CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization
In evolutionary computation, systematically structuring the population is used to manage the evolution process. Thus controlling the amount of diversity during the algorithm search. Island-based, hierarchical-based, and cellular automata are the most popular structured population models utilized fo...
محفوظ في:
| المؤلف الرئيسي: | |
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| منشور في: |
2022
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| الوصول للمادة أونلاين: | https://dspace.auk.edu.kw/handle/11675/9625 https://www.sciencedirect.com/science/article/abs/pii/S0957417421017188 |
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| _version_ | 1870679720912224256 |
|---|---|
| author | Abou Doush, Iyad |
| author_facet | Abou Doush, Iyad |
| author_role | author |
| dc.contributor.none.fl_str_mv | Mohammed Azmi Al-Betar; Mohammed Awadallah; Zaid Abdi Alkareem Alyasseri; Osama Alomari; Sharif Naser Makhadmeh; Ammar Kamal Abasi |
| dc.creator.none.fl_str_mv | Abou Doush, Iyad |
| dc.date.none.fl_str_mv | 2022-01-13 2023-04-09T10:32:28Z 2023-04-09T10:32:28Z |
| dc.identifier.none.fl_str_mv | Awadallah, M. A., Al-Betar, M. A., Doush, I. A., Makhadmeh, S. N., Alyasseri, Z. A. A., Abasi, A. K., & Alomari, O. A. (2022). CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization. Expert Systems with Applications, 194, 116431. https://doi.org/10.1016/j.eswa.2021.116431 https://dspace.auk.edu.kw/handle/11675/9625 https://www.sciencedirect.com/science/article/abs/pii/S0957417421017188 |
| dc.publisher.none.fl_str_mv | Expert Systems with Applications |
| dc.relation.none.fl_str_mv | College of Engineering & Applied Sciences |
| dc.title.none.fl_str_mv | CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization |
| dc.type.none.fl_str_mv | Peer Reviewed Journal Article info:eu-repo/semantics/publishedVersion |
| description | In evolutionary computation, systematically structuring the population is used to manage the evolution process. Thus controlling the amount of diversity during the algorithm search. Island-based, hierarchical-based, and cellular automata are the most popular structured population models utilized for evolutionary algorithms to improve their diversity and convergence. Specifically, the cellular automata model arranges the population of individuals in a 2D grid spatial structure where the concepts of cells and their neighbors are considered to drive the evolution step. A recent population-based algorithm is proposed called the crow search algorithm (CSA). It stimulates the behavior of crows in keeping their food in hidden places to be retrieved when needed. Like other population-based algorithms, CSA endures from slow convergence because of inadequate diversity. In this paper, the cellular automata model is incorporated with the optimization framework of CSA to control its diversity during the search, thus boosts its efficiency. The proposed method is abbreviated as CCSA. In CCSA, the population is structured as a 2D grid where the active population is iteratively determined. At each iteration, each individual is updated based on current neighboring individuals determined by the topological neighborhood shapes and follows the best neighboring individual in its active population. The CCSA is evaluated using 23 standard benchmark functions well-circulated in the literature. Initially, six cellular topological neighborhood shapes (i.e., L5, L9, C9, C13, C21, and C25) are studied with various problem sizes to investigate their impact on the CCSA convergence. Comparative evaluations against twelve state-of-the-art methods, including four CSA versions, are conducted. The comparative results prove the effectiveness of the proposed CCSA. The diversity of the proposed CCSA has also been stressed using convergence analysis and hamming distance. For further validations, the proposed algorithm is evaluated using three real-world problems published in IEEE-CEC2011 and welded beam design problems. Again, the proposed CCSA yields more fruitful results than other well-established methods using real-world problems. In conclusion, this paper provides an efficient alternative of CSA to perfectly maintain the diversity of the search, which can be further tested using other optimization problems. |
| id | AUKR_54703cf5bbb1176b9fbe2d3d7c0d8b75 |
| identifier_str_mv | Awadallah, M. A., Al-Betar, M. A., Doush, I. A., Makhadmeh, S. N., Alyasseri, Z. A. A., Abasi, A. K., & Alomari, O. A. (2022). CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization. Expert Systems with Applications, 194, 116431. https://doi.org/10.1016/j.eswa.2021.116431 |
| network_acronym_str | AUKR |
| network_name_str | AU Kuwait Rep |
| oai_identifier_str | oai:dspace.auk.edu.kw:11675/9625 |
| publishDate | 2022 |
| publisher.none.fl_str_mv | Expert Systems with Applications |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimizationAbou Doush, IyadIn evolutionary computation, systematically structuring the population is used to manage the evolution process. Thus controlling the amount of diversity during the algorithm search. Island-based, hierarchical-based, and cellular automata are the most popular structured population models utilized for evolutionary algorithms to improve their diversity and convergence. Specifically, the cellular automata model arranges the population of individuals in a 2D grid spatial structure where the concepts of cells and their neighbors are considered to drive the evolution step. A recent population-based algorithm is proposed called the crow search algorithm (CSA). It stimulates the behavior of crows in keeping their food in hidden places to be retrieved when needed. Like other population-based algorithms, CSA endures from slow convergence because of inadequate diversity. In this paper, the cellular automata model is incorporated with the optimization framework of CSA to control its diversity during the search, thus boosts its efficiency. The proposed method is abbreviated as CCSA. In CCSA, the population is structured as a 2D grid where the active population is iteratively determined. At each iteration, each individual is updated based on current neighboring individuals determined by the topological neighborhood shapes and follows the best neighboring individual in its active population. The CCSA is evaluated using 23 standard benchmark functions well-circulated in the literature. Initially, six cellular topological neighborhood shapes (i.e., L5, L9, C9, C13, C21, and C25) are studied with various problem sizes to investigate their impact on the CCSA convergence. Comparative evaluations against twelve state-of-the-art methods, including four CSA versions, are conducted. The comparative results prove the effectiveness of the proposed CCSA. The diversity of the proposed CCSA has also been stressed using convergence analysis and hamming distance. For further validations, the proposed algorithm is evaluated using three real-world problems published in IEEE-CEC2011 and welded beam design problems. Again, the proposed CCSA yields more fruitful results than other well-established methods using real-world problems. In conclusion, this paper provides an efficient alternative of CSA to perfectly maintain the diversity of the search, which can be further tested using other optimization problems.Expert Systems with ApplicationsMohammed Azmi Al-Betar; Mohammed Awadallah; Zaid Abdi Alkareem Alyasseri; Osama Alomari; Sharif Naser Makhadmeh; Ammar Kamal Abasi2023-04-09T10:32:28Z2023-04-09T10:32:28Z2022-01-13Peer ReviewedJournal Articleinfo:eu-repo/semantics/publishedVersionAwadallah, M. A., Al-Betar, M. A., Doush, I. A., Makhadmeh, S. N., Alyasseri, Z. A. A., Abasi, A. K., & Alomari, O. A. (2022). CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization. Expert Systems with Applications, 194, 116431. https://doi.org/10.1016/j.eswa.2021.116431https://dspace.auk.edu.kw/handle/11675/9625https://www.sciencedirect.com/science/article/abs/pii/S0957417421017188College of Engineering & Applied Sciencesoai:dspace.auk.edu.kw:11675/96252023-04-09T10:32:28Z |
| spellingShingle | CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization Abou Doush, Iyad |
| status_str | publishedVersion |
| title | CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization |
| title_full | CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization |
| title_fullStr | CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization |
| title_full_unstemmed | CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization |
| title_short | CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization |
| title_sort | CCSA: Cellular Crow Search Algorithm with topological neighborhood shapes for optimization |
| url | https://dspace.auk.edu.kw/handle/11675/9625 https://www.sciencedirect.com/science/article/abs/pii/S0957417421017188 |