Improved Moth Flame Optimization with Multioperator for solving real-world optimization problems

The Moth flame optimization algorithm (MFO) is a nature-inspired optimization algorithm which imitates moths darts into the fire. Similarly, to other meta-heuristic algorithm, MFO still has the disadvantage of getting into the local best and the convergence rate cannot be satisfying. In this paper a Multi-operator Moth Flame Optimization algorithm (MOMFO) is proposed to improve the MFO algorithm, which combines Elite Search strategy, Adaptive control strategy and Chaos search strategy to enhance MFO.A two-stage strategy is used to balance the global search and local search ability, which makes the algorithm have better search performance and accuracy. The proposed MOMFO is benchmarked on eight well-known test functions and real-world problem, and the results show that the algorithm is effective in solving complex nonlinearity problems.