# Embedded PSO for Solving FJSP on Embedded Environment (Industry 4.0 Era)
## 5 Proposed Technique: Embedded Two-Level PSO-FJSP
## 7 Practical and Managerial Uses of E2L-PSO

Based on the findings obtained after testing on Raspberry Pi B+ and STM32 boards, we can conclude that our technique is preferable for handling optimization difficulties in embedded settings. The following are some examples of practical and managerial applications:

* Home staff scheduling is an example of a healthcare optimization problem. * The difficulty of pattern recognition and image processing. * Applications for detecting medical diseases. * Problems with electrical power transmission optimization. * Heterogeneous wireless sensor network routing and clustering. All cyber-physical systems can support our technique E2L-PSO, which can be used to solve any NP-hard optimization issue. ## 8 Conclusions

In this work, we introduce an embedded adaptive two-level PSO (E2L-PSO) method for the FJSP that is based on the two level PSO algorithm [32]. The E2L-PSO approach was developed to be the standard variant dedicated to the dynamic environment. The experiments were conducted using our E2L-PSO algorithm on Raspberry Pi B+ and STM32F407 VGT6. The results obtained show the efficiency of our metaheuristic method in adapting to the changes of the environment as well as to the CPU needs of the execution equipment. Experiments run using our E2L-PSO algorithm on benchmark problems returned better results than those previously obtained by other metaheuristics in a much smaller amount of CPU time. The high quality of the solutions (MS) is due to the efficient explo \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & [35] & [36] & [37] & [38] & [39] & [40] & Our & Our \\  & PSO & ABC & QAO & GA & HLO-PSO & Hybrid PSO & E2L-PSO & E2L-PSO \\ \hline \multirow{2}{*}{Machine} & Intel Core i7; & Intel Core i3 & n-a & Intel Core i5; & n-a & Intel Core i3; & Raspberry Pi B+ & STM32F407 VGT6 \\  & 16 GB RAM & & 8 GB RAM & & 4 GB RAM & 1 GB RAM & 192 KB \\ \hline Particles & 20 & - & - & - & 100 & 50 & 50 & 50 \\ Iterations & 50 & - & - & - & 500 & 200 & 100 & 100 \\ \hline \(4\times 5\) & 11 & 11 & - & 11 & 11 & - & **11** & **11** \\ \(8\times 8\) & 14 & 14 & - & 14 & 14 & - & **14** & 16 \\ \(10\times 10\) & - & 11 & - & 11 & 7 & - & 7 & 9 \\ MK01 & 40 & 40 & 41 & 40 & 40 & 40 & **39** & **39** \\ MK02 & 29 & 27 & 27 & 27 & 28 & **26** & 27 & 29 \\ MK03 & 204 & 204 & 204 & 204 & 204 & 204 & **204** & **204** \\ MK04 & 66 & 60 & 67 & 60 & 63 & - & **60** & 62 \\ MK05 & 175 & 172 & 176 & 172 & 175 & 173 & 173 & 173 \\ MK06 & 77 & 63 & 62 & 69 & 71 & 61 & 60 & 62 \\ MK07 & 145 & 140 & 144 & 144 & 144 & 141 & **139** & **139** \\ MK08 & 523 & 523 & 523 & 523 & 523 & 523 & **523** & **523** \\ MK09 & 320 & 307 & 314 & 320 & 326 & 307 & **307** & 309 \\ MK10 & 299 & 214 & 214 & 254 & 238 & 204 & **205** & **205** \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Comparison of the E2L-PSO against other works (non PSO-based and PSO-based metaheuristics). ration of the solution space. The low CPU time is due to the use of the adaptive process (iteration reduction). The low machine load (workload) is assured by the appearance of the two-level notion. This is one of the goals achieved with the two-level PSO. All these criteria are ensured in real-time application. In future work, we aim to include the memory reduction and the rescheduling method, developed for the breakdown cases, which should be included with E2L-PSO on an embedded system. We aim to apply this in the fields of sensor networks and IoT. To expand the capability of E2L-PSO, we should use additional machine learning approaches. Conceptualization, Methodology and Validation: R.Z., W.B.D. and S.M. ; software, writing--original draft preparation, review and editing, R.Z. and S.M. ; Supervision, A.J. ; All authors have read and agreed to the published version of the manuscript. This work was supported by the Deanship of Scientific Research, Qassim University. Not applicable. The authors declare no conflict of interest. ## References

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