A new particle swarm optimization with a dynamic inertia weight for solving constrained optimization problems

This study has presented an enhanced particle swarm optimization approach which is designed to solve constrained optimization problems. The approach incorporates a dynamic inertia weight in order to help the algorithm to find the global and overcome the problem of premature convergence to local optima. The inertia weight of every individual is dynamically controlled by the Euclidean distance between individual and global best individual. The approach was tested with a well-known benchmark. Simulation results show that the suitability of the proposed algorithm in terms of effectiveness and robustness.