基于多阶段调度框架的麻雀搜索优化算法

王毅; 郑宏志; 黄欣; 洪国栋; 闫小婕

doi:10.12263/DZXB.20230152

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基于多阶段调度框架的麻雀搜索优化算法

学术论文 | 更新时间：2025-12-08

- 基于多阶段调度框架的麻雀搜索优化算法
- Sparrow Search Optimization Algorithm Based on Multi-Stage Scheduling Framework
- 电子学报 2024年52卷第9期页码：3086-3096
- 作者机构：
  
  1.西北大学信息科学与技术学院，陕西西安 710127
  2.西安交通大学，陕西西安 710049
- 作者简介：
  
  [ "王毅男，1979年2月生，上海人，博士（后）.现为西北大学信息科学与技术学院副教授，主要从事智能信息处理、深度学习与群体智能优化算法." ]
  [ "郑宏志男，1997年8月生，山东济南人，西北大学信息科学与技术学院硕士研究生，主要从事深度学习，群体智能与多目标优化.zzheng27189@163.com" ]
  [ "黄欣女，1979年6月生，陕西人，博士.现为西安交通大学第一附属医院副教授，主要从事血管腔内影像处理与优化研究. hearthx@126.com" ]
- 基金信息：
  
  国家自然科学基金重大仪器专项(42027806);国家重点研发计划项目(2018YFC1504705);国家自然科学基金(61731015;61402517);陕西省重点研发计划项目(2022GY-331);陕西省自然科学基金(2018JM6029)
- DOI：10.12263/DZXB.20230152
  中图分类号： TP182;TP391
- 收稿：2023-02-20，
  
  修回：2023-05-25，
  
  纸质出版：2024-09-25
- 稿件说明：
移动端阅览
王毅, 郑宏志, 黄欣, 等. 基于多阶段调度框架的麻雀搜索优化算法[J]. 电子学报, 2024, 52(09): 3086-3096.

WANG Yi, ZHENG Hong-zhi, HUANG Xin, et al. Sparrow Search Optimization Algorithm Based on Multi-Stage Scheduling Framework[J]. Acta Electronica Sinica, 2024, 52(09): 3086-3096.
王毅, 郑宏志, 黄欣, 等. 基于多阶段调度框架的麻雀搜索优化算法[J]. 电子学报, 2024, 52(09): 3086-3096. DOI：10.12263/DZXB.20230152

WANG Yi, ZHENG Hong-zhi, HUANG Xin, et al. Sparrow Search Optimization Algorithm Based on Multi-Stage Scheduling Framework[J]. Acta Electronica Sinica, 2024, 52(09): 3086-3096. DOI：10.12263/DZXB.20230152

摘要

本文提出一种多阶段调度框架，实现对麻雀种群的初始位置、觅食、侦查与反捕食不同阶段的多策略调度.利用Halton序列与Tent映射提升种群个体质量与初始位置的分布均匀性.在觅食阶段，针对发现者与加入者因位置争夺导致种群质量劣化，设计最佳适配比调控二者数量关系，对超出适配比的加入者采用碰撞反弹算子改变其优化轨迹.满足适配比后则通过侦查判断是否存在天敌，若有则进入反捕食阶段，并利用Levy飞行并结合指数分布设计随机迁移机制，生成潜在的全局最优解区域；当连续多次没有发现天敌时为避免种群陷入局部极值，建立模拟预警机制并采用蝗虫算法进行多路径开发，避免寻优方向单一化.不同策略与机制的交替运行、协同调度，平衡了算法的多样性与收敛性. 实验结果表明，与最近麻雀变体算法和元启发改进算法相比，该算法在寻优效率与收敛精度上显著优于对比方法.

Abstract

This paper proposes a multi-stage scheduling framework to realize multi-strategy scheduling of sparrow populations in different stages of initial location

foraging

detection

and anti-predation. Halton sequence and Tent mapping are used to improve the quality of the population individuals and the distribution uniformity of initial position. In the foraging stage

aiming at the deterioration of the population quality caused by the position competition between the finder and the joiner

the best fit ratio is designed to control the quantitative relationship between the two

and the collision rebound operator is used to change the optimal trajectory of the joiner beyond the fit ratio. After the adaptation ratio is met

judge whether there is a natural enemy through investigation

and if there is

enter the anti-predation stage

and use Levy flight and combine exponential distribution to design a random migration mechanism to generate a potential global optimal solution area; when no natural enemy is found for many times in a row in order to prevent the population from falling into local extremum

an early warning mechanism is established and the locust algorithm is used for multi-path development to avoid a single optimization direction. The alternate operation and coordinated scheduling of different strategies and mechanisms balance the diversity and convergence of the algorithm. Experimental results show that

compared with the latest sparrow variant algorithm and meta-heuristic improved algorithm

the algorithm is significantly better than the comparison methods in terms of optimization efficiency and convergence accuracy.

关键词

Keywords

references

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