Improved Quantum-behaved Particle Swarm Optimization Algorithm and Its Application

To enhance the performance of quantum-behaved particle swarm optimization, we propose an improved algorithm that uses quantum potential as well as an optimization mechanism and establishes the center of the potential well. In each iteration, we first calculate the fitness of each particle and then take the first K particles with the greatest fitness as the candidate set. We use a roulette strategy to select a particle from the candidate set as the center of the potential well and move other particles toward the center of the well. During optimization, the K value is monotonically decreased to achieve a balance between exploration and exploitation. We apply the proposed approach in the extremum optimization of benchmark functions and weight optimization of a quantum-inspired neural network. Experimental results demonstrate that the optimization ability of the proposed algorithm is quite competitive with that of the original algorithm.