# Embedded PSO for Solving FJSP on Embedded Environment (Industry 4.0 Era)
## 2 Previous Works
### Discussion  
Following these brief observations from the literature (presented in Table 1), we count a multitude of studies working on the PSO to solve embedded problems. In fact, we note that the related works have flaws.  
* Most studies do not deal with the use of its algorithms in embedded systems.
* Even compared to the works which envisage the adaptive PSO, the authors speak about the adaptability to a dynamic problem and not to a dynamic environment.
* The related work which uses the PSO to solve an optimization problem in an embedded environment does not place importance on the behaviors of this algorithm. Instead, they used it as a tool only, without taking into account the potential losses of CPU and memory time that we can have. In fact, they do not provide a version of PSO that is adaptable to the change of an on-board system in real time.
* Despite the importance of their contributions, none of these works address the general framework with the five types of constraints which are considered here: (1) the makespan (MS), (2) the machine load constraint (workload), (3) CPU time, (4) memory, (5) and feasibility in real time.  
In this paper, we work on only two criteria: the quality of the solutions (MS) and the CPU time, in an environmental constraint (embedded environments). Both of these criteria are ensured in real-time application.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Publication Year** & **Variant** & **Field of Application** & **Objective** \\ \hline
[16] & 2004 & Basic PSO & Mobile sensors & Optimize the membership functions and rules of the fuzzy logic controller \\ \hline
[17] & 2017 & Sixteen metaheuristics (GA, ACO, PSO...) & Virtual network & Evaluate the Virtual Network Embedding (VNE) process \\ \hline
[18] & 2008 & Basic PSO & Mobile robots & Use PSO embedded into a robot swarm to find a target, in the presence of obstacles \\ \hline
[19] & 2014 & Robotic Darwinian PSO (RDPSO) & Mobile robots & Multi-robot optimization search \\ \hline
[20] & 2008 & Basic PSO & Embedded system design & Hardware software partitioning resolution \\ \hline
[21] & 2009 & Basic PSO & Production planning & Solving an extended model of dynamic virtual cellular manufacturing systems \\ \hline
[22] & 2010 & Physically-embedded PSO (pePSO) & Mobile robots & Multi-robot optimization search \\ \hline
[24] & 2016 & PSO and GA & Facial expression recognition & Conduct feature selection for Intelligent face emotion identification \\ \hline
[25] & 2021 & PSO and virtual force (VF) algorithm & Wireless sensor network & Optimization on the basis of providing perception service and collecting location information \\ \hline
[26] & 2021 & Chaos-embedded PSO (CEPSO) & Electric and aerial vehicle & Used to find the unknown parameters of polymer electrolyte membrane fuel cells (PEMFC). \\ \hline
[27] & 2019 & Hybrid PSO & Artificial neural network & Optimize behavior prediction of dunned connections \\ \hline
[28] & 2019 & Basic PSO & Multimedia, image processing & Optimize the scaling factors and the parameter of the improved discrete fractional angular transform in watermark schemes. \\ \hline
[29] & 2020 & Chaos-embedded PSO (CEPS) & Multimedia, image processing & Improve the interpolation precision and reduce the calculation time of interpolation to realize the fast reconstruction of the target 3D image. \\ \hline
[30] & 2020 & Adaptive PSO & Medical imaging and deep learning & Provide automation in brain tumor segmentation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Previous embedded particle swarm optimization (PSO) works.