Application of manta ray foraging optimization with gradient-based mutation (cMRFO) for solving power system problems

In this paper, the Manta Ray Foraging Optimization (MRFO) algorithm is applied to solve real parameter constrained optimization problems, using the Gradient-based Mutation MRFO (cMRFO) variant. The cMRFO algorithm integrates the MRFO strategy, which emulates the foraging behavior of Manta Rays, with the Gradient-based Mutation strategy, inspired by the ε-MatrixAdaptation Evolution Strategy (εMAgES), to enhance solution feasibility and repair during the search process. Previous studies have demonstrated the effectiveness of MRFO in solving artificial benchmark-function tests, and GbM in improving solution feasibility during the search. This study found cMRFO to be a competitive optimization algorithm for solving constrained optimization problems. To validate the performance of the cMRFO algorithm, it was applied to a power system problem of sizing single-phase distributed generation with reactive power support for phase balancing at the main transformer/grid. The analysis revealed that cMRFO outperformed εMAgES and COLSHADE in terms of overall performance.