# Particle Swarm Optimisation for Emotion Recognition Systems: A Decade Review of the Literature
## 1 Introduction
For example, in the study by [11], an artificial bee colony algorithm was used in conjunction with PSO to optimise the control of electrical appliances to minimise operational costs, reduce energy consumption, and maximise comfort levels in indoor spaces. In general, it is considered to be a good choice for practical solutions to optimisation problems when the problem is high-dimensional, is defined by multiple criteria and potentially conflicting constraints, or is of complex combinatorial nature. The PSO algorithm has demonstrated efficiency in various optimization-related applications, and recent technological advancements have the potential to enhance its features by integrating PSO. One such example is the ERS. ERS is a sub-part of artificial intelligence that enables a system to learn and recognise human emotions through different data modalities. Affective computing (AC) is a related field that focuses on developing computers that can understand human behaviour, as introduced by [12]. Over the last decade, researchers have identified the subpart of AI that can be enhanced further using the capabilities of AI machine learning. One of the main highlights that have been suggested is machines and computers identify or learn human emotions [13; 14] to improve the interaction between humans and computers and increase potential future applications. Supported by the field of AC that was introduced by [12], in recent years, researchers identified specific applications, such as the ERS. ERS is an emerging technology that has gained attention and has become an important area of study due to the progress of technology. Commonly, ERS is an embedded technology based on AI that enables machines to recognise human emotions [15]. The ability of machines to identify human emotions has been a focus of researchers, and the development of ERS has resulted from this body of work. ERS is still in the developing stage, and researchers and engineers are working on producing more modalities to enable more real-world applications [16]. ERS has the potential to be a significant technology that enhances AI further in the real world. Indeed, ERS hasthe potential to revolutionise various sectors and bring about significant advancements, namely, towards manufacturing, healthcare, and education. A previous study suggested that the application of ERS across various industries is possible, given the innovations seen thus far [13]. In healthcare, ERS can be used to detect and/or monitor patients' emotional states, manage mental health conditions, and provide personalised care. For example, ERS can be used to detect depression symptoms in patients and alert healthcare providers to intervene and provide timely care [17]. In the automotive industry, ERS can be utilised to improve driving safety by detecting and responding to the emotional state of drivers, such as detecting drowsiness or stress and alerting the driver to take necessary actions [18]. In the education sector, ERS can be used to monitor students' emotional states and provide personalised feedback, tailored learning materials, and interventions to improve learning outcomes [19]. Overall, ERS has the potential to bring about significant improvements and advancements in various sectors, making it an important field of study for researchers and technologists. ERS using PSO involves the use of PSO as an optimisation method to enhance the performance of ERS in recognising human emotions. This approach aims to improve the accuracy and efficiency of ERS by optimising the selection and combination of modalities, feature extraction, and classification algorithms. Hence, an SLR is needed to find a better understanding of ERS using PSO. The major contributions of this paper are summarised below:  
1. The paper focuses on the PSO approach for ERS, highlighting its significance in metaheuristic algorithms and its potential for achieving improved results in ERS. This contribution enhances the understanding of the benefits of PSO, specifically in the context of ERS;
2.