Mechanism analysis of non-inertial particle swarm optimization for Internet of Things in edge computing

This paper carries out the mechanism analysis of non-inertial particle swarm optimization. It is a modified algorithm based on a new kinetic equation which is applied to the prediction of non-stationary time series for the internet of things in the edge computing. In order to avoid premature convergence and accelerate convergence rate, different from standard particle swarm optimization, the modified algorithm uses a new kinetic equation to lead particles motion direction, besides generalized opposition-based learning (GOBL) and adaptive elite mutation (AEM) strategies. The work presents the second order standardized recurrence equation for the new kinetic equation whose corresponding characteristic equations can be analyzed via the difference functions to obtain the boundaries of coefficients and its convergence region. Besides, GOBL and AEM strategies are also analyzed to boost global and local convergence of the algorithm as an interpretation. It is illustrated that performance analysis of the algorithm with two well-known test functions. The good performance is further validated from application in edge computing.