计算机工程与应用 ›› 2019, Vol. 55 ›› Issue (11): 46-51.DOI: 10.3778/j.issn.1002-8331.1809-0091

• 理论与研发 • 上一篇    下一篇

融合折射原理反向学习的飞蛾扑火算法

王  光,金嘉毅   

  1. 辽宁工程技术大学 软件学院,辽宁 葫芦岛 125105
  • 出版日期:2019-06-01 发布日期:2019-05-30

Moth-Flame Optimization Algorithm Fused on Refraction Principle and Opposite-Based Learning

WANG Guang, JIN Jiayi   

  1. School of Software, Liaoning Technical University, Huludao, Liaoning 125105, China
  • Online:2019-06-01 Published:2019-05-30

摘要: 飞蛾扑火算法是一种新型群智能优化算法,目前已经应用于特征选择和图像分割等诸多领域。然而,传统的飞蛾扑火算法后期收敛速度不足且容易陷入局部最优,从而影响了算法的整体性能。为了提高飞蛾扑火算法的优化性能,提出了一种基于折射原理反向学习的飞蛾扑火算法(ROBL-MFO)。该算法首先在飞蛾的更新公式中引入历史最优火焰平均值,使火焰间的信息能够互相交流,提高算法的收敛能力;其次利用随机反向学习策略对解进行反向学习,扩大算法的搜索空间;最后使用折射原理对解进行折射操作,提高种群的多样性,帮助算法跳出局部最优。在六个标准实验函数上得到的实验结果表明,对比其他算法,ROBL-MFO算法拥有更好的收敛速度,且能够有效跳出局部最优。

关键词: 飞蛾扑火算法, 折射原理, 反向学习, 群智能算法, 种群多样性

Abstract: Moth-flame optimization algorithm is a new swarm intelligence optimization algorithm. It has been applied to many fields such as feature selection and image segmentation. However, the traditional moth-flame optimization algorithm is prone to fall into local optimum, affecting the performance of the algorithm. For solving the deficiency, a moth-flame optimization algorithm based on refraction principle and opposite-based learning(ROBL-MFO) is proposed in this paper. Firstly, it uses the average of the best flame to improve the convergence speed. Then, the opposite-based learning is used to expand the search space of the ROBL-MFO. Finally, for jumping out of the local optimal, it uses refraction principle to improve the diversity of the population. Six test function is used to compare the ROBL-MFO with other algorithms, and the results show that the ROBL-MFO has better convergence speed and can effectively jump out of the local optimal.

Key words: moth-flame algorithm, refraction principle, opposite-based learning, swarm intelligence algorithm, population diversity