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Adaptive and Improved Multi-population Based Nature-inspired Optimization Algorithms for Water Pump Station Scheduling

Author

Listed:
  • Luca O. Turci

    (Shanghai Jiao Tong University)

  • Jingcheng Wang

    (Shanghai Jiao Tong University)

  • Ibrahim Brahmia

    (Shanghai Jiao Tong University)

Abstract

A common problem that the world faces is the waste of energy. In water pump stations, the situation is not different. Employees still use the traditional, manual, and empirical operation of the water pumps. This process gradually generates unwanted losses of energy and money. To avoid such profligacy, this paper presents two Adaptive and one Improved Multi-population based nature-inspired optimization algorithms for water pump station scheduling. The main goal here is to obtain the optimal operational scheduling of each group of pumps, wasting the minimum amount of energy. Therefore, since the objective function relies on the shaft power consumption of all the pumps running together, our aim becomes feasible. We implemented and tested the algorithms in the main water pump station of Shanghai, in China. Based on traditional multi-population based nature-inspired optimization algorithms, such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO), this work adapts and improves the models to fit the complex constraints and characteristics of the system. It also compares and analyses the performance of each method used in this case study, considering the obtained results. The method which demonstrated outperformance was chosen as the best solution for the present problem.

Suggested Citation

  • Luca O. Turci & Jingcheng Wang & Ibrahim Brahmia, 2020. "Adaptive and Improved Multi-population Based Nature-inspired Optimization Algorithms for Water Pump Station Scheduling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2869-2885, July.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:9:d:10.1007_s11269-020-02588-3
    DOI: 10.1007/s11269-020-02588-3
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    References listed on IDEAS

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    1. Zhang, Zijun & Zeng, Yaohui & Kusiak, Andrew, 2012. "Minimizing pump energy in a wastewater processing plant," Energy, Elsevier, vol. 47(1), pages 505-514.
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    Cited by:

    1. Przemysław Kowalik & Magdalena Rzemieniak, 2021. "Binary Linear Programming as a Tool of Cost Optimization for a Water Supply Operator," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    2. Xiaoli Feng & Baoyun Qiu & Yongxing Wang, 2020. "Optimizing Parallel Pumping Station Operations in an Open-Channel Water Transfer System Using an Efficient Hybrid Algorithm," Energies, MDPI, vol. 13(18), pages 1-19, September.

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