Parametric optimization technique for continuous and combinational problems based on simulated annealing algorithm
Simulated annealing is an optimization method adapted from the annealing process. The optimization process using simulated annealing method is done by mapping the elements of physical coolant process onto the elements of optimization problem. This method uses local neighborhood search to find soluti...
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University of Muhammadiyah Malang
2023-12-01
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| Series: | JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) |
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| Online Access: | https://ejournal.umm.ac.id/index.php/JEMMME/article/view/29556 |
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| author | Yosua Heru Irawan Po Ting Lin |
| author_facet | Yosua Heru Irawan Po Ting Lin |
| author_sort | Yosua Heru Irawan |
| collection | DOAJ |
| description | Simulated annealing is an optimization method adapted from the annealing process. The optimization process using simulated annealing method is done by mapping the elements of physical coolant process onto the elements of optimization problem. This method uses local neighborhood search to find solutions, meaning it searches around it for answers itself and takes another solution based on everything around it. The simulated annealing method has been used successfully for the optimization process in the continuous case (Himmelblau’s function) and combinational case (Quadratic Assignment Problem or QAP). Based on the optimization results (global minima) for the Himmelblau's function, the points and are obtained with objective function . The optimal solution for the eight departmental arrangements is F, E, A, G for the bottom floor and H, D, C, B for the top floor, this arrangement produces an optimal total cost of 214. The simulated annealing method accepts an uphill move (worse move) by considering the probability, in this way we will not be trapped in the local minima position. These four search space variables and determine the performance of the simulated annealing method, we can adjust them according to the optimized case.Simulated annealing is an optimization method adapted from the annealing process. The optimization process using simulated annealing method is done by mapping the elements of physical coolant process onto the elements of optimization problem. This method uses local neighborhood search to find solutions, meaning it searches around it for answers itself and takes another solution based on everything around it. The simulated annealing method has been used successfully for the optimization process in the continuous case (Himmelblau’s function) and combinational case (Quadratic Assignment Problem or QAP). Based on the optimization results (global minima) for the Himmelblau's function, the points and are obtained with objective function . The optimal solution for the eight departmental arrangements is F, E, A, G for the bottom floor and H, D, C, B for the top floor, this arrangement produces an optimal total cost of 214. The simulated annealing method accepts an uphill move (worse move) by considering the probability, in this way we will not be trapped in the local minima position. These four search space variables and determine the performance of the simulated annealing method, we can adjust them according to the optimized case. |
| format | Article |
| id | doaj-art-ca521ab9f58b45ee87f0b9cef4b394da |
| institution | Kabale University |
| issn | 2541-6332 2548-4281 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | University of Muhammadiyah Malang |
| record_format | Article |
| series | JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) |
| spelling | doaj-art-ca521ab9f58b45ee87f0b9cef4b394da2025-01-21T05:02:28ZengUniversity of Muhammadiyah MalangJEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering)2541-63322548-42812023-12-0182758210.22219/jemmme.v8i2.2955627402Parametric optimization technique for continuous and combinational problems based on simulated annealing algorithmYosua Heru Irawan0Po Ting Lin1Institut Teknologi Nasional Yogyakarta (ITNY)National Taiwan University of Science and TechnologySimulated annealing is an optimization method adapted from the annealing process. The optimization process using simulated annealing method is done by mapping the elements of physical coolant process onto the elements of optimization problem. This method uses local neighborhood search to find solutions, meaning it searches around it for answers itself and takes another solution based on everything around it. The simulated annealing method has been used successfully for the optimization process in the continuous case (Himmelblau’s function) and combinational case (Quadratic Assignment Problem or QAP). Based on the optimization results (global minima) for the Himmelblau's function, the points and are obtained with objective function . The optimal solution for the eight departmental arrangements is F, E, A, G for the bottom floor and H, D, C, B for the top floor, this arrangement produces an optimal total cost of 214. The simulated annealing method accepts an uphill move (worse move) by considering the probability, in this way we will not be trapped in the local minima position. These four search space variables and determine the performance of the simulated annealing method, we can adjust them according to the optimized case.Simulated annealing is an optimization method adapted from the annealing process. The optimization process using simulated annealing method is done by mapping the elements of physical coolant process onto the elements of optimization problem. This method uses local neighborhood search to find solutions, meaning it searches around it for answers itself and takes another solution based on everything around it. The simulated annealing method has been used successfully for the optimization process in the continuous case (Himmelblau’s function) and combinational case (Quadratic Assignment Problem or QAP). Based on the optimization results (global minima) for the Himmelblau's function, the points and are obtained with objective function . The optimal solution for the eight departmental arrangements is F, E, A, G for the bottom floor and H, D, C, B for the top floor, this arrangement produces an optimal total cost of 214. The simulated annealing method accepts an uphill move (worse move) by considering the probability, in this way we will not be trapped in the local minima position. These four search space variables and determine the performance of the simulated annealing method, we can adjust them according to the optimized case.https://ejournal.umm.ac.id/index.php/JEMMME/article/view/29556annealingoptimizationuphillprobabilisticmetaheuristic |
| spellingShingle | Yosua Heru Irawan Po Ting Lin Parametric optimization technique for continuous and combinational problems based on simulated annealing algorithm JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) annealing optimization uphill probabilistic metaheuristic |
| title | Parametric optimization technique for continuous and combinational problems based on simulated annealing algorithm |
| title_full | Parametric optimization technique for continuous and combinational problems based on simulated annealing algorithm |
| title_fullStr | Parametric optimization technique for continuous and combinational problems based on simulated annealing algorithm |
| title_full_unstemmed | Parametric optimization technique for continuous and combinational problems based on simulated annealing algorithm |
| title_short | Parametric optimization technique for continuous and combinational problems based on simulated annealing algorithm |
| title_sort | parametric optimization technique for continuous and combinational problems based on simulated annealing algorithm |
| topic | annealing optimization uphill probabilistic metaheuristic |
| url | https://ejournal.umm.ac.id/index.php/JEMMME/article/view/29556 |
| work_keys_str_mv | AT yosuaheruirawan parametricoptimizationtechniqueforcontinuousandcombinationalproblemsbasedonsimulatedannealingalgorithm AT potinglin parametricoptimizationtechniqueforcontinuousandcombinationalproblemsbasedonsimulatedannealingalgorithm |