关键词: 群智能, 飞蛾火焰优化算法, Kent混沌, 动态惯性权值, 数值函数

Abstract: Moth-Flame Optimization（MFO） algorithm, which is inspired by social behaviors of individuals in moth flame, is a nature-inspired and easy to implement global optimization algorithm. The MFO has shown good performance for many real-world optimization tasks. However, MFO has problems with premature convergence and easy trapping into local optimum solutions. In order to overcome these deficiencies, an Ameliorative MFO（AMFO） algorithm based on Kent chaotic dynamic inertia weight is proposed. In the AMFO algorithm, a Kent chaotic map search strategy is introduced to help the current optimal solution jump out of the local optimal solution. In addition, the dynamic inertia weight strategy based on fitness value and iteration number is used to balance the development and exploration ability and further improve the performance of MFO algorithm. The search accuracy and performance of AMFO algorithm are verified on 8 classical benchmark functions. The experimental results show that AMFO technique has superior search performance compared with standard MFO algorithm, particle swarm optimization algorithm and differential evolution algorithm.

Key words: swarm intelligence, moth-flame optimization algorithm, Kent chaotic, dynamic inertia weight, numerical function