Hybrid linear and nonlinear weight Particle Swarm Optimization algorithm

Abstract

The inertia weight is an important parameter in the Particle Swarm Optimization algorithm, which controls the degree of influence of the contemporary speed to the next generation and plays a role of balancing global search and local search. In the iteration process, the inertia weight will decrease nonlinearly at the early stage and decrease linearly at the later stage. The improved algorithm will effectively prevent premature convergence of the algorithm. The simulation results show that the improved algorithm is superior to the particle swarm optimization algorithm of the linear decreasing weight.