A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization
There are many tricky optimization problems in real life, and metaheuristic algorithms are the most effective way to solve optimization problems at a lower cost. The dung beetle optimization algorithm (DBO) is a more innovative algorithm proposed in 2022, which is affected by the action of dung beet...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682300830X |
| _version_ | 1797659935618629632 |
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| author | Zhendong Wang Lili Huang Shuxin Yang Dahai Li Daojing He Sammy Chan |
| author_facet | Zhendong Wang Lili Huang Shuxin Yang Dahai Li Daojing He Sammy Chan |
| author_sort | Zhendong Wang |
| collection | DOAJ |
| description | There are many tricky optimization problems in real life, and metaheuristic algorithms are the most effective way to solve optimization problems at a lower cost. The dung beetle optimization algorithm (DBO) is a more innovative algorithm proposed in 2022, which is affected by the action of dung beetles such as ball rolling, foraging, and reproduction. Therefore, A dung beetle optimization algorithm is proposed based on quasi-oppositional learning and Q-learning (QOLDBO). First, the quantum state update idea is cleverly integrated into quasi-oppositional learning to increase the randomness of the generated population. And the best behavior pattern is selected by adding Q-learning in the rolling stage to improve the search effect. In addition, the variable spiral local domain method is proposed to make up for the shortage of developing only around the neighborhood optimum. For the optimal solution of each iteration, the dimensional adaptive Gaussian variation is selected and the optimal solution is retained. Experimental performance tests show that QOLDBO performs well in both benchmark test functions and CEC 2017. Simultaneously, the validity of the algorithm is verified on several classical practical application engineering problems. |
| first_indexed | 2024-03-11T18:22:30Z |
| format | Article |
| id | doaj.art-be00921c8920406e91270fbd1d6f864a |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-11T18:22:30Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-be00921c8920406e91270fbd1d6f864a2023-10-15T04:36:55ZengElsevierAlexandria Engineering Journal1110-01682023-10-0181469488A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimizationZhendong Wang0Lili Huang1Shuxin Yang2Dahai Li3Daojing He4Sammy Chan5School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China; Corresponding author.School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, ChinaDepartment of Electrical Engineering, City University of Hong Kong, Hong Kong 999077, ChinaThere are many tricky optimization problems in real life, and metaheuristic algorithms are the most effective way to solve optimization problems at a lower cost. The dung beetle optimization algorithm (DBO) is a more innovative algorithm proposed in 2022, which is affected by the action of dung beetles such as ball rolling, foraging, and reproduction. Therefore, A dung beetle optimization algorithm is proposed based on quasi-oppositional learning and Q-learning (QOLDBO). First, the quantum state update idea is cleverly integrated into quasi-oppositional learning to increase the randomness of the generated population. And the best behavior pattern is selected by adding Q-learning in the rolling stage to improve the search effect. In addition, the variable spiral local domain method is proposed to make up for the shortage of developing only around the neighborhood optimum. For the optimal solution of each iteration, the dimensional adaptive Gaussian variation is selected and the optimal solution is retained. Experimental performance tests show that QOLDBO performs well in both benchmark test functions and CEC 2017. Simultaneously, the validity of the algorithm is verified on several classical practical application engineering problems.http://www.sciencedirect.com/science/article/pii/S111001682300830XDung beetle optimizerQuantum stateQ-learningVariable screwDimensional Gaussian mutation |
| spellingShingle | Zhendong Wang Lili Huang Shuxin Yang Dahai Li Daojing He Sammy Chan A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization Alexandria Engineering Journal Dung beetle optimizer Quantum state Q-learning Variable screw Dimensional Gaussian mutation |
| title | A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization |
| title_full | A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization |
| title_fullStr | A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization |
| title_full_unstemmed | A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization |
| title_short | A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization |
| title_sort | quasi oppositional learning of updating quantum state and q learning based on the dung beetle algorithm for global optimization |
| topic | Dung beetle optimizer Quantum state Q-learning Variable screw Dimensional Gaussian mutation |
| url | http://www.sciencedirect.com/science/article/pii/S111001682300830X |
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