Particle Swarm Optimization with Dynamic Adaptive Inertia Weight

Abstract

Aiming at the premature convergence problem of particle swarm optimization algorithm, a new particle swarm Optimization algorithm with dynamic adaptive inertia weigh was presented to solve the typical multi-peak, high dimensional function optimization problems. The dynamic adaptive strategy was introduced in this new algorithm and the change of inertia weight was formulated as an adjust function of this factor according to its impact on the search performance of the swarm. In each iteration process, the inertia weight was timely changed based on the current the swarm diversity and congregate degree, which provides the algorithm with effective dynamic adaptability. The experiments show that the proposed strategy is effectiveness.