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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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Main Authors: Ahmad Azwan, Abdul Razak, Ahmad Nor Kasruddin, Nasir
Format: Conference or Workshop Item
Language:English
Published: 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
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
http://umpir.ump.edu.my/id/eprint/36959/
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
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spelling my.ump.umpir.369592023-02-10T03:44:48Z 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.. 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
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
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
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.
format Conference or Workshop Item
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
title 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_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_sort gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems
publishDate 2022
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
http://umpir.ump.edu.my/id/eprint/36959/
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
_version_ 1758578264830377984
score 13.211869

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