基于非线性跨代差分进化的花授粉优化算法及其应用研究

梁靓; 魏亚星; 李义鑫; 贾云健

doi:10.12263/DZXB.20211674

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基于非线性跨代差分进化的花授粉优化算法及其应用研究

学术论文 | 更新时间：2025-12-08

- 基于非线性跨代差分进化的花授粉优化算法及其应用研究
- A Flower Pollination Algorithm Based on Nonlinear Cross-Generation Differential Evolution and Its Application Study
- 电子学报 2023年51卷第9期页码：2445-2456
- 作者机构：
  
  重庆大学微电子与通信工程学院，重庆 400000
- 作者简介：
  
  [ "梁靓女，1981年9月出生于重庆市，现为重庆大学副教授，博士/硕士生导师，主要研究方向为新一代移动通信、可信物联网等.E-mail: liangliang@cqu.edu.cn" ]
  魏亚星男，1996年3月出生于内蒙古乌兰察布市，现为重庆大学微电子与通信工程学院硕士研究生，主要研究方向为网络性能优化、网络智能. E-mail: wyx@cqu.edu.cn
  李义鑫男，1992年10月出生于重庆市，现为重庆大学微电子与通信工程学院博士研究生，主要研究方向为区块链、可信评估等. E-mail: liyixin@cqu.edu.cn
  贾云健男，1976年8月出生于山西省太原市，现为重庆大学教授，博士/硕士生导师，主要研究方向为通信与计算融合、新一代移动通信等.E-mail: yunjian@cqu.edu.cn
- 基金信息：
  
  国家自然科学基金面上项目(62071075;61971077);重庆市自然科学基金面上项目(cstc2020jcyj-msxmX0704);中央高校基本科研业务费项目(2020CDJ-LHZZ-022)
- DOI：10.12263/DZXB.20211674
  中图分类号： TP391
- 收稿：2021-12-20，
  
  修回：2022-04-22，
  
  纸质出版：2023-09-25
- 稿件说明：
移动端阅览
梁靓,魏亚星,李义鑫等.基于非线性跨代差分进化的花授粉优化算法及其应用研究[J].电子学报,2023,51(09):2445-2456.

LIANG Liang,WEI Ya-xing,LI Yi-xin,et al.A Flower Pollination Algorithm Based on Nonlinear Cross-Generation Differential Evolution and Its Application Study[J].ACTA ELECTRONICA SINICA,2023,51(09):2445-2456.
梁靓,魏亚星,李义鑫等.基于非线性跨代差分进化的花授粉优化算法及其应用研究[J].电子学报,2023,51(09):2445-2456. DOI： 10.12263/DZXB.20211674.

LIANG Liang,WEI Ya-xing,LI Yi-xin,et al.A Flower Pollination Algorithm Based on Nonlinear Cross-Generation Differential Evolution and Its Application Study[J].ACTA ELECTRONICA SINICA,2023,51(09):2445-2456. DOI： 10.12263/DZXB.20211674.

摘要

针对高维度变量的优化问题，本文设计了一种基于非线性跨代差分进化的花授粉优化算法.该算法利用跨代差分进化引导个体逼近最优解，使算法的局部搜索过程具备导向性，并设置非线性惯性权重提升算法的搜索收敛速度.同时，通过参数自适应调整实现缩放因子和交叉概率的动态更新，从而提高种群丰富度、减少局部解的数量，再结合跨代赌轮盘方式以降低陷入局部最优解的概率.仿真验证表明，该算法能够在不同维度测试函数下保持较好的寻优特性和稳定性，尤其在高维度测试函数下的寻优性能更好.同时，本文以工业互联网中的无人机智能巡检的路径规划为例，评估了算法在实际应用中的性能.实验结果表明该算法可以满足巡检路径规划的低成本、高效率和规避外部攻击的需求.

Abstract

For the optimization problem of high-dimensional variables

we design a flower pollination algorithm based on nonlinear cross-generation differential evolution (FPA-NCDE). The algorithm guides individuals to approximate the optimal solution with cross-generation differential evolution to make local search process oriented. Meanwhile

the nonlinear inertia weight is set to improve the search convergence speed. The scaling factor and crossover probability are dynamically updated by parameter adaptive adjustment to enhance the population richness and reduce the number of local solutions. Combined with the cross-generation roulette wheel

the probability of trapping into local optimal solution is decreased. The performance evaluation verifies that the proposed FPA-NCDE can maintain good optimization characteristics and stability under different dimensional benchmark functions

especially under high dimensional test functions. In addition

FPA-NCDE is applied to unmanned aerial vehicle intelligent inspection of industrial internet to evaluate the performance of the algorithm in practical applications. The experiments results show that FPA-NCDE can satisfy the needs of low cost

high efficiency and avoidance of external attacks in inspection path planning.

关键词

Keywords

references

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