融合多策略优化SSA的仓储机器人任务调度算法

Task Scheduling Algorithm for Warehouse Robots Incorporating Multi-Strategy Optimized SSA

针对仓储任务调度过程中机器人任务分配不合理,导致系统运行的时间和路径成本增加的问题,提出了多策略优化麻雀搜索算法的任务分配算法。首先,引入克隆选择机制优化初始种群,提高初始麻雀种群质量;其次,设计自适应蝴蝶更新机制替换发现者位置更新方式,扩大种群搜索范围,平衡全局搜索能力;最后,引入融合变异概率的反向学习策略,解决算法后期陷入局部最优的问题。通过多次实验验证,所提算法在提升机器人任务调度性能方面具有显著的效果。

Aiming at the problem of unreasonable robot task assignment in the storage task scheduling process,which leads to the increase of time and path cost of system operation,the task assignment algorithm of multi-strategy optimized sparrow search algorithm is proposed.Firstly,a clone selection mechanism is introduced to optimize the initial population and improve the quality of the initial sparrow population;secondly,an adaptive butterfly update mechanism is designed to replace the discoverer position update method to expand the population search range and balance the global search capability;finally,a backward learning strategy incorporating the probability of variation is introduced to solve the problem of the algorithm falling into local optimum at a later stage.Through several experiments,the algorithm proposed in this paper has significant effect in improving the performance of multi-robot scheduling system.