An adaptive moth flame optimization algorithm with historical flame archive strategy and its application

Moth Flame Optimization (MFO) is a new nature-inspired heuristic algorithm, and has successfully been applied in various fields of practical engineering. To enhance exploitation of MFO and avoid dropping into local optimal solution, an adaptive MFO algorithm with historical flame archive strategy is proposed in this paper, which is termed MFO-HFA to avoid ambiguity. In MFO-HFA, to make full use of population history information, the archive consists of historical optimal individuals, which is utilized to preserve the information of better historical flame. Besides, to make full use of the information of top flame information, a top flame randomly matching mechanism is utilized to improve the convergence ability of population. To demonstrate the advantage of MFO-HFA, it is compared with several well-known variants of MFO and some state-of-the-art intelligence algorithms on both 25 benchmark functions of CEC 2005. The experimental results indicate that MFO-HFA outperforms other compared algorithms and has obtained best accuracy. Furthermore, MFO-HFA is used to generate the rules of IDS by NSL-KDD dataset. The test results demonstrate that MFO-HFA outperforms compared algorithms and has gained 96.5% accuracy.