Quantum-behaved particle swarm optimization algorithm with inverse-proportional inertia weight
Xin Zheng, Qiang Li · 2010
In order to improve the performance of particle swarm optimization algorithm and avoid trapping to local excellent situations, this paper presents a new quantum behaved particle swarm optimization algorithm with inverse proportional inertia weight. By the inverse proportional inertia weight function, with the number of iterations increasing, the value of inertia weight function decreasing, the algorithm can keep the searching capability in the early iteration and make the convergence accelerate in later iteration. Testing experiments show this new algorithm's merits not only having the global optimization performance but also raising capability for convergence speed and better quality solutions.