计算机工程与应用 ›› 2023, Vol. 59 ›› Issue (3): 66-76.DOI: 10.3778/j.issn.1002-8331.2205-0007

• 理论与研发 • 上一篇    下一篇

融合浓度平衡和菲克定律的新平衡优化器算法

张梦溪,马良,刘勇   

  1. 上海理工大学 管理学院,上海 200093
  • 出版日期:2023-02-01 发布日期:2023-02-01

New Equilibrium Optimizer Algorithm Combining Concentration Equilibrium and Fick’s Law

ZHANG Mengxi, MA Liang, LIU Yong   

  1. School of Management, University of Shanghai for Science and Technology, Shanghai 200093, China
  • Online:2023-02-01 Published:2023-02-01

摘要: 针对平衡优化器算法(equilibrium optimizer,EO)出现的收敛速度慢、算法精度不够、开发和搜索阶段信息不平衡等问题,提出了融合浓度平衡和菲克定律的新平衡优化器算法(new equilibrium optimizer,NEO)。根据布朗运动和扩散现象,不同浓度区域的粒子采取不同的浓度平衡机制,改进算法平衡池,提高种群间的信息交流能力;在算法参数中引入幂函数和指数函数两种自适应因子,进一步平衡全局搜索和局部开发能力,使得粒子种群在解空间中进行广泛搜索和深度挖掘;根据菲克定律,在粒子位置更新公式中引入扰动机制,提高算法寻优精度和收敛速度。采用24个基准测试函数和Wilcoxon 秩和检验,将NEO算法和其他智能优化算法进行仿真实验对比,结果表明NEO算法具有良好的优化性能。

关键词: 平衡优化器算法, 浓度平衡, 自适应因子, 菲克定律

Abstract: New equilibrium optimizer algorithm(NEO), which combines concentration equilibrium and Fick’s law, is proposed to solve the problems of slow convergence speed, insufficient accuracy and information imbalance in development and search stage of equilibrium optimizer algorithm(EO). According to Brownian motion and diffusion phenomenon, different concentration balancing mechanism is adopted for particles in different concentration regions, and the algorithm balancing pool is improved to improve the information communication ability between populations. Then, two adaptive factors, power function and exponential function, are introduced into the algorithm parameters to further balance the global search and local development capabilities, so that the particle population can conduct extensive search and in-depth mining in the solution space. Finally, according to Fick’s law, a disturbance mechanism is introduced into the particle position updating formula to improve optimization accuracy and convergence speed of the algorithm. In addition, 24 benchmark test functions and Wilcoxon rank sum test are used to compare NEO algorithm with other intelligent optimization algorithms. The results show that NEO algorithm has good optimization performance.

Key words: equilibrium optimizer, concentration balance, adaptive factor, Fick’s law