An improved moth-flame optimization algorithm with orthogonal opposition-based learning and modified position updating mechanism of moths for global optimization problems

摘要

Moth-Flame Optimization (MFO) algorithm is a new population-based meta-heuristic algorithm for solving global optimization problems. Flames generation and spiral search are two key components that affect the performance of MFO. To improve the diversity of flames and the searching ability of moths, an improved Moth-Flame Optimization (IMFO) algorithm is proposed. The main features of the IMFO are: the flames are generated by orthogonal opposition-based learning (OOBL); the modified position updating mechanism of moths with linear search and mutation operator. To evaluate the performance of IMFO, the IMFO algorithm is compared with other 20 algorithms on 23 benchmark functions and IEEE (Institute of Electrical and Electronics Engineers) CEC (Congress on Evolutionary Computation) 2014 benchmark test set. The comparative results show that the IMFO is effective and has good performance in terms of jumping out of local optimum, balancing exploitation ability and exploration ability. Moreover, the IMFO is also used to solve three engineering optimization problems, and it is compared with other well-known algorithms. The comparison results show that the IMFO algorithm can improve the global search ability of MFO and effectively solve the practical engineering optimization problems.