改进的麻雀搜索优化算法及其应用

麻雀搜索算法SSA在求解目标函数最优解时，存在种群多样性不丰富，易陷于局部最优，多维函数求解精度差等问题，针对这些问题提出改进的麻雀搜索算法ISSA。首先，利用反向学习策略初始化种群，增加种群多样性；然后，对步长因子进行动态调整，提高算法的求解精度；最后，在侦查预警的麻雀位置更新公式中引入Levy飞行，提高算法寻优能力和跳出局部极值的能力。将ISSA、SSA和其他算法在8个测试函数上进行求解，并进行秩和检验，仿真结果表明，ISSA具有更高的寻优性能。还将ISSA应用到认知无线电的频谱分配中，实验结果表明，ISSA的系统效益和公平性优于其他算法，验证了ISSA在实际应用中的可行性。

An improved sparrow searchoptimization algorithm and its application

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.