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A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

Author

Listed:
  • José García

    (Escuela de Ingeniería en Construcción, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
    These authors contributed equally to this work.)

  • Victor Yepes

    (Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 València, Spain
    These authors contributed equally to this work.)

  • José V. Martí

    (Institute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 València, Spain
    These authors contributed equally to this work.)

Abstract

The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO 2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.

Suggested Citation

  • José García & Victor Yepes & José V. Martí, 2020. "A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem," Mathematics, MDPI, vol. 8(4), pages 1-22, April.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:4:p:555-:d:344071
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    References listed on IDEAS

    as
    1. José García & Christopher Pope & Francisco Altimiras, 2017. "A Distributed -Means Segmentation Algorithm Applied to Lobesia botrana Recognition," Complexity, Hindawi, vol. 2017, pages 1-14, August.
    2. Martin, Simon & Ouelhadj, Djamila & Beullens, Patrick & Ozcan, Ender & Juan, Angel A. & Burke, Edmund K., 2016. "A multi-agent based cooperative approach to scheduling and routing," European Journal of Operational Research, Elsevier, vol. 254(1), pages 169-178.
    3. Broderick Crawford & Ricardo Soto & Gino Astorga & José García & Carlos Castro & Fernando Paredes, 2017. "Putting Continuous Metaheuristics to Work in Binary Search Spaces," Complexity, Hindawi, vol. 2017, pages 1-19, May.
    4. Víctor Yepes & José V. Martí & José García, 2020. "Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls," Sustainability, MDPI, vol. 12(7), pages 1-18, April.
    5. Jana Ries & Patrick Beullens, 2015. "A semi-automated design of instance-based fuzzy parameter tuning for metaheuristics based on decision tree induction," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(5), pages 782-793, May.
    6. José García & Francisco Altimiras & Alvaro Peña & Gino Astorga & Oscar Peredo, 2018. "A Binary Cuckoo Search Big Data Algorithm Applied to Large-Scale Crew Scheduling Problems," Complexity, Hindawi, vol. 2018, pages 1-15, July.
    7. Juan, Angel A. & Faulin, Javier & Grasman, Scott E. & Rabe, Markus & Figueira, Gonçalo, 2015. "A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems," Operations Research Perspectives, Elsevier, vol. 2(C), pages 62-72.
    8. El-Ghazali Talbi, 2016. "Combining metaheuristics with mathematical programming, constraint programming and machine learning," Annals of Operations Research, Springer, vol. 240(1), pages 171-215, May.
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    Cited by:

    1. Marcelo Becerra-Rozas & José Lemus-Romani & Felipe Cisternas-Caneo & Broderick Crawford & Ricardo Soto & Gino Astorga & Carlos Castro & José García, 2022. "Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review," Mathematics, MDPI, vol. 11(1), pages 1-32, December.

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