Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network
This study introduced a novel ant colony optimization algorithm that implements the population selection strategy of the Estimation of Distribution Algorithm and a new pheromone updating formula. It aimed to optimize the performance of G-type random high-order satisfiability logic structures embedde...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/114433/ http://psasir.upm.edu.my/id/eprint/114433/1/114433.pdf |
| _version_ | 1848866492969385984 |
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| author | Gao, Yuan Mohd Kasihmuddin, Mohd Shareduwan Chen, Ju Zheng, Chengfeng Romli, Nurul Atiqah Mansor, Mohd. Asyraf Zamri, Nur Ezlin |
| author_facet | Gao, Yuan Mohd Kasihmuddin, Mohd Shareduwan Chen, Ju Zheng, Chengfeng Romli, Nurul Atiqah Mansor, Mohd. Asyraf Zamri, Nur Ezlin |
| author_sort | Gao, Yuan |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This study introduced a novel ant colony optimization algorithm that implements the population selection strategy of the Estimation of Distribution Algorithm and a new pheromone updating formula. It aimed to optimize the performance of G-type random high-order satisfiability logic structures embedded in Discrete Hopfield Neural Networks, thereby enhancing the efficiency of the Hopfield Neural Network learning algorithm. Through comparative analysis with other metaheuristic algorithms, our model demonstrated superior performance in terms of global convergence, time complexity, and algorithm complexity. Additionally, we evaluated the learning phase, retrieval phase, and similarity analysis using various ratios of literals and clauses. It was shown that our proposed model exhibits stronger search ability compared to other metaheuristic algorithms and Exhaustive Search. Our model enhanced the efficiency of the learning phase, resulting in the number of global solutions accounting for 100 %, and significantly improved the global solution diversity. These advancements contributed to the efficiency of the model in convergence, rendering it applicable to a wide range of nonlinear classification and prediction problems. |
| first_indexed | 2025-11-15T14:21:28Z |
| format | Article |
| id | upm-114433 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:21:28Z |
| publishDate | 2024 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1144332025-03-10T01:43:27Z http://psasir.upm.edu.my/id/eprint/114433/ Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network Gao, Yuan Mohd Kasihmuddin, Mohd Shareduwan Chen, Ju Zheng, Chengfeng Romli, Nurul Atiqah Mansor, Mohd. Asyraf Zamri, Nur Ezlin This study introduced a novel ant colony optimization algorithm that implements the population selection strategy of the Estimation of Distribution Algorithm and a new pheromone updating formula. It aimed to optimize the performance of G-type random high-order satisfiability logic structures embedded in Discrete Hopfield Neural Networks, thereby enhancing the efficiency of the Hopfield Neural Network learning algorithm. Through comparative analysis with other metaheuristic algorithms, our model demonstrated superior performance in terms of global convergence, time complexity, and algorithm complexity. Additionally, we evaluated the learning phase, retrieval phase, and similarity analysis using various ratios of literals and clauses. It was shown that our proposed model exhibits stronger search ability compared to other metaheuristic algorithms and Exhaustive Search. Our model enhanced the efficiency of the learning phase, resulting in the number of global solutions accounting for 100 %, and significantly improved the global solution diversity. These advancements contributed to the efficiency of the model in convergence, rendering it applicable to a wide range of nonlinear classification and prediction problems. Elsevier 2024 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/114433/1/114433.pdf Gao, Yuan and Mohd Kasihmuddin, Mohd Shareduwan and Chen, Ju and Zheng, Chengfeng and Romli, Nurul Atiqah and Mansor, Mohd. Asyraf and Zamri, Nur Ezlin (2024) Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network. Applied Soft Computing, 166. art. no. 112192. pp. 1-37. ISSN 1568-4946; eISSN: 1568-4946 https://linkinghub.elsevier.com/retrieve/pii/S1568494624009669 10.1016/j.asoc.2024.112192 |
| spellingShingle | Gao, Yuan Mohd Kasihmuddin, Mohd Shareduwan Chen, Ju Zheng, Chengfeng Romli, Nurul Atiqah Mansor, Mohd. Asyraf Zamri, Nur Ezlin Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network |
| title | Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network |
| title_full | Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network |
| title_fullStr | Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network |
| title_full_unstemmed | Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network |
| title_short | Binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network |
| title_sort | binary ant colony optimization algorithm in learning random satisfiability logic for discrete hopfield neural network |
| url | http://psasir.upm.edu.my/id/eprint/114433/ http://psasir.upm.edu.my/id/eprint/114433/ http://psasir.upm.edu.my/id/eprint/114433/ http://psasir.upm.edu.my/id/eprint/114433/1/114433.pdf |