Abstract
The paper describes the development of a technique for the optimal design of water supply and distribution systems, based on a coupling between evolutionary algorithms and a pressurized hydraulic network solver. The purpose is to show the capabilities of Pareto genetic algorithms (PGAs) in solving multi-objective, constrained optimization problems: in such cases, the optimum is represented not only by one solution, as in single-objective optimization, but by a set of optimal configurations (the Pareto front or frontier), satisfying different levels of compromise among the competing objectives. A Pareto GA should determine the family of such non-dominated solutions, each of which is optimal in the sense that no improvement can be achieved in one criterion without the degradation in at least one of the remaining criteria. This might be of great help to the decision maker in selecting the best trade-off configuration, which will eventually depend on the actual context. An application to a real case is also presented.
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