Abstract: This paper presents an Enhanced Moth-Flame Optimization (EMFO) technique based on Cultural Learning (CL) and Gaussian Mutation (GM). The mechanism of CL and the operator of GM are incorporated to the original algorithm of Moth-Flame Optimization (MFO). CL plays an important role in the inheritance of historical experiences and stimulates moths to obtain information from flames more effectively, which helps MFO enhance its searching ability. Furthermore, in order to overcome the disadvantage of trapping into local optima, the operator of GM is introduced to MFO. This operator acts on the best flame in order to generate several variant ones, which can increase the diversity. The proposed algorithm of EMFO has been comprehensively evaluated on 13 benchmark functions, in comparison with MFO. Simulation results verify that EMFO shows a significant improvement on MFO, in terms of solution quality and algorithmic reliability.

Key words: bioinspired computing, moth-flame optimization, Gaussian mutation, cultural learning, benchmark functions