The Particle Swarm Optimization (PSO) Algorithm attempts on the use of an improved range for inertia weight, social, and cognitive factors utilizing the Pareto principle. The function exhibits better convergence and search efficiency than PSO algorithms that use conventional linearly varying or exponentially varying inertia weights. It also presents a technique to intelligently navigate the search space around the obtained optima and looks for better optima if available and continue converging with the new values using a velocity restriction factor based on the Pareto principle. The improvised algorithm searches the neighborhood of the global optima while maintaining frequent resets in the position of some particles in the form of a mutation based on its escape probability. The results have been compared and tabulated against popular PSO with conventional weights and it has been shown that the introduced PSO performs much better on various benchmark functions.
H. Mouna, Azhagan, M. S. Mukhil, Radhika, M. N., V, M., and M. Devi, N., “Velocity Restriction-Based Improvised Particle Swarm Optimization Algorithm”, in Progress in Advanced Computing and Intelligent Engineering, Singapore, 2018.