Simulated Annealing with Exploratory Sensing for Global Optimization
Simulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/13/9/230 |
| _version_ | 1797553876607434752 |
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| author | Majid Almarashi Wael Deabes Hesham H. Amin Abdel-Rahman Hedar |
| author_facet | Majid Almarashi Wael Deabes Hesham H. Amin Abdel-Rahman Hedar |
| author_sort | Majid Almarashi |
| collection | DOAJ |
| description | Simulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search algorithms such as the tabu search, the search in simulated annealing is dependent on the choice of the initial temperature to explore the search space, which has little indications of how much exploration has been carried out. The lack of exploration eye can affect the quality of the found solutions while the nature of the search in simulated annealing is mainly local. In this work, a methodology of two phases using an automatic diversification and intensification based on memory and sensing tools is proposed. The proposed method is called Simulated Annealing with Exploratory Sensing. The computational experiments show the efficiency of the proposed method in ensuring a good exploration while finding good solutions within a similar number of iterations. |
| first_indexed | 2024-03-10T16:22:49Z |
| format | Article |
| id | doaj.art-bbb168cd2f2a48c6875490ccb90b39ba |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T16:22:49Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-bbb168cd2f2a48c6875490ccb90b39ba2023-11-20T13:32:56ZengMDPI AGAlgorithms1999-48932020-09-0113923010.3390/a13090230Simulated Annealing with Exploratory Sensing for Global OptimizationMajid Almarashi0Wael Deabes1Hesham H. Amin2Abdel-Rahman Hedar3Department of Computer Sciences and Artificial Intelligence, College of Computer Sciences and Engineering, University of Jeddah, Jeddah 21589, Saudi ArabiaDepartment of Computer Science in Jamoum, Umm Al-Qura University, Makkah 25371, Saudi ArabiaDepartment of Computer Science in Jamoum, Umm Al-Qura University, Makkah 25371, Saudi ArabiaDepartment of Computer Science in Jamoum, Umm Al-Qura University, Makkah 25371, Saudi ArabiaSimulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search algorithms such as the tabu search, the search in simulated annealing is dependent on the choice of the initial temperature to explore the search space, which has little indications of how much exploration has been carried out. The lack of exploration eye can affect the quality of the found solutions while the nature of the search in simulated annealing is mainly local. In this work, a methodology of two phases using an automatic diversification and intensification based on memory and sensing tools is proposed. The proposed method is called Simulated Annealing with Exploratory Sensing. The computational experiments show the efficiency of the proposed method in ensuring a good exploration while finding good solutions within a similar number of iterations.https://www.mdpi.com/1999-4893/13/9/230simulated annealingexplorationintensificationsensing searchsearch memory |
| spellingShingle | Majid Almarashi Wael Deabes Hesham H. Amin Abdel-Rahman Hedar Simulated Annealing with Exploratory Sensing for Global Optimization Algorithms simulated annealing exploration intensification sensing search search memory |
| title | Simulated Annealing with Exploratory Sensing for Global Optimization |
| title_full | Simulated Annealing with Exploratory Sensing for Global Optimization |
| title_fullStr | Simulated Annealing with Exploratory Sensing for Global Optimization |
| title_full_unstemmed | Simulated Annealing with Exploratory Sensing for Global Optimization |
| title_short | Simulated Annealing with Exploratory Sensing for Global Optimization |
| title_sort | simulated annealing with exploratory sensing for global optimization |
| topic | simulated annealing exploration intensification sensing search search memory |
| url | https://www.mdpi.com/1999-4893/13/9/230 |
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