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