Application of Manta Ray Foraging Optimization with Gradient-based Mutation (cMRFO) for Solving Power System Problem
Ahmad Azwan Abd Razak, Ahmad Nor Kasruddin Nasir · 2023
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 $\varepsilon$-Matrix-Adaptation Evolution Strategy $(\varepsilon$ MAgES), 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 $\varepsilon$ MAgES and COLSHADE in terms of overall performance.