An efficient oscillating inertia weight of particle swarm optimisation for tracking optima in dynamic environments

One of the effective techniques for improving the rate of convergence in the particle swarm optimisation (PSO) is modifying the inertia weight parameter. This parameter can specify the search area of the swarm in the environment and establish a good balance between the global and local search ability of the particles. Several strategies have been already suggested and well tested for setting the inertia weight in static environments. However, in dynamic environments, the effect of this parameter on increasing the ability of PSO in tracking the changing optimum has been barely considered. In this paper, a time-varying inertia weight, called oscillating triangular inertia weight, is presented and its performance is measured on the moving peaks benchmark (MPB). Experimental results on various dynamic scenarios generated by MPB demonstrate that the proposed strategy has a better capability to adapt with the environmental changes in comparison with other techniques including constant inertia weight and linearly decreasing inertia weight.