An Interactive Manta Ray Foraging Optimization Algorithm with Hierarchical Population Structure

The manta ray foraging optimization (MRFO) algorithm is a meta-heuristic method derived by imitating the behavior inspired by manta rays foraging. However, due to the lack of communication among individuals in the population, it suffers from the local optimum trapping problem. In this study, we innovatively propose a hierarchical population structure for it by adding an information interaction layer to the original MRFO population, namely an interactive manta rays foraging optimization (IMRFO) algorithm. In it, the hierarchical population structure successfully realizes the balance between local exploitation and global exploration. The superiority of IMRFO is confirmed in terms of solution quality, convergence speed, population diversity, and search trajectory by experimental findings based on thirty IEEE CEC2017 benchmark functions in comparison with other state-of-the-art meta-heuristic methods.