A Modified Particle Swarm Optimization Algorithm with Dynamic Adaptive

Abstract

To overcome premature searching by standard particle swarm optimization (PSO) algorithm for the large lost in population diversity, the measure of population diversity and its calculation are given, and an adaptive PSO with dynamically changing inertia weight is proposed. Simulation results show that the adaptive PSO not only effectively alleviates the problem of premature convergence, but also has fast convergence speed for balancing the trade-off between exploration and exploitation.