Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
The energy-efficiency benefits of pumps-scheduling optimization for potable water supplies
IBM Journal of Research and Development
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Operation of pumping stations represents high costs to water supply companies. Therefore, reducing such costs through an optimal pump scheduling becomes an important issue. This work presents the use of Multiobjective Evolutionary Algorithms (MOEAs) to solve an optimal pump-scheduling problem. For the first time, six different approaches were implemented and compared. These algorithms aim to minimise four objectives: electric energy cost, pumps' maintenance cost, maximum power peak, and level variation in the reservoir. In order to consider hydraulic and technical constrains, a heuristic constrain algorithm was developed and combined with each MOEA utilised. Evaluation of experimental results of a set of metrics shows that the Strength Pareto Evolutionary Algorithm (SPEA) achieves the best performance for this problem. Moreover, SPEA's set of solutions provide pumping station operation engineers with a wide range of optimal pump schedules to chose from.