Particle Swarm Optimization Algorithm with Adaptive Threshold Mutation

Aiming at the phenomenon of premature convergence and later period oscillatory occurrences, an adaptive particle swarm optimization algorithm with the changes of the population diversity was proposed. In the algorithm, the adaptive exponent decreasing inertia weight and a dynamic adaptive changing threshold were proposed, the satisfied particle of threshold will be mutation by the average distance of particle. Adaptive adjustment of the threshold and the mutation can enhance the algorithm escape from local optima. The results show that the new algorithm of the global search capability has been improved, effectively avoid the premature convergence and later period oscillatory occurrences.