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Improved Understanding on the Searching Behavior of NSGA-II Operators Using Run-Time Measure Metrics with Application to Water Distribution System Design Problems

Author

Listed:
  • Feifei Zheng

    (Zhejiang University)

  • Zhexian Qi

    (Zhejiang University)

  • Weiwei Bi

    (Zhejiang University of Technology)

  • Tuqiao Zhang

    (Zhejiang University)

  • Tingchao Yu

    (Zhejiang University)

  • Yu Shao

    (Zhejiang University)

Abstract

In recent years, multi-objective evolutionary algorithms (MOEAs) have been widely used to handle various water resources problems. One challenge within MOEAs’ applications is a lack of understanding on how various operators alter a MOEA’s behavior to achieve its final performance (i.e., MOEAs are black-boxes to practitioners), and hence it is difficult to select the most appropriate operators to ensure the MOEA’s best performance for a given real-world problem. To address this issue, this study proposes the use of the run-time measure metrics to reveal the underlying searching behavior of the MOEA’s operators. The proposed methodology is demonstrated by the non-dominated sorting genetic algorithm II (NSGA-II, a widely used MOEA in water resources) with five commonly used crossover operators applied to six water distribution system design problems. Results show that the simulated binary crossover (SBX) and the simplex crossover (SPX) operators possess great ability in extending the front and finding Pareto-front solutions, respectively, while the naive crossover (NVX) strategy exhibits the overall worst performance in identifying optimal fronts. The obtained understanding on the operators’ searching behavior not only offers guidance for selecting appropriate operators for real-world water resources problems, but also builds fundamental knowledge for developing more advanced MOEAs in future.

Suggested Citation

  • Feifei Zheng & Zhexian Qi & Weiwei Bi & Tuqiao Zhang & Tingchao Yu & Yu Shao, 2017. "Improved Understanding on the Searching Behavior of NSGA-II Operators Using Run-Time Measure Metrics with Application to Water Distribution System Design Problems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1121-1138, March.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:4:d:10.1007_s11269-016-1564-7
    DOI: 10.1007/s11269-016-1564-7
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    References listed on IDEAS

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    1. J. Yazdi, 2016. "Decomposition based Multi Objective Evolutionary Algorithms for Design of Large-Scale Water Distribution Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(8), pages 2749-2766, June.
    2. Mohammad Karamouz & Sara Nazif & Mohammad Sherafat & Zahra Zahmatkesh, 2014. "Development of an Optimal Reservoir Operation Scheme Using Extended Evolutionary Computing Algorithms Based on Conflict Resolution Approach: A Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3539-3554, September.
    3. Kimberly Artita & Prakash Kaini & John Nicklow, 2013. "Examining the Possibilities: Generating Alternative Watershed-Scale BMP Designs with Evolutionary Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 3849-3863, September.
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    Cited by:

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    4. Zhong-kai Feng & Wen-jing Niu & Peng-fei Shi & Tao Yang, 2022. "Adaptive Neural-Based Fuzzy Inference System and Cooperation Search Algorithm for Simulating and Predicting Discharge Time Series Under Hydropower Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2795-2812, June.
    5. Tian Peng & Jianzhong Zhou & Chu Zhang & Na Sun, 2018. "Modeling and Combined Application of Orthogonal Chaotic NSGA-II and Improved TOPSIS to Optimize a Conceptual Hydrological Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3781-3799, September.
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