Knowledge-based multi-objective genetic algorithms for the design of water distribution networks
Johns, M; Keedwell, E; Savic, D
Date: 29 November 2019
Article
Journal
Journal of Hydroinformatics
Publisher
IWA Publishing for IAHR-IWA-IAHS Joint Committee on Hydroinformatics
Publisher DOI
Abstract
Water system design problems are complex and difficult to optimise. It has been demonstrated that involving engineering expertise
is required to tackle real-world problems. This paper presents two engineering inspired hybrid evolutionary algorithms for the multiobjective design of water distribution networks. The heuristics are developed ...
Water system design problems are complex and difficult to optimise. It has been demonstrated that involving engineering expertise
is required to tackle real-world problems. This paper presents two engineering inspired hybrid evolutionary algorithms for the multiobjective design of water distribution networks. The heuristics are developed from traditional design approaches of practicing
engineers and integrated into the mutation operator of a multi-objective evolutionary algorithm. The first engineering inspired
heuristic is designed to identify hydraulic bottlenecks within the network and eliminate them with a view to speeding up the
algorithm’s search to the feasible solution space. The second heuristic is based on the notion that pipe diameters smoothly transition
from large, at the source, to small at the extremities of the network. The performance of the engineering inspired hybrid evolutionary
algorithms is compared to NSGA-II and assessed on three networks of varying complexity, two benchmarks and one real-world
network. The experiments presented in this paper demonstrate that the incorporation of engineering expertise can improve
evolutionary algorithm performance often producing superior solutions both in terms of mathematical optimality but also
engineering feasibility.
Computer Science
Faculty of Environment, Science and Economy
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