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

Abstract

[1] 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.INDEX TERMS: 1884 Hydrology: Water supply; 1890 Hydrology: Wetlands; 1894 Hydrology: Instruments and techniques; 6304 Policy Sciences: Benefit-cost analysis; KEYWORDS: decision support system, multiple objective optimization, drinking water supply, genetic algorithm, evolutionary programming Citation: Vink K., and P. Schot, Multiple-objective optimization of drinking water production strategies using a genetic algorithm,

A macro-evolutionary multi-objective immune algorithm with application to optimal allocation of water resources in Dongjiang River basins, South China

“…MA randomly chooses only one objective to optimize at each generation and therefore, cannot guarantee the convergence of Pareto-optimal solutions during the optimization procedure (Vink and Schot 2002). However, many of the memory cells are diversified and noninferior, and only a few solutions need to be deleted at a time.…”

“…The optimization problem becomes more complicated as the number of interaction factors, such as upstreamdownstream impacts and environmental changes, becomes large. The interactions factors are also often nonlinear and thus difficult to predict (Vink and Schot 2002). Traditionally, multi-objective optimization problems are often solved using the weighting method or the e-constraint method (Carlos and Peter 1995;Cohon and Marks 1975;Nemhauser et al 1989).…”

Application of a genetic algorithm to minimize agricultural nitrogen deposition in nature reserves

“…Genetic algorithms have been widely used to solve either single and multi-objective optimization problems (Sarkar and Modak, 2003;Dijk et al, 2002;Rauch and Harremoes, 1999). Recently, GAs have been applied to spatial optimization problems (e.g., Aerts, 2002;Vink and Schot, 2002;Matthews, 2001). The emission-deposition problem described here is also a spatial optimization problem.…”

“…In a comparative study of methods for large-scale environmental optimization problems, Mayer et al (2001) conclude that so-called heuristic methods, e.g., GA and SA (simulated annealing), outperform more classical methods such as non-linear programming. See also Sarkar and Modak (2003), Van Dijk et al (2002), Seppelt and Voinov (2002), Vink and Schot (2002), Brookes (2001) and Rauch and Harremoes (1999) for the application of heuristic methods to non-linear, qualitative and environmental optimization problems.…”

Water allocation under future climate change and socio-economic development : the case of Pearl River Basin

“…This is the first study assessing the performance of different MOEAs before using. Previous studies selected MOEA based on its historical applications for other problems Vink and Schot, 2002;. However, the MOEAs perform differently for different optimization problems.…”