An improved genetic bat algorithm for unconstrained global optimization problems
Metaheuristic search algorithms have been in use for quite a while to optimally solve complex searching problems with ease. Nowadays, nature inspired swarm intelligent algorithms have become quite popular due to their propensity for finding optimal solutions with agility. Genetic algorithm (GA) is s...
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| Format: | Conference or Workshop Item |
| Language: | English |
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Association for Computing Machinery (ACM)
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/28710/1/2.1%20An%20improved%20genetic%20bat%20algorithm%20for%20unconstrained.pdf |
| _version_ | 1825813401952059392 |
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| author | Muhammad Zubair, Rehman Kamal Z., Zamli Abdullah, Nasser |
| author_facet | Muhammad Zubair, Rehman Kamal Z., Zamli Abdullah, Nasser |
| author_sort | Muhammad Zubair, Rehman |
| collection | UMP |
| description | Metaheuristic search algorithms have been in use for quite a while to optimally solve complex searching problems with ease. Nowadays, nature inspired swarm intelligent algorithms have become quite popular due to their propensity for finding optimal solutions with agility. Genetic algorithm (GA) is successfully applied in several engineering fields for the past four decades but it still has a problem of slow convergence due to its reliability on the initial state of its operators. Therefore, to ensure that GA converges to a global solution, this paper proposed a two-stage improved Genetic Bat algorithm (GBa) in which the GA finds the optimal solution first and then Bat starts from where the GA has converged. This multi-stage optimization ensures that optimal solution is always reached through fine balance in between exploration and exploitation behavior of Genetic algorithm. |
| first_indexed | 2024-03-06T12:43:29Z |
| format | Conference or Workshop Item |
| id | UMPir28710 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:43:29Z |
| publishDate | 2020 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | UMPir287102021-02-10T06:55:04Z http://umpir.ump.edu.my/id/eprint/28710/ An improved genetic bat algorithm for unconstrained global optimization problems Muhammad Zubair, Rehman Kamal Z., Zamli Abdullah, Nasser QA76 Computer software Metaheuristic search algorithms have been in use for quite a while to optimally solve complex searching problems with ease. Nowadays, nature inspired swarm intelligent algorithms have become quite popular due to their propensity for finding optimal solutions with agility. Genetic algorithm (GA) is successfully applied in several engineering fields for the past four decades but it still has a problem of slow convergence due to its reliability on the initial state of its operators. Therefore, to ensure that GA converges to a global solution, this paper proposed a two-stage improved Genetic Bat algorithm (GBa) in which the GA finds the optimal solution first and then Bat starts from where the GA has converged. This multi-stage optimization ensures that optimal solution is always reached through fine balance in between exploration and exploitation behavior of Genetic algorithm. Association for Computing Machinery (ACM) 2020-02 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/28710/1/2.1%20An%20improved%20genetic%20bat%20algorithm%20for%20unconstrained.pdf Muhammad Zubair, Rehman and Kamal Z., Zamli and Abdullah, Nasser (2020) An improved genetic bat algorithm for unconstrained global optimization problems. In: 9th International Conference on Software and Computer Applications (ICSCA 2020) , 18 - 21 Feb. 2020 , Langkawi, Malaysia. pp. 94-98.. ISBN 978-145037665-5 (Published) https://doi.org/10.1145/3384544.3384603 |
| spellingShingle | QA76 Computer software Muhammad Zubair, Rehman Kamal Z., Zamli Abdullah, Nasser An improved genetic bat algorithm for unconstrained global optimization problems |
| title | An improved genetic bat algorithm for unconstrained global optimization problems |
| title_full | An improved genetic bat algorithm for unconstrained global optimization problems |
| title_fullStr | An improved genetic bat algorithm for unconstrained global optimization problems |
| title_full_unstemmed | An improved genetic bat algorithm for unconstrained global optimization problems |
| title_short | An improved genetic bat algorithm for unconstrained global optimization problems |
| title_sort | improved genetic bat algorithm for unconstrained global optimization problems |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/28710/1/2.1%20An%20improved%20genetic%20bat%20algorithm%20for%20unconstrained.pdf |
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