An adaptive multi-population evolutionary algorithm for contamination source identification in water distribution systems

Date

2021-02

Advisors

Journal Title

Journal ISSN

ISSN

0733-9496

Volume Title

Publisher

American Society of Civil Engineers

Type

Article

Peer reviewed

Yes

Abstract

Real-time monitoring of drinking water in a water distribution system (WDS) can effectively warn and reduce safety risks. One of the challenges is to identify the contamination source through these observed data due to the real-time, non-uniqueness, and large scale characteristics. To address the real-time and non-uniqueness challenges, we propose an adaptive multi-population 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 feed-back learning mechanism. To effectively locate an optimal solution for a population, a co-evolutionary strategy is used to identify the location and the injection profile separately. Experimental results on three WDS networks show that the proposed algorithm is competitive in comparison with three other state-of-the-art evolutionary algorithms.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Multipopulation adaptation, Dynamic bilevel optimization, Evolutionary computation, Contamination source identification

Citation

Li, C., Yang, R., Zhou, L., Zeng, S., Mavrovouniotis, M., Yang, M., Yang, S. and Wu, M. (2021) An adaptive multi-population evolutionary algorithm for contamination source identification in water distribution systems. Journal of Water Resources Planning and Management, in press.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)