Abstract: The sparrow search algorithm (SSA) has poor population diversity, falls into the local optimum easily and low solution accuracy of multi-dimensional functions when solving the optimal solution of the objective function. To solve these probems, the improved sparrows search optimization algorithm (ISSA) is proposed. Firstly, the population is initialized with the opposition-based learning strategy to increase the population diversity. Secondly, the step factor is dynamically adjusted to improve the solution accuracy of the algorithm. Finally, Levy strategy is introduced into the sparrow position update formula for reconnaissance and early warning to improve the algorithms ability of global search and jumping out of local extremum. ISSA, SSA and other algorithms are tested and perform rank sum test on 8 test functions to evaluate the solution accuracy, and Wilcoxon rank sum test is carried out. The experimental results show that the ISSA has higher searching performance. Meanwhile, ISSA is applied to the spectrum allocation of cognitive radio, the experimental results show that ISSA has better system benefit and fairness than other algorithms, which verifies the feasibility of ISSA in practice.

Key words: sparrow search algorithm, opposition-based learning strategy, Levy strategy, function optimization