应用精英反向学习的飞蛾扑火优化算法

Moth-Flame Optimization Algorithm Using Elite Opposition-Based Learning

摘要: 飞蛾扑火优化算法(MFO)是一个新颖的启发式算法，其主要设计灵感来源于自然界中称为横向定位的导航机制。由于标准飞蛾扑火优化算法容易陷入局部最优、求解精度低，所以借鉴精英反向学习策略，提出一种应用精英反向学习的飞蛾扑火优化算法(EOMFO)。新算法不仅增加了群体的多样性，并且提高了算法的综合性能。通过对7组标准测试函数的实验仿真，结果表明应用精英反向学习的飞蛾扑火优化算法具有更高的收敛精度，从而验证了新算法是有效的和可行的。

Abstract: The Moth-flame Optimization (MFO) algorithm is a novel heuristic algorithm. The main inspiration of this algorithm is the navigation method of moths in nature called transverse orientation. Since the Moth-flame Optimization algorithm is easy to fall into the local optimum and the solution accuracy is low, elite opposition-based learning was utilized to propose the Moth-flame optimization algorithm using elite opposition-based learning (EOMFO). The proposed algorithm not only increases the diversity of the group, but also improves the overall performance of the algorithm. Through the experimental simulation of 7 sets of standard test functions, the results show that the Moth-flame optimization algorithm with elite opposition-based learning has higher convergence precision, which verifies that the new algorithm is effective and feasible.