Hybrid quantum particle swarm algorithm based on Lévy flights

In order to diversify the particle swarm during the searching process of quantum particle swarm optimization (QPSO) and avoid the algorithm being trapped into premature easily, a hybrid quantum particle swarm optimization algorithm based on Lévy flights is proposed in this paper. The new algorithm effectively takes advantage of quantum computing and Lévy flights. We use the probability amplitude encoding method of the quantum bit to initialize the particle position and combine the potential well particle updating formula with the quantum rotation gate to update the particle swarm, which effectively ameliorates the search process and increases the population diversity. Then the Lévy flights strategy is employed to improve the population variation process and enhance the quality of the solution while preventing the algorithm from falling into the precocious convergence. Compared with other algorithms on benchmark functions, it is shown that the algorithm is effective and feasible. © 2020 Computer Society of the Republic of China. All rights reserved.