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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | en en en |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
|
| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/37720/1/Application%20of%20manta%20ray%20foraging%20optimization%20with%20gradient-based%20mutation%20.pdf https://umpir.ump.edu.my/id/eprint/37720/2/Application%20of%20manta%20ray%20foraging%20optimization%20with%20gradient-based%20mutation_FULL.pdf https://umpir.ump.edu.my/id/eprint/37720/13/Application%20of%20manta%20ray%20foraging%20optimization.pdf https://umpir.ump.edu.my/id/eprint/37720/ https://doi.org/10.1109/ISCAIE57739.2023.10165419 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|