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Weiwei Bi
Researcher at Zhejiang University of Technology
Publications - 14
Citations - 338
Weiwei Bi is an academic researcher from Zhejiang University of Technology. The author has contributed to research in topics: Evolutionary algorithm & Engineering. The author has an hindex of 6, co-authored 9 publications receiving 253 citations. Previous affiliations of Weiwei Bi include University of Adelaide.
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Journal ArticleDOI
Improved genetic algorithm optimization of water distribution system design by incorporating domain knowledge
TL;DR: A new heuristic procedure called the Prescreened Heuristic Sampling Method (PHSM) is proposed and tested on seven WDS cases studies of varying size and shows that PHSM clearly performs best overall, both in terms of computational efficiency and the ability to find near-optimal solutions.
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Crowdsourcing Methods for Data Collection in Geophysics : State of the Art, Issues, and Future Directions
Feifei Zheng,Ruoling Tao,Holger R. Maier,Holger R. Maier,Holger R. Maier,Linda See,Dragan Savic,Tuqiao Zhang,Qiuwen Chen,Thaine H. Assumpção,Pan Yang,Pan Yang,Bardia Heidari,Jörg Rieckermann,Barbara S. Minsker,Weiwei Bi,Ximing Cai,Dimitri Solomatine,Ioana Popescu +18 more
TL;DR: A review of the state of the art in this field can be found in this article, where the authors present a framework for categorizing the methods used in the seven domains of geophysics considered in this review.
Journal ArticleDOI
Optimization of Water Distribution Systems Using Online Retrained Metamodels
Weiwei Bi,Graeme C. Dandy +1 more
TL;DR: In this article, the authors proposed the use of online retrained metamodels for the optimization of water distribution system (WDS) design, where artificial neural networks (ANNs) are used to replace the full hydraulic and water quality simulation models and differential evolution (DE) is utilized to carry out the optimization.
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Use of Domain Knowledge to Increase the Convergence Rate of Evolutionary Algorithms for Optimizing the Cost and Resilience of Water Distribution Systems
TL;DR: Developing and testing a method for identifying high-quality initial populations for multiobjective EAs (MOEAs) for WDS design problems aimed at minimizing cost and maximizing network resilience and the benefit of using the proposed approach compared with randomly generating initial popu...
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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
TL;DR: The use of the run-time measure metrics to reveal the underlying searching behavior of the MOEA’s operators’ searching behavior offers guidance for selecting appropriate operators for real-world water resources problems, but also builds fundamental knowledge for developing more advanced MOEAs in future.