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Title: Adaptive Multipopulation Evolutionary Algorithm for Contamination Source Identification in Water Distribution Systems
Authors: Li, Changhe 
Yang, Ruixia 
Zhou, Lin 
Zeng, Sanyou 
Mavrovouniotis, Michalis 
Yang, Ming 
Yang, Shengxiang 
Wu, Min 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Contamination source identification;Dynamic bilevel optimization;Evolutionary computation;Multipopulation adaptation
Issue Date: 1-May-2021
Source: Journal of Water Resources Planning and Management, 2021, vol. 147, iss. 5
Volume: 147
Issue: 5
Journal: Journal of Water Resources Planning and Management 
Abstract: Real-time monitoring of drinking water in a water distribution system (WDS) can effectively warn of and reduce safety risks. One of the challenges is to identify the contamination source through these observed data due to the real-time, nonuniqueness, and large-scale characteristics. To address the real-time and nonuniqueness challenges, we propose an adaptive multipopulation evolutionary optimization algorithm to determine the real-time characteristics of contamination sources, where each population aims to locate and track a different global optimum. The algorithm adaptively adjusts the number of populations using a feedback learning mechanism. To effectively locate an optimal solution for a population, a coevolutionary strategy is used to identify the location and the injection profile separately. Experimental results from three WDS networks show that the proposed algorithm is competitive in comparison with three other state-of-the-art evolutionary algorithms.
ISSN: 07339496
DOI: 10.1061/(ASCE)WR.1943-5452.0001362
Rights: © American Society of Civil Engineers
Type: Article
Affiliation : China University of Geosciences 
Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems 
University of Cyprus 
De Montfort University 
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