A Flower Pollination Algorithm Based on Nonlinear Cross-Generation Differential Evolution and Its Application Study

LIANG Liang; WEI Ya-xing; LI Yi-xin; JIA Yun-jian

doi:10.12263/DZXB.20211674

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A Flower Pollination Algorithm Based on Nonlinear Cross-Generation Differential Evolution and Its Application Study

PAPERS | 更新时间：2025-12-08

- A Flower Pollination Algorithm Based on Nonlinear Cross-Generation Differential Evolution and Its Application Study
- ACTA ELECTRONICA SINICA Vol. 51, Issue 9, Pages: 2445-2456(2023)
- 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：
- 基金信息：
  
  National Natural Science Foundation of China(62071075;61971077);Natural Science Foundation of Chongqing(cstc2020jcyj-msxmX0704);Fundamental Research Funds for the Central Universities(2020CDJ-LHZZ-022)
- DOI：10.12263/DZXB.20211674
  CLC： TP391
- Received：20 December 2021，
  
  Revised：2022-04-22，
  
  Published：25 September 2023
- 稿件说明：
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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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