IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i2p262-d1318439.html
   My bibliography  Save this article

Chaotic Binarization Schemes for Solving Combinatorial Optimization Problems Using Continuous Metaheuristics

Author

Listed:
  • Felipe Cisternas-Caneo

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile)

  • Broderick Crawford

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile)

  • Ricardo Soto

    (Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile)

  • Giovanni Giachetti

    (Facultad de Ingeniería, Universidad Andres Bello, Antonio Varas 880, Providencia, Santiago 7591538, Chile)

  • Álex Paz

    (Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile)

  • Alvaro Peña Fritz

    (Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile)

Abstract

Chaotic maps are sources of randomness formed by a set of rules and chaotic variables. They have been incorporated into metaheuristics because they improve the balance of exploration and exploitation, and with this, they allow one to obtain better results. In the present work, chaotic maps are used to modify the behavior of the binarization rules that allow continuous metaheuristics to solve binary combinatorial optimization problems. In particular, seven different chaotic maps, three different binarization rules, and three continuous metaheuristics are used, which are the Sine Cosine Algorithm, Grey Wolf Optimizer, and Whale Optimization Algorithm. A classic combinatorial optimization problem is solved: the 0-1 Knapsack Problem. Experimental results indicate that chaotic maps have an impact on the binarization rule, leading to better results. Specifically, experiments incorporating the standard binarization rule and the complement binarization rule performed better than experiments incorporating the elitist binarization rule. The experiment with the best results was STD_TENT, which uses the standard binarization rule and the tent chaotic map.

Suggested Citation

  • Felipe Cisternas-Caneo & Broderick Crawford & Ricardo Soto & Giovanni Giachetti & Álex Paz & Alvaro Peña Fritz, 2024. "Chaotic Binarization Schemes for Solving Combinatorial Optimization Problems Using Continuous Metaheuristics," Mathematics, MDPI, vol. 12(2), pages 1-39, January.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:262-:d:1318439
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/2/262/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/2/262/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hui Lu & Xiaoteng Wang & Zongming Fei & Meikang Qiu, 2014. "The Effects of Using Chaotic Map on Improving the Performance of Multiobjective Evolutionary Algorithms," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-16, February.
    2. Martello, Silvano & Pisinger, David & Toth, Paolo, 2000. "New trends in exact algorithms for the 0-1 knapsack problem," European Journal of Operational Research, Elsevier, vol. 123(2), pages 325-332, June.
    3. Muter, İbrahim & Sezer, Zeynep, 2018. "Algorithms for the one-dimensional two-stage cutting stock problem," European Journal of Operational Research, Elsevier, vol. 271(1), pages 20-32.
    4. Y.C. Ho & D.L. Pepyne, 2002. "Simple Explanation of the No-Free-Lunch Theorem and Its Implications," Journal of Optimization Theory and Applications, Springer, vol. 115(3), pages 549-570, December.
    5. Naanaa, Anis, 2015. "Fast chaotic optimization algorithm based on spatiotemporal maps for global optimization," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 402-411.
    6. Le Wang & Yuelin Gao & Jiahang Li & Xiaofeng Wang & Rui Wang, 2021. "A Feature Selection Method by using Chaotic Cuckoo Search Optimization Algorithm with Elitist Preservation and Uniform Mutation for Data Classification," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-19, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin, Feng-Tse, 2008. "Solving the knapsack problem with imprecise weight coefficients using genetic algorithms," European Journal of Operational Research, Elsevier, vol. 185(1), pages 133-145, February.
    2. Xinbiao Wang & Yuxuan Du & Zhuozhuo Tu & Yong Luo & Xiao Yuan & Dacheng Tao, 2024. "Transition role of entangled data in quantum machine learning," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    3. Boyer, V. & Elkihel, M. & El Baz, D., 2009. "Heuristics for the 0-1 multidimensional knapsack problem," European Journal of Operational Research, Elsevier, vol. 199(3), pages 658-664, December.
    4. Akinc, Umit, 2006. "Approximate and exact algorithms for the fixed-charge knapsack problem," European Journal of Operational Research, Elsevier, vol. 170(2), pages 363-375, April.
    5. Genserik L. L. Reniers & Kenneth Sörensen, 2013. "An Approach for Optimal Allocation of Safety Resources: Using the Knapsack Problem to Take Aggregated Cost‐Efficient Preventive Measures," Risk Analysis, John Wiley & Sons, vol. 33(11), pages 2056-2067, November.
    6. Sbihi, Abdelkader, 2010. "A cooperative local search-based algorithm for the Multiple-Scenario Max-Min Knapsack Problem," European Journal of Operational Research, Elsevier, vol. 202(2), pages 339-346, April.
    7. Büther, Marcel & Briskorn, Dirk, 2007. "Reducing the 0-1 knapsack problem with a single continuous variable to the standard 0-1 knapsack problem," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 629, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    8. van der Merwe, D.J. & Hattingh, J.M., 2006. "Tree knapsack approaches for local access network design," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1968-1978, November.
    9. Víctor M. Albornoz & Gabriel E. Zamora, 2021. "Decomposition-based heuristic for the zoning and crop planning problem with adjacency constraints," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 248-265, April.
    10. Mhand Hifi & Hedi Mhalla & Slim Sadfi, 2005. "Sensitivity of the Optimum to Perturbations of the Profit or Weight of an Item in the Binary Knapsack Problem," Journal of Combinatorial Optimization, Springer, vol. 10(3), pages 239-260, November.
    11. Paola Cappanera & Marco Trubian, 2005. "A Local-Search-Based Heuristic for the Demand-Constrained Multidimensional Knapsack Problem," INFORMS Journal on Computing, INFORMS, vol. 17(1), pages 82-98, February.
    12. Benati, Stefano, 2004. "The computation of the worst conditional expectation," European Journal of Operational Research, Elsevier, vol. 155(2), pages 414-425, June.
    13. Toth, Paolo, 2000. "Optimization engineering techniques for the exact solution of NP-hard combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 125(2), pages 222-238, September.
    14. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    15. Simon Thevenin & Nicolas Zufferey & Marino Widmer, 2016. "Order acceptance and scheduling with earliness and tardiness penalties," Journal of Heuristics, Springer, vol. 22(6), pages 849-890, December.
    16. Ben O’Neill, 2022. "Smallest covering regions and highest density regions for discrete distributions," Computational Statistics, Springer, vol. 37(3), pages 1229-1254, July.
    17. Kohli, Rajeev & Krishnamurti, Ramesh & Mirchandani, Prakash, 2004. "Average performance of greedy heuristics for the integer knapsack problem," European Journal of Operational Research, Elsevier, vol. 154(1), pages 36-45, April.
    18. R. Pablo Arribillaga & G. Bergantiños, 2022. "Cooperative and axiomatic approaches to the knapsack allocation problem," Annals of Operations Research, Springer, vol. 318(2), pages 805-830, November.
    19. Stefan Hajkowicz & Andrew Higgins & Kristen Williams & Daniel P. Faith & Michael Burton, 2007. "Optimisation and the selection of conservation contracts," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 51(1), pages 39-56, March.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:262-:d:1318439. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.