Abstract
This paper presents a many-objective analysis framework to handle large real-world water distribution system design problems (WDSDP), which is a typically difficult infrastructure engineering optimization problem type. Six objectives are formulated, focusing on economic, structural and functional aspects in the operation and management of the water distribution system (WDS), and solved by Borg, which is one state-of-the-art multi-objective evolutionary algorithm (MOEA) in water resources. The framework comprehensively analyzes and reveals the underlying trade-offs among many objectives, thereby facilitating the selection of the most appropriate design solutions for real-world WDSs. A real-world WDSDP with 1278 decision variables is used to demonstrate the effectiveness of the proposed framework, and results show that it can clearly reveal the complex trade-offs among these six different objectives, and it greatly enhances the understanding of the underlying characteristics of Pareto-front solutions. The insights have great practical implications for optimally designing large real-world WDS problems.
Highlights
The multi-objective evolutionary algorithms (MOEAs) have been widely used to handle complex urban water resources and engineering problems over the last few decades
The MOEAs have been commonly used to deal with bi-objective optimization problems of water resources systems by combining the performance-related and costrelated objectives
It is not straightforward for the decision-makers to identify the most appropriate solutions from the many-objective Pareto front based on these optimization techniques [2]. This is because it is difficult to visualize the solutions with many objectives, resulting in challenges to understanding their underlying trade-offs among different competing objectives, which is especially the case for the large-scale and real-world water resource optimization problems. This issue has seriously hampered the wide up-takes of many-objective optimization techniques (e.g., MOEAs) to handle complex water resource optimization problems
Summary
The multi-objective evolutionary algorithms (MOEAs) have been widely used to handle complex urban water resources and engineering problems over the last few decades. It is not straightforward for the decision-makers to identify the most appropriate solutions from the many-objective Pareto front based on these optimization techniques [2] This is because it is difficult to visualize the solutions with many objectives (more than three), resulting in challenges to understanding their underlying trade-offs among different competing objectives, which is especially the case for the large-scale and real-world water resource optimization problems. This issue has seriously hampered the wide up-takes of many-objective optimization techniques (e.g., MOEAs) to handle complex water resource optimization problems. Through the application and analysis of this large-scale real-world WDSDP, this paper aims to find an effective approach to visualizing and analyzing the results of many-objective optimization as well as to propose an appropriate solution strategy for decision-makers for better WDS design and management
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