Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (11): 46-51.DOI: 10.3778/j.issn.1002-8331.1809-0091
Previous Articles Next Articles
WANG Guang, JIN Jiayi
Online:
Published:
王 光,金嘉毅
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
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
WANG Guang, JIN Jiayi. Moth-Flame Optimization Algorithm Fused on Refraction Principle and Opposite-Based Learning[J]. Computer Engineering and Applications, 2019, 55(11): 46-51.
王 光,金嘉毅. 融合折射原理反向学习的飞蛾扑火算法[J]. 计算机工程与应用, 2019, 55(11): 46-51.
0 / Recommend
Add to citation manager EndNote|Ris|BibTeX
URL: http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1809-0091
http://cea.ceaj.org/EN/Y2019/V55/I11/46