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Development of a Self-Adaptive Ant Colony Optimization for Designing Pipe Networks

Author

Listed:
  • Sheyda Bahoosh

    (Shahid Chamran University of Ahvaz)

  • Reza Bahoosh

    (Shahid Chamran University of Ahvaz)

  • Ali Haghighi

    (Shahid Chamran University of Ahvaz)

Abstract

Optimum design of a water distribution network leads to a nonlinearly constrained problem that is inherently discrete and multimodal. The penalty function approach is traditionally used to handle the problem constraint. Constraint violations are directly added to the objective function by applying the penalty functions. Penalty functions and coefficients are case-dependent requiring careful attention to be appropriately justified and calibrated. This is a complicated issue that requires many initial trial-and-error computations and may deform the problem search space. To overcome the problems associated with the penalty function method, this study introduces a self-adaptive ant colony optimization (SACO) method. The focus of this study is on developing a methodology for adaptive handling of the problem constraints without using penalty functions. The proposed approach is applied to a benchmark example, i.e., Hanoi pipe network. According to a comparing between the results of the new method and the conventional penalty function method, the self-adaptive scheme would remarkably increase the optimization efficiency as well as chances of reaching a global optimum design. Besides, there is no need to determine and calibrate any coefficient for meeting the problem constraints.

Suggested Citation

  • Sheyda Bahoosh & Reza Bahoosh & Ali Haghighi, 2019. "Development of a Self-Adaptive Ant Colony Optimization for Designing Pipe Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4715-4729, November.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:14:d:10.1007_s11269-019-02379-5
    DOI: 10.1007/s11269-019-02379-5
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    References listed on IDEAS

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    1. Seyem Mohammad Ashrafi & Noushin Emami Kourabbaslou, 2015. "An Efficient Adaptive Strategy for Melody Search Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 6(3), pages 1-37, July.
    2. D. Mora-Melia & P. Iglesias-Rey & F. Martinez-Solano & V. Fuertes-Miquel, 2013. "Design of Water Distribution Networks using a Pseudo-Genetic Algorithm and Sensitivity of Genetic Operators," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(12), pages 4149-4162, September.
    3. Abbas Afshar & Fariborz Massoumi & Amin Afshar & Miquel Mariño, 2015. "State of the Art Review of Ant Colony Optimization Applications in Water Resource Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(11), pages 3891-3904, September.
    4. Ali Haghighi & Hossein Samani & Zeinab Samani, 2011. "GA-ILP Method for Optimization of Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(7), pages 1791-1808, May.
    5. Milan Cisty, 2010. "Hybrid Genetic Algorithm and Linear Programming Method for Least-Cost Design of Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 1-24, January.
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    1. Yiyuan Pang & Hong Li & Pan Tang & Chao Chen, 2022. "Synchronization Optimization of Pipe Diameter and Operation Frequency in a Pressurized Irrigation Network Based on the Genetic Algorithm," Agriculture, MDPI, vol. 12(5), pages 1-16, May.

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