Quantum firefly algorithm with stochastic search strategies

Abstract

The firefly algorithm (FA) is a popular swarm intelligence optimization algorithm. The FA is used to solve various optimization problems, but it still has some deficiencies, such as high complexity, slow convergence rate, and low accuracy of the solution. This paper proposes a highly efficient quantum firefly algorithm with stochastic search strategies (QSSFA). In QSSFA, individuals are generated in the way of quantum angle coding by introducing the laws of quantum physics and quantum gates, and combined with the random neighborhood attraction model, an adaptive step size strategy is also introduced in the optimization. The complexity of the algorithm is greatly reduced, and the global search ability of the algorithm is optimized. The convergence speed of the algorithm, the ability to jump out of the local optimum, and the algorithm accuracy are improved. The proposed QSSFA’s performance is tested on ten mathematical test functions. The obtained results show that the QSSFA algorithm is very competitive compared to the firefly algorithm and three other FA variants.