混合策略改进的麻雀搜索算法及其应用

针对麻雀搜索算法（sparrow search algorithm，SSA）在优化过程中易陷入局部最优、寻优精度低等问题，提出了一种混合策略改进的麻雀搜索算法（MSSA）。为了使麻雀个体在搜索空间中能够进行充分搜索，在算法寻优过程中引入存档阶段去接收麻雀发现者向安全区域移动时可能被捕获而残留的位置信息；在算法的迭代过程中对当前最优个体作自适应邻域搜索，通过充分探索优质个体周围的位置信息来增强算法跳出局部最优的能力。通过九个基准测试函数进行性能评估，将MSSA、SSA以及四个改进的麻雀搜索算法：混沌麻雀搜索算法、混合策略改进的麻雀搜索算法、改进的麻雀搜索算法、增强型的麻雀搜索算法进行性能评测比较。实验结果表明MSSA相较于其他对比算法在近80%的测试函数上都有更好的收敛精度和稳定性，并且在Friedman检验中MSSA的排名均获得了第一。最后，将MSSA应用于障碍物环境下的无线传感器网络（wireless sensor network，WSN）覆盖优化问题，MSSA比五个对比算法的覆盖率分别提高了9.77%、4.25%、6.62%、3.02%、7.38%。

Improved sparrow search algorithm with mixed strategy and its application

Aiming at the problems that the sparrow search algorithm(SSA) was prone to fall into local optimum and the optimization accuracy was low in the optimization process, this paper proposed a hybrid strategy improved sparrow search algorithm(MSSA). Firstly, in order to enable individual sparrows to fully search in the search space and increase the accuracy of algorithm optimization, it introduced an archiving stage in the algorithm optimization process to receive the residual position information that might be captured by sparrow producers when they moved to the safe area. Secondly, in the iterative process of the algorithm, it performed an adaptive neighborhood search operation on the current optimal individual, and fully explored the location information around the high-quality individual to enhance the algorithm''s ability to jump out of the local optimum. This paper used 9 benchmark functions to evaluate the MSSA with SSA, and other four improved SSA, which were chaotic sparrow search optimization algorithm(CSSOA), mixed strategy improved sparrow search algorithm(MSSSA), improved sparrow search algorithm(ISSA), and enhanced sparrow search algorithm(ESSA). Experiment result illustrates that MSSA can achieve better convergence accuracy and stability on nearly 80% of the benchmark functions, and MSSA is also ranked first in the Friedman test. This paper also applied MSSA to optimize wireless sensor network(WSN) coverage in an obstacle environment. Compared with other five compared algorithms, MSSA can increase the coverage rates of WSN up 9.77%, 4.25%, 6.62%, 3.02%, and 7.38%, respectively.