discrete time, and continuous dynamics in a mathematically sound way. & [11; 15; 16] \\  & & The aim here was to provide a model mapping rule to transform discrete and continuous behaviors into general systems models based on the state machine and constraint diagrams of SysML. & \\ \hline
7 & & Multi-Agent-based Simulation (MAS): An emerging simulation technique to address very different individual models ranging from simple (reactive agents) to more complex (cognitive agents) within the unified conceptual framework. & \\ \hline
7 & & The aim here was to develop an autonomous CPS computation model to integrate intelligence by communicating/computing from cyber components and a real-time adaptation via the distributed control of physical components. & [22; 23] \\ \hline \end{tabular}
\end{table} 
Table 1: The taxonomy of related methodologies/methods.