多策略融合的改进麻雀搜索算法研究

Research on Improved Sparrow Search Algorithm Based on Multi Strategy Fusion

针对基本麻雀搜索算法(sparrow search algorithm, SSA)存在路径规划时间长、非最优路径且收敛速度慢、容易陷入局部最优解等问题,提出了一种基于Cubic映射和正余弦搜索策略的SSA。首先,在麻雀搜索算法的基础上,引入Cubic混沌映射,对麻雀种群进行初始化策略,加速种群间的信息交流,提高了物种的多样性;再运用正余弦搜索策略对发现者位置进行更新,有效地降低了产生局部最优的概率,加快收敛的速度;最后通过6个基准测试函数对多策略融合的算法和单一策略的算法进行对比仿真实验。结果表明,该多策略的改进算法在寻优搜索能力上得到了显著的提高,迭代次数少,收敛精度提高且具有较高的实时性和稳定性。

To address the problems of long path planning time, non-optimal path and slow convergence speed, and easy to fall into local optimal solutions in the basic sparrow search algorithm (SSA), a SSA based on Cubic mapping and sine cosine search strategy is proposed. First, based on the sparrow search algorithm, Cubic chaotic mapping is introduced to speed up information exchange between populations and improve species diversity. Initialization strategy for the sparrow population, which accelerates the information exchange between populations and improves the species diversity;Then the sine cosine search strategy is applied to update the discoverer position, which effectively reduces the probability of generating a local optimum and speeds up the convergence;Finally, the simulation experiments are conducted to compare the algorithm of multi-strategy fusion and the algorithm of single strategy by six benchmark test functions. The results show that the multi-strategy improved algorithm has significantly improved the search capability, decreased iterations, improved convergence accuracy and it has higher real-time and stability.