A Hybrid Particle Swarm Optimizer for Curriculum Sequencing Problem
Curriculum sequencing problem is crucial to e-learning system, which is a NP-hard optimization problem and commonly solved by swarm intelligence. As a form of swarm intelligence, particle swarm optimization (PSO) is widely used in various kinds of optimization problems. However, PSO is found ineffec...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/5291296 |
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| _version_ | 1850174990206369792 |
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| author | Xianjie Peng Xiaonan Sun Zhen He |
| author_facet | Xianjie Peng Xiaonan Sun Zhen He |
| author_sort | Xianjie Peng |
| collection | DOAJ |
| description | Curriculum sequencing problem is crucial to e-learning system, which is a NP-hard optimization problem and commonly solved by swarm intelligence. As a form of swarm intelligence, particle swarm optimization (PSO) is widely used in various kinds of optimization problems. However, PSO is found ineffective in complex optimization problems. The main reason is that PSO is ineffective in diversity preservation, leading to high risks to be trapped by the local optima. To solve this problem, a novel hybrid PSO algorithm is proposed in this study. First, a competitive-genetic crossover strategy is proposed for PSO to balance the convergence and diversity. Second, an adaptive polynomial mutation is introduced in PSO to further improve its diversity preservation ability. Furthermore, a curriculum scheduling model is proposed, where several constraints are taken into considerations to ensure the practicability of the curriculum sequencing. The numerical comparison experiments show that the proposed algorithm is effective in solving function optimization in comparison to several popular PSO variants; furthermore, for the optimization of the designed curriculum sequencing problem, the proposed algorithm shows significant advantages over the compared algorithms with respect to the degree of the satisfaction of the objectives, i.e., 20, 14, and 5 percentages higher, respectively. |
| format | Article |
| id | doaj-art-d6f2f370d22d4239a1402c80cc7d016a |
| institution | OA Journals |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-d6f2f370d22d4239a1402c80cc7d016a2025-08-20T02:19:33ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/5291296A Hybrid Particle Swarm Optimizer for Curriculum Sequencing ProblemXianjie Peng0Xiaonan Sun1Zhen He2Department of Human ResourcesInstitute of Vocational EducationFaculty of EducationCurriculum sequencing problem is crucial to e-learning system, which is a NP-hard optimization problem and commonly solved by swarm intelligence. As a form of swarm intelligence, particle swarm optimization (PSO) is widely used in various kinds of optimization problems. However, PSO is found ineffective in complex optimization problems. The main reason is that PSO is ineffective in diversity preservation, leading to high risks to be trapped by the local optima. To solve this problem, a novel hybrid PSO algorithm is proposed in this study. First, a competitive-genetic crossover strategy is proposed for PSO to balance the convergence and diversity. Second, an adaptive polynomial mutation is introduced in PSO to further improve its diversity preservation ability. Furthermore, a curriculum scheduling model is proposed, where several constraints are taken into considerations to ensure the practicability of the curriculum sequencing. The numerical comparison experiments show that the proposed algorithm is effective in solving function optimization in comparison to several popular PSO variants; furthermore, for the optimization of the designed curriculum sequencing problem, the proposed algorithm shows significant advantages over the compared algorithms with respect to the degree of the satisfaction of the objectives, i.e., 20, 14, and 5 percentages higher, respectively.http://dx.doi.org/10.1155/2022/5291296 |
| spellingShingle | Xianjie Peng Xiaonan Sun Zhen He A Hybrid Particle Swarm Optimizer for Curriculum Sequencing Problem Discrete Dynamics in Nature and Society |
| title | A Hybrid Particle Swarm Optimizer for Curriculum Sequencing Problem |
| title_full | A Hybrid Particle Swarm Optimizer for Curriculum Sequencing Problem |
| title_fullStr | A Hybrid Particle Swarm Optimizer for Curriculum Sequencing Problem |
| title_full_unstemmed | A Hybrid Particle Swarm Optimizer for Curriculum Sequencing Problem |
| title_short | A Hybrid Particle Swarm Optimizer for Curriculum Sequencing Problem |
| title_sort | hybrid particle swarm optimizer for curriculum sequencing problem |
| url | http://dx.doi.org/10.1155/2022/5291296 |
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