Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems
In this paper, a new variant of Manta Ray Foraging Optimization (MRFO) algorithm is introduced to deal with real parameter constrained optimization problem. Gradient-based Mutation MRFO (GbM-MRFO) is derived from basic strategy of MRFO and synergized with the Gradient-based Mutation strategy. MRFO i...
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| Format: | Conference or Workshop Item |
| Language: | English |
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2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/36959/1/Gradient-based%20mutation%20manta%20ray%20foraging%20optimization%20%28gbm-mrfo%29%20for%20solving%20constrained%20real-world%20problems.pdf |
| _version_ | 1825814845616816128 |
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| author | Ahmad Azwan, Abdul Razak Ahmad Nor Kasruddin, Nasir |
| author_facet | Ahmad Azwan, Abdul Razak Ahmad Nor Kasruddin, Nasir |
| author_sort | Ahmad Azwan, Abdul Razak |
| collection | UMP |
| description | In this paper, a new variant of Manta Ray Foraging Optimization (MRFO) algorithm is introduced to deal with real parameter constrained optimization problem. Gradient-based Mutation MRFO (GbM-MRFO) is derived from basic strategy of MRFO and synergized with the Gradient-based Mutation strategy. MRFO is a recently new introduced algorithm that consists of strategy of foraging adopted by Manta Ray while Gradient-based Mutation (GbM) is a feasibility-and solution repair strategy adopted from ϵ-Matrix-Adaptation Evolution Strategy (ϵ-MAES). MRFO is proven to solve artificial benchmark-function test by relatively good performance compared to several state-of-the-art algorithm while GbM is a productive approach to repair solution which led to improve the feasibility of the solution throughout the search by using Jacobian approximation in finite differences. GbM-MRFO turn out to be a competitive optimization algorithm on solving constrained optimization problem of Three-bar Truss problem. The performance of GbM-MRFO is proven to be efficient in solving the problems by providing lighter weight of truss with better accuracy of solution. |
| first_indexed | 2024-03-06T13:04:37Z |
| format | Conference or Workshop Item |
| id | UMPir36959 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:04:37Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | UMPir369592023-02-10T03:44:48Z http://umpir.ump.edu.my/id/eprint/36959/ Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems Ahmad Azwan, Abdul Razak Ahmad Nor Kasruddin, Nasir T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering In this paper, a new variant of Manta Ray Foraging Optimization (MRFO) algorithm is introduced to deal with real parameter constrained optimization problem. Gradient-based Mutation MRFO (GbM-MRFO) is derived from basic strategy of MRFO and synergized with the Gradient-based Mutation strategy. MRFO is a recently new introduced algorithm that consists of strategy of foraging adopted by Manta Ray while Gradient-based Mutation (GbM) is a feasibility-and solution repair strategy adopted from ϵ-Matrix-Adaptation Evolution Strategy (ϵ-MAES). MRFO is proven to solve artificial benchmark-function test by relatively good performance compared to several state-of-the-art algorithm while GbM is a productive approach to repair solution which led to improve the feasibility of the solution throughout the search by using Jacobian approximation in finite differences. GbM-MRFO turn out to be a competitive optimization algorithm on solving constrained optimization problem of Three-bar Truss problem. The performance of GbM-MRFO is proven to be efficient in solving the problems by providing lighter weight of truss with better accuracy of solution. 2022-11-15 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/36959/1/Gradient-based%20mutation%20manta%20ray%20foraging%20optimization%20%28gbm-mrfo%29%20for%20solving%20constrained%20real-world%20problems.pdf Ahmad Azwan, Abdul Razak and Ahmad Nor Kasruddin, Nasir (2022) Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems. In: The 6th National Conference for Postgraduate Research (NCON-PGR 2022) , 15 November 2022 , Virtual Conference, Universiti Malaysia Pahang, Malaysia. p. 122.. (Published) https://ncon-pgr.ump.edu.my/index.php/en/?option=com_fileman&view=file&routed=1&name=E-BOOK%20NCON%202022%20.pdf&folder=E-BOOK%20NCON%202022&container=fileman-files |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Ahmad Azwan, Abdul Razak Ahmad Nor Kasruddin, Nasir Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems |
| title | Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems |
| title_full | Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems |
| title_fullStr | Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems |
| title_full_unstemmed | Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems |
| title_short | Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems |
| title_sort | gradient based mutation manta ray foraging optimization gbm mrfo for solving constrained real world problems |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/36959/1/Gradient-based%20mutation%20manta%20ray%20foraging%20optimization%20%28gbm-mrfo%29%20for%20solving%20constrained%20real-world%20problems.pdf |
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