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Multiobjective evolutionary optimization of water distribution systems: Exploiting diversity with infeasible
Tiku T Tanyimboh1, Alemtsehay G Seyoum1
1Department of Civil and Environmental Engineering, University of Strathclyde, James Weir Building, 75 Montrose Street, Glasgow G1 1XJ, UK.
Journal of Environmental Management
|September 4, 2016
Summary
This study optimized water distribution systems using evolutionary algorithms, efficiently finding cost-effective solutions. A larger population size (1000) yielded slightly better results for pipe cost savings.
Area of Science:
- Engineering
- Computer Science
- Environmental Science
Background:
- Water distribution systems require efficient optimization for cost-effectiveness and reliability.
- Constraint handling in multi-objective evolutionary optimization (MOEO) is crucial for complex systems.
Purpose of the Study:
- To investigate the computational efficiency of MOEO for water distribution system optimization.
- To evaluate a novel constraint handling approach promoting feasible and infeasible solution diversity.
Main Methods:
- Developed a MOEO approach fostering co-existence of feasible and infeasible solutions using Pareto dominance.
- Exploited non-dominated infeasible solutions for boundary search and gene pool diversity.
- Analyzed performance with varying population sizes against decision variables in a real-world system.
Main Results:
- The optimization algorithm demonstrated efficiency, stability, and robustness.
- Optimal and near-optimal solutions were found reliably.
- Achieved significant cost savings (up to 48.2%) in pipe costs for the real-world system.
Conclusions:
- The proposed MOEO constraint handling is effective for water distribution system optimization.
- A population size of 1000 provided marginally superior results compared to 200.
- The method consistently met flow and pressure requirements while reducing costs.
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