Multiswarm Multiobjective Particle Swarm Optimization with Simulated Annealing for Extracting Multiple Tests
Journal article, Peer reviewed
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Original versionBui, T., Nguyen, T., Huynh, H. M., Vo, B., Chun-Wei Lin, J., & Hong, T.-P. (2020). Multiswarm Multiobjective Particle Swarm Optimization with Simulated Annealing for Extracting Multiple Tests. Scientific Programming, 2020, Article ID 7081653, 1-15. 10.1155/2020/7081653
Education is mandatory, and much research has been invested in this sector. An important aspect of education is how to evaluate the learners’ progress. Multiple-choice tests are widely used for this purpose. The tests for learners in the same exam should come in equal difficulties for fair judgment. Thus, this requirement leads to the problem of generating tests with equal difficulties, which is also known as the specific case of generating tests with a single objective. However, in practice, multiple requirements (objectives) are enforced while making tests. For example, teachers may require the generated tests to have the same difficulty and the same test duration. In this paper, we propose the use of Multiswarm Multiobjective Particle Swarm Optimization (MMPSO) for generating k tests with multiple objectives in a single run. Additionally, we also incorporate Simulated Annealing (SA) to improve the diversity of tests and the accuracy of solutions. The experimental results with various criteria show that our approaches are effective and efficient for the problem of generating multiple tests.