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The Effect Evaluation of Music Schedule Problem Based on the Improved Linear Programming Method

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Exploration of Novel Intelligent Optimization Algorithms (ISICA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1590))

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Abstract

This paper collected the actual data of music schedule problem of Chinese colleges and universities, a new linear regression analysis method is applied to the problem, it is concluded that there is a linear correlation of music schedule problem linear and course effect, it designs schedule problem of music effect evaluation model and establishes a schedule problem efficiency objective function. The new linear regression analysis method is applied to solve the optimal value and makes the music schedule problems to get the best effect. The results show that the method has reliable convergence, high convergence rate and solution precision.

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References

  1. Muzaffar, M., Eusuffand, K., Lansey, E.: Optimization of water distribution net work design using the shuffled frog leaping algorithm. J. Water Resources Plann. Manage. 129(3), 210–225 (2003)

    Google Scholar 

  2. Ebeltagi, E., Hegazy, T., Grierson, D.: Comparison among five evolutionary based optimization algorithm. Adv. Eng. Inform. 19(l), 43–53 (2005)

    Google Scholar 

  3. Eusuff, M.M., Lansey, K.E., Pasha, F.: Shuffled frog leaping algorithm: a memetic meta-heuristic for discrete Optimization. Eng. Optim. 38(2), 129–154 (2006)

    Article  MathSciNet  Google Scholar 

  4. Elbeltagi, E., Hegazy, T., Grierson, D.: A modified shuffled frog leaping algorithm: applications to project management. Struct. Infra Struct. Eng. 3(1), 125–130 (2007)

    Google Scholar 

  5. Zhang, X., Hu, X., Cui, G.: An improved shuffled frog leaping algorithm with cognitive behavior. In: Proceedings of the 7th World Congress on Intelligent Control and Automation. Chongqing, pp. 6197–6202 (2008)

    Google Scholar 

  6. Zongyi, X., Cuijun, Z.: The knapsack problem solving based on shuffled frog leaping algorithm. Sci. Technol. Eng. 9(15), 4363–4365 (2009)

    Google Scholar 

  7. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. San Franciso, CA: Freeman 4, 109–122 (1979)

    MATH  Google Scholar 

  8. Holland, J.H.: Genetic algorithms and the optimal allocation of trials. SIAM J. Comput. 2(2), 89–104 (1973)

    Article  MathSciNet  Google Scholar 

  9. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed Optimization by Ant Colonies[A] Proc 1st European Conf Artificial Life Plans, pp. 134–142. Elsevier, France (1991)

    Google Scholar 

  10. Eberhart, R., Kennedy, J.: A new optimizer using particles swarm theory. In: Proceedings of 6th International Symposium on Micro Machine and Human Science. Nagoya: IEEE Service Center, Piscataway, pp. 39–43 (1995)

    Google Scholar 

  11. Dasgupta, D., Forrest, S.: Artificial Immune Systems and Their Applications [M], pp. 267–277. Spring-Verlag, Berlin (1998)

    Google Scholar 

  12. Wang, L.: Intelligent Optimization Algorithms Applications. Tsinghua University Press, Beijing (2001)

    Google Scholar 

Download references

Acknowledgments

The work described in this paper was fully supported by Quality Engineering project of Guangdong University of Science and Technology (No. GKZLGC2021187) and Science and technology research project of Education Department of Jiangxi Province (No. GJJ191462).

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Correspondence to Yueguang Li .

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Wei, H., Zhang, Q., Li, M., Li, Y. (2022). The Effect Evaluation of Music Schedule Problem Based on the Improved Linear Programming Method. In: Li, K., Liu, Y., Wang, W. (eds) Exploration of Novel Intelligent Optimization Algorithms. ISICA 2021. Communications in Computer and Information Science, vol 1590. Springer, Singapore. https://doi.org/10.1007/978-981-19-4109-2_36

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  • DOI: https://doi.org/10.1007/978-981-19-4109-2_36

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-4108-5

  • Online ISBN: 978-981-19-4109-2

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