| 许富景,刘强,张焱婷,李彤,兰洺洋.基于麻雀搜索算法的复杂测量任务调度方法[J].电子测量与仪器学报,2025,39(7):128-139 |
| 基于麻雀搜索算法的复杂测量任务调度方法 |
| Scheduling method for complex measurement task basedon sparrow search algorithm |
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| DOI： |
| 中文关键词: 复杂测量任务调度 麻雀搜索算法 反向学习 信息交换 变邻域搜索 |
| 英文关键词:scheduling for complex measurement task sparrow search algorithm reverse learning information switching variable neighborhood search |
| 基金项目:省部共建动态测试技术国家重点实验室基金(2022-SYSJJ-02）项目资助 |
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| Author | Institution |
| Xu Fujing | School of Automation and Software Engineering, Shanxi University, Taiyuan 030031, China |
| Liu Qiang | School of Automation and Software Engineering, Shanxi University, Taiyuan 030031, China |
| Zhang Yanting | School of Automation and Software Engineering, Shanxi University, Taiyuan 030031, China |
| Li Tong | School of Automation and Software Engineering, Shanxi University, Taiyuan 030031, China |
| Lan Mingyang | School of Automation and Software Engineering, Shanxi University, Taiyuan 030031, China |
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| 中文摘要: |
| 大型复杂机电设备在航空航天、遥感测绘和智能制造行业的应用越来越广。针对复杂机电设备在仓储和运输过程中的状态信息实时测量问题，特别是测量过程中的复杂测量任务调度难题，提出了一种基于改进麻雀搜索算法(improving the sparrow search algorithm，ISSA)的复杂测量任务实时调度方法。该方法首先通过tent混沌映射并结合反向学习初始化麻雀种群，提升算法初始解质量；随后引入灰狼优化算法信息交换机制改进发现者搜索策略，提升算法全局搜索能力；最后将正余弦机制与跟随者位置更新相结合，并在迭代完成后对发现者个体进行变邻域搜索，提高调度算法收敛速度，防止算法陷入局部最优。为验证调度方法的综合调度性能，对其进行了大量对比实验分析。实验结果表明，该方法将系统调度算法的计算时间缩减了14.3%，最大完成时间也较传统方法优化了46.6%，充分验证了其在复杂测量任务调度中的有效性和稳定性。 |
| 英文摘要: |
| The large and complex electromechanical equipment is more and more widely used in aerospace, remote sensing and intelligent manufacturing industries. A real-time scheduling method for complex measurement task based on improved sparrow search algorithm is proposed to address the real-time measurement problem of status information of large and complex electromechanical equipment during storage and transportation, especially the complex scheduling issue for measurement processes. Firstly, the initial population of sparrows is initialized using a combination of tent chaos mapping and reverse learning to enhance the quality of initial solutions. Subsequently, the information exchange mechanism of the grey wolf optimization algorithm is introduced to improve the explorer search strategy and enhance algorithm global search capability. Finally, the sine-cosine mechanism is combined with the follower position update process and the variable neighborhood search is carried out to improve the convergence speed of the scheduling algorithm and prevent the algorithm from falling into the local optimal. In order to verify the comprehensive performance of the scheduling method, a large number of comparative experiments are conducted. The experimental results indicate that the proposed method reduces the system computation time by 14.3% and optimizes the maximum completion time by 46.6% compared with the traditional method, which validates its effectiveness and stability in the scheduling of complex measurement tasks. |
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