# Particle Swarm Optimisation for Emotion Recognition Systems: A Decade Review of the Literature
## 1 Introduction  
Artificial Intelligence (AI) is a system that can perceive its environment and take actions to maximise its chances of success [1]. It has become an essential tool for addressing real-world problems inspired by human-logical thinking. One approach to achieving AI is through the SI field, a subfield of AI that has gained increasing attention due to its ability to solve high-complexity problems within a specific time frame [2]. The SI concept was inspired by natural biological systems that adopt the collective behaviour of an organised group [2]. SI is inspired by natural biological systems that use the collective behaviour of an organised group to solve complex problems [3]. Swarms are vast numbers of simple homogenous agents that interact with their environment, exhibiting decentralised control of global behaviour [2]. Swarm-based techniques use nature-inspired algorithms to produce fast, robust, and cost-effective solutions to complex problems [3]. Social swarms in nature, such as honeybees, bird flocks, and ant colonies, exhibit collective behaviour that can be modelled and adapted to solve complex problems [2, 3]. Inside the SI field are examples of algorithms, such as cuckoo search, flower pollination algorithm and particle swarm, optimisation that have been included to achieve SI results [2]. Such an example from a previous study for SI is the applications of SI towardsair overpressure (AOp) [4] from blasting and causing damage to nearby civilians. Therefore, to predict AOp accurately, ref. [4] used the artificial neural network (ANN), and the prediction was trained using PSO to predict the AOp accuracy [4]. PSO is a swarm-based intelligence that exploits the concept of social behaviour algorithm with a potential solution to a given problem viewed as a particle similar to a flock of birds [3; 5; 6]. Each particle then combines its historical best locations and gradually approaches the objective function optimum solution [3]. The above-mentioned advantages show that PSO is a promising development for optimising solutions to real-world problems [7]. PSO, in computing terms, is also defined as a computational procedure to select the most effective element and optimised it based on the collection of accessible alternatives [8]. Either with increment or minimisation, it is the true operation of the simplest state while constantly screening the input elements associated with an allowed set of accessible alternatives [8]. In the field of structural engineering, PSO is one of the evolutionary computation (EC) techniques used by many researchers [9]. PSO was proposed by [5] in 1995, and it contains memory that allows each member of the swarm to remember and acquire while moving through the searching space [9]. PSO is inspired by social problem-solving and seeks to perform a parallel search for the optimal solution with many individual searches associated with particles and collaborative influencing by joining the best performance of all searches [10]. According to [10], PSO is considered to be a good choice for practical solutions to optimisation problems when the problem is high-dimensional, defined by multiple criteria and potentially conflicting constraints or complex combinatorial nature [3; 11] and the results achieved are based on the particles moving as a swarm in the space of the defined problem searching for the best solution, with the likelihood of finding an optimal or near-optimal solution being high. PSO is a versatile algorithm that has been applied to a wide range of optimisation problems, including power system management [10] and indoor comfort and energy consumption optimisation [11]. PSO is especially useful in practical solutions to optimisation problems that are high-dimensional, defined by multiple criteria and potentially conflicting constraints, or of complex combinatorial nature.