计算机工程与应用2019，Vol.55Issue(11)：46-51,59,7.DOI:10.3778/j.issn.1002-8331.1809-0091
融合折射原理反向学习的飞蛾扑火算法
Moth-Flame Optimization Algorithm Fused on Refraction Principle and Opposite-Based Learning
摘要
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 algo-rithm 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 princi-ple 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.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Key words
moth-flame algorithm/refraction principle/opposite-based learning/swarm intelligence algorithm/population diversity分类
信息技术与安全科学引用本文复制引用
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):46-51,59,7.基金项目
国家自然科学基金(No.71371091). （No.71371091）
