2025年02期 v.46 38-47页
黄嘉铖 蔡延光 胡城 曾庆丰
（广东工业大学自动化学院，广东 广州 510006）
摘要：针对麻雀搜索算法在解决远程健康线下服务任务调度的路径规划时，存在收敛速度慢、全局搜索能力不足和易陷入局部最优解等问题，提出一种改进麻雀搜索算法的远程健康线下服务任务调度方法。首先，引入基于融合转移概率的正余弦策略发现者更新机制，提高发现者个体的全局搜索能力；然后，引入基于动态自适应权重的混合粒子群追随者更新机制，增强种群间的信息交流，避免算法陷入局部最优解；最后，引入种群多样性丰富机制，扩大算法搜索范围，提升跳出局部最优解的能力。 实验结果表明：在解决远程健康线下服务任务调度的路径规划问题上，改进的麻雀搜索算法相较于麻雀搜索算法、多目标遗传算法，服务成本更低，服务效率更高。
关键词：改进麻雀搜索算法；远程健康线下服务；任务调度；路径规划；正余弦策略；粒子群优化
中图分类号：TP18; TP301.6 文献标志码：A 文章编号：1674-2605(2025)02-0006-10
DOI：10.12475/aie.20250206 开放获取
Remote Health Service Task Scheduling Method Based on
Improved Sparrow Search Algorithm
HUANG Jiacheng CAI Yanguang HU Cheng ZENG Qingfeng
(School of Automation, Guangdong University of Technology, Guangzhou 510006, China)
Abstract: To address the issues of the sparrow search algorithm (SSA) in solving path planning for remote health offline service task scheduling, such as slow convergence speed, insufficient global search capability, and susceptibility to local optima, an improved sparrow search algorithm for remote health offline service task scheduling is proposed. First, a discoverer update mechanism based on a fusion transition probability sine-cosine strategy is introduced to enhance the global search capability of discoverer individuals. Then, a follower update mechanism incorporating dynamic adaptive weights and hybrid particle swarm optimization is implemented to strengthen information exchange within the population and avoid the algorithm falling into local optima. Finally, a population diversity enrichment mechanism is introduced to expand the algorithm’s search range and improve its ability to escape local optima. Experi-mental results demonstrate that the improved sparrow search algorithm achieves lower service costs and higher service efficiency compared to the original sparrow search algorithm and multi-objective genetic algorithms in addressing path planning for remote health offline service task scheduling.
Keywords: improved sparrow search algorithm; remote health offline service; task scheduling; path planning; sine-cosine strategy; particle swarm optimization
