Chaos-enhanced moth-flame optimization algorithm for global optimization

Moth-flame optimization (MFO) is a novel metaheuristic algorithm inspired by the characteristics of a moth's navigation method in nature called transverse orientation. Like other metaheuristic algorithms, it is easy to fall into local optimum and leads to slow convergence speed. The chaotic map is one of the best methods to improve exploration and exploitation of the metaheuristic algorithms. In the present study, we propose a chaos-enhanced MFO (CMFO) by incorporating chaos maps into the MFO algorithm to enhance its performance. The chaotic map is utilized to initialize the moths' population, handle the boundary overstepping, and tune the distance parameter. The CMFO is benchmarked on three groups of benchmark functions to find out the most efficient one. The performance of the CMFO is also verified by using two real engineering problems. The statistical results clearly demonstrate that the appropriate chaotic map (singer map) embedded in the appropriate component of MFO can significantly improve the performance of MFO.