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Test-Sheet Composition Using Analytic Hierarchy Process and Hybrid Metaheuristic Algorithm TS/BBO

Author

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  • Hong Duan
  • Wei Zhao
  • Gaige Wang
  • Xuehua Feng

Abstract

Due to the shortcomings in the traditional methods which dissatisfy the examination requirements in composing test sheet, a new method based on tabu search (TS) and biogeography-based optimization (BBO) is proposed. Firstly, according to the requirements of the test-sheet composition such as the total score, test time, chapter score, knowledge point score, question type score, cognitive level score, difficulty degree, and discrimination degree, a multi constrained multiobjective model of test-sheet composition is constructed. Secondly, analytic hierarchy process (AHP) is used to work out the weights of all the test objectives, and then the multiobjective model is turned into the single objective model by the linear weighted sum. Finally, an improved biogeography-based optimization—TS/BBO is proposed to solve test-sheet composition problem. To prove the performance of TS/BBO, TS/BBO is compared with BBO and other population-based optimization methods such as ACO, DE, ES, GA, PBIL, PSO, and SGA. The experiment illustrates that the proposed approach can effectively improve composition speed and success rate.

Suggested Citation

  • Hong Duan & Wei Zhao & Gaige Wang & Xuehua Feng, 2012. "Test-Sheet Composition Using Analytic Hierarchy Process and Hybrid Metaheuristic Algorithm TS/BBO," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-22, November.
    • Handle: RePEc:hin:jnlmpe:712752
    DOI: 10.1155/2012/712752
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    Cited by:

    1. Juan Li & Dan-dan Xiao & Hong Lei & Ting Zhang & Tian Tian, 2020. "Using Cuckoo Search Algorithm with Q -Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location," Mathematics, MDPI, vol. 8(2), pages 1-32, January.
    2. Cheng-Long Wei & Gai-Ge Wang, 2020. "Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization," Mathematics, MDPI, vol. 8(9), pages 1-23, August.
    3. Xiaoqi Zhao & Haipeng Qu & Wenjie Lv & Shuo Li & Jianliang Xu, 2021. "MooFuzz: Many-Objective Optimization Seed Schedule for Fuzzer," Mathematics, MDPI, vol. 9(3), pages 1-19, January.
    4. Jiang Li & Lihong Guo & Yan Li & Chang Liu, 2019. "Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems," Mathematics, MDPI, vol. 7(5), pages 1-35, April.

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