Figure 5. Process flow of PSO-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

<p>PSO is used to identify the best solution from collection of solution. It is a computational<br> method that optimizes a problem by iteratively trying to improve a candidate solution with regard to<br> a given measure of quality. PSO optimizes a problem by having a population of candidate solutions,<br> here dubbed particles, and moving these particles around in the search-space according to simple<br> mathematical formulae over the particle&#39;s position and velocity. Each particle&#39;s movement is<br> influenced by its local best known position but, is also guided toward the best known positions in<br> the search-space, which are updated as better positions are found by other particles. This is expected<br> to move the swarm toward the best solutions. PSO is a metaheuristic as it makes few or no<br> assumptions about the problem being optimized and can search very large spaces of candidate<br> solutions. However, metaheuristic such as PSO do not guarantee an optimal solution is ever found.<br> The following Figure 5 explains the basic flow of PSO process.</p>