# Embedded PSO for Solving FJSP on Embedded Environment (Industry 4.0 Era)
## 2 Previous Works
### Embedded Particle Swarm Optimization  
One of the most significant disadvantages of CPS devices is their high power consumption. It has been proven that making optimal use of the device's memory enhances its energy efficiency. For the resolution of optimization problems in such systems, many metaheuristics are used. In this field, Particle Swarm Optimization (PSO) has long been attracting wide attention from researchers. Many embedded systems have used PSO, such as the navigation of mobile sensors [16], virtual network embedded applications [17],multi-robot (swarm robot) applications [18; 19], hardware software partitioning problem in embedded system design [20], and dynamic virtual cellular manufacturing systems [21]. Ref. [22] presents the physically embedded PSO (pePSO) algorithm, which uses two search techniques. The swarm robots first travel around the search space, taking measurements as they approach their objectives. Second, the idea of trophallactic is converted into an algorithm and used in the search. Trophallactic is the exchange of vomited partly digested food between adults and larvae in social insect colonies. No robot-to-robot communication is used in this method. Furthermore, the robots do not need to be aware of those exact locations. Because the pePSO does not employ a primary agent to control the motions and behaviors of the swarm robots, the volume of information exchange between them is reduced. The authors give a complete overview of chaotic embedded metaheuristic optimization algorithms in [23] and detail their evolution, as well as certain advances, in addition to their integration with various methodologies and applications. Ref. [19] presents a survey on multi-robot search inspired by swarm intelligence, which classifies and discusses the theoretical advantages and downsides of previous investigations. The most appealing strategies are then analyzed and contrasted by highlighting their most important characteristics. Experiments were carried out to compare five state-of-the-art algorithms (robotic Darwinian PSO (RDPSO), extended PSO (EPSO), physically embedded PSO, etc.) for cooperative exploration tasks. The simulated experimental results show the superiority of the RDPSO. In [24], the authors suggest a PSO variation that is integrated with the notion of a micro-genetic algorithm (mGA), dubbed mGA embedded PSO, to conduct feature selection for intelligent face emotion identification. To alleviate the premature convergence problem of traditional PSO, it adds a non-replaceable memory, a small-population secondary swarm, and a collaboration of local exploitation and global exploration search mechanisms. On a complex and diverse MPSoC, the authors of [21] apply a PSO variation to handle mapping and scheduling challenges. All previous studies had only used PSO for the resolution of problems on complex and heterogeneous embedded systems. In a recent study [25], the authors used a PSO and virtual force (VF) algorithm for solving wireless sensor network problems (optimization on the basis of providing perception service and collecting location information). In [26], a chaos-embedded PSO (CEPSO) is used to find the unknown parameters of polymer electrolyte membrane fuel cells (PEMFC) in electric vehicles and unmanned aerial vehicle applications. The authors in [27] propose a hybrid artificial neural network PSO (ANN-PSO) model in the behavior prediction of channel connectors embedded. In the multimedia domain (image processing), PSO [28] is used to optimize the scaling factors and the parameter of the improved discrete fractional angular transform in watermark schemes. A chaos-embedded PSO (CEPS) [29] is introduced, which can improve the interpolation precision and reduce the calculation time of interpolation to realize the fast reconstruction of the target 3D image based on biomechanical characteristics. In the medical imaging and deep learning domain, Ref. [30] provides automation in brain tumor segmentation with an adaptive PSO with noise removal and improved image quality.