Multiple‐objective optimization of drinking water production strategies using a genetic algorithm

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Abstract
Finding a strategy that allows economically efficient drinking water production at minimal environmental cost is often a complex task. A systematic trade‐off among the costs and benefits of possible strategies is required for determining the optimal production configuration. Such a trade‐off involves the handling of interdependent and nonlinear relations for drawdown‐related objective categories like damage to wetland vegetation, agricultural yield depression, reduction of river base flow rates, and soil subsidence. We developed a method for multiple‐objective optimization of drinking water production by combining Busacker and Gowen's [1961] “minimum cost flow” procedure for optimal use of the transport network with a genetic algorithm (GA) for optimization of other impacts. The performance of the GA was compared with analytically determined solutions of a series of hypothetical case studies. Pareto‐optimality and uniqueness of solutions proved to be effective fitness criteria for identifying trade‐off curves with the GA.

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