OPTIMIZATION OF WATER RESOURCES SYSTEMS USING MULTI-OBJECTIVE EVOLUTION STRATEGIES

Muschalla, Dirk

This paper deals with the development of a new multi-objective evolution strategy in combination with an integrated pollution-load and water-quality model. The optimization algorithm combines the advantages of the Non-Dominated Sorting Genetic Algorithm and Self-Adaptive Evolution Strategies. The identification of a good spread of solutions on the pareto-optimum front and the optimization of a large number of decision variables equally demands numerous simulation runs. In addition, statements with regard to the frequency of critical concentrations and peak discharges require continuous long-term simulations. Therefore, a fast operating integrated simulation model is needed providing the required precision of the results. For this purpose, a hydrological deterministic pollution-load model has been coupled with a river water-quality and a rainfall-runoff model. Wastewater treatment plants are simulated in a simplified way. The functionality of the optimization and simulation tool has been validated by analyzing a real catchment area including sewer system, WWTP, water body and natural river basin. For the optimization/rehabilitation of the urban drainage system, both innovative and approved measures have been examined and used as decision variables. As objective functions, investment costs and river water quality criteria have been used.

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Muschalla, Dirk: OPTIMIZATION OF WATER RESOURCES SYSTEMS USING MULTI-OBJECTIVE EVOLUTION STRATEGIES. 2006.

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