工业工程

工业工程 ›› 2023, Vol. 26 ›› Issue (2): 101-110.doi: 10.3969/j.issn.1007-7375.2023.02.012

• 系统建模与优化算法 • 上一篇    下一篇

求解高维复杂函数的改进飞蛾扑火算法

李煜1, 朱新亚2, 刘景森3   

  1. 1. 河南大学 管理科学与工程研究所;
    2. 商学院;
    3. 智能网络系统研究所, 河南 开封 475004
  • 收稿日期:2021-10-01 发布日期:2023-05-05
  • 作者简介:李煜(1969-),女,河南省人,教授,博士,主要研究方向为智能优化、电子商务等
  • 基金资助:
    国家自然科学基金资助项目(71601071)；河南省重点研发与推广资助专项(222102210065)；教育部人文社科青年基金资助项目(15YJC630079)

An Improved Moth-flame Optimization Algorithm for Solving High-dimensional Complex Functions

LI Yu1, ZHU Xinya2, LIU Jingsen3   

  1. 1. Institute of Management Science and Engineering;
    2. School of Business;
    3. Institute of Intelligent Network Systems, Henan University, Kaifeng 475004, China
  • Received:2021-10-01 Published:2023-05-05

RichHTML

15108

PDF

1739

摘要： 提出一种融合自适应动态扰动系数和分段可调节搜索策略的飞蛾扑火优化算法 (a moth-flame optimization algorithm with adaptive dynamic disturbance coefficient and piecewise adjustable search strategy, ADMFO)，求解高维复杂函数优化问题。通过自适应动态扰动系数策略来提高算法的全局搜索能力，避免算法陷入局部最优；通过分段可调节搜索策略来平衡全局探索和局部开发的比重，以此实现更好的寻优策略。对15个单峰和多峰复杂高维基准函数进行寻优实验，与粒子群算法、正弦余弦算法、蝴蝶算法、灰狼算法和其他4种改进算法进行对比。实验结果表明，ADMFO算法具有更好的寻优精度和稳定性。

关键词: 飞蛾扑火优化算法, 自适应动态扰动系数, 分段可调节搜索, 高维复杂函数优化

Abstract: A moth-flame optimization algorithm with adaptive dynamic disturbance coefficient and piecewise adjustable search strategy (ADMFO) is proposed to solve a large-scale complex optimization problem. The adaptive dynamic perturbation coefficient strategy is adopted to improve the global searching ability of the algorithm and avoid the algorithm falling into the local optimum. The segmented search strategy can balance the proportion of global exploration and local development, so as to achieve a better search strategy. 15 unimodal and multi-peak complex high-dimensional function optimization experiments are conducted, comparing particle swarm algorithm, sine cosine algorithm, butterfly optimization algorithm, the gray wolf algorithm, and the four improved algorithms proposed in other literature. The experimental data proves that the improved algorithm has better optimization accuracy and stability.

Key words: moth-flame optimization algorithm, adaptive dynamic disturbance coefficient, segmented adjustable search, optimization of high-dimensional complex functions

中图分类号: