Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems

Abstract EN

The moth-flame optimization (MFO) algorithm is a novel nature-inspired heuristic paradigm.

The main inspiration of this algorithm is the navigation method of moths in nature called transverse orientation.

Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances.

However, these fancy insects are trapped in a spiral path around artificial lights.

Aiming at the phenomenon that MFO algorithm has slow convergence and low precision, an improved version of MFO algorithm based on Lévy-flight strategy, which is named as LMFO, is proposed.

Lévy-flight can increase the diversity of the population against premature convergence and make the algorithm jump out of local optimum more effectively.

This approach is helpful to obtain a better trade-off between exploration and exploitation ability of MFO, thus, which can make LMFO faster and more robust than MFO.

And a comparison with ABC, BA, GGSA, DA, PSOGSA, and MFO on 19 unconstrained benchmark functions and 2 constrained engineering design problems is tested.

These results demonstrate the superior performance of LMFO.