摘要
通过多种策略对基本麻雀搜索算法(SSA)进行改进,以解决麻雀搜索算法后期由于种群多样性丢失而导致的全局优化精度和速度问题。首先,改进无限折叠迭代映射(ICMIC)初始化种群,将自适应分段步长因子引入麻雀探测器的位置更新公式中,使麻雀搜索算法观察者的固定比例系数随迭代次数动态变化。然后,将观察者的位置与新公式和正弦余弦算法(SCA)相结合,并干扰先前的观察者步长。最后,在基准测试函数上比较了改进的麻雀搜索算法(ISSA)、麻雀搜索算法(SSA)、鲸鱼算法(WOA)、灰狼算法(GWO)、改进的灰狼算法(CGWO)、正弦余弦算法(SCA)和粒子群优化算法(PSO)的收敛性和准确性,并将其应用于路径规划。实验表明改进的麻雀搜索算法具有良好的优化性能。
The basic sparrow search algorithm(SSA)has been improved through various strategies to solve the global optimization accuracy and speed problems caused by the loss of population diversity in the later stage of sparrow search algorithm.First,the population was initialized by the improved infinite folding iterative mappin(ICMIC),and the adaptive segmented step factor was introduced into the position update formula of the sparrow detector,which made the fixed proportion coefficient of the observer of the sparrow search algorithm changed dynamically with the number of iterations.Second,the observer s position was combined with the new formula and sine cosine algorithm(SCA),and the previous observer s step size was interfered.And finally,the convergence and accuracy of the improved sparrow search algorithm(ISSA),sparrow search algorithm(SSA),whale algorithm(WOA),grey wolf algorithm(GWO),improved grey wolf algorithm(CGWO),sine cosine algorithm(SCA)and particle swarm optimization algorithm(PSO)were compared on the benchmark function,and applied to path planning.Experiments showed that the improved sparrow search algorithm had good optimization performance.
作者
杨红
杨超
YANG Hong;YANG Chao(School of Information Engineering,Shenyang University,Shenyang 110044,China)
出处
《沈阳大学学报（自然科学版）》
CAS
2024年第2期141-152,共12页
Journal of Shenyang University：Natural Science
基金
辽宁省“兴辽英才计划”(XLYC1807138)
沈阳市“中青年科技创新人才”支持计划(RC200530)。
关键词
麻雀搜索算法
无限折叠迭代混沌映射
自适应惯性权重
正余弦算法
路径规划
sparrow search algorithm
infinite folding iterative chaotic map
adaptive inertia weight
sine cosine algorithm
path planning
