An Ameliorated Moth-Flame Optimization Algorithm
Xiaodong Zhao, Yiming Fang, Zhuang Ma, Miao Xu · 2018
To improve the global search ability under the condition of ensuring convergence speed, it is still a major challenge for most meta-heuristic optimization algorithms. The Moth-Flame Optimization (MFO) algorithm is an innovative nature-inspired algorithm. To improve the precision of the solution and to quicken the convergence speed and to increase the stability of MFO, an ameliorated Moth-flame optimization algorithm (A-MFO) that combines the crisscross optimization algorithm with MFO is proposed to solve this problems that are mentioned above. The performance of proposed A-MFO is demonstrated on six benchmark mathematical function optimization problems regarding superior accuracy and lower computational time achieved compared to other well-known nature-inspired algorithms.