Abstract:

For the shortcomings of falling into the local optimum easily and slow convergence in Sparrow Search Algorithm （SSA）， a Sparrow Search Algorithm based on Sobol sequence and Crisscross strategy （SSASC） was proposed. Firstly， the Sobol sequence was introduced in the initialization stage to enhance the diversity and ergodicity of the population. Secondly， the nonlinear inertia weight in exponential form was proposed to improve the convergence efficiency of the algorithm. Finally， the crisscross strategy was applied to improve the algorithm. In specific， the horizontal crossover was used to enhance the global search ability， while the vertical crossover was used to maintain the diversity of the population and avoid the algorithm from trapping into the local optimum. Thirteen benchmark functions were selected for simulation experiments， and the performance of the algorithm was evaluated by Wilcoxon rank sum test and Friedman test. In comparison experiments with other metaheuristic algorithms， the mean and standard deviation generated by SSASC are always better than other algorithms when the benchmark functions extending from 10 dimensions to 100 dimensions. Experimental results show that SSASC achieves certain superiority in both convergence speed and solution accuracy.

Key words: Sparrow Search Algorithm (SSA), Sobol sequence, inertia weight, crisscross strategy, nonlinear strategy, benchmark function

摘要：

针对麻雀搜索算法（SSA）容易陷入局部最优、收敛速度较慢等问题，提出一种基于Sobol序列和纵横交叉策略的麻雀搜索算法（SSASC）。首先，在初始化阶段引入类随机采样方法中的Sobol序列，以增强种群的多样性和遍历性；其次，提出一种指数形式的非线性惯性权重，从而提高算法的收敛效率；最后，应用纵横交叉策略对算法进行改进，即利用横向交叉增强全局搜索能力，利用纵向交叉保持种群的多样性并防止算法陷入局部最优。选取了13个基准函数进行仿真实验，同时使用Wilcoxon秩和检验和Friedman检验评价算法的性能。在与其他元启发式算法的对比实验中，将基准函数从10维扩展到100维，SSASC在平均值和标准差处始终优于其他算法。实验结果表明，该算法在收敛速度和求解准确度方面均取得了一定的优势。

关键词: 麻雀搜索算法, Sobol序列, 惯性权重, 纵横交叉策略, 非线性策略, 基准函数