A Quantum-behaved Simulated Annealing Enhanced Moth-flame Optimization Method

Abstract This study develops an improved moth-flame optimization (MFO) algorithm, which is a recently proposed optimizer based on moth behavior in nature. It has achieved favorable results in medical science, educational evaluation, and other fields. However, the convergence rate of the original MFO is too fast in the running process and it is prone to fall into local optimum, which leads to the failure to produce the high-quality optimal result. Accordingly, this paper proposes a reinforced technique for the MFO algorithm. Firstly, the simulated annealing strategy is introduced into the MFO to boost the advantage of the algorithm in the local exploitation process. Then, the idea of the quantum rotation gate is integrated to enhance the global exploration ability of the algorithm and ameliorate the diversity of the moth. These two steps maintain the relationship between exploitation and exploration as well as strengthen the performance of the algorithm in both phases. After that, the method is compared with ten well-regarded and ten alternative algorithms on benchmark functions to verify the effectiveness of the approach. Also, the Wilcoxon sign rank and Friedman assessment were performed to verify the significance of the proposed method against other counterparts. The simulation results reveal that the two introduced strategies significantly improve the exploration and exploitation capacity of MFO. Finally, the algorithm is utilized to feature selection and two engineering problems, including pressure vessel design (PVD) and multiple disk clutch brake (MDCB) problem. In these practical applications, the novel algorithm also achieves extremely impressive results, which also illustrates that the algorithm is qualified is an effective auxiliary appliance in solving complex optimization problems.