The optimal design of sewer networks typically comprises two sub-problems. The first is to determine an optimal layout of the network elements, and the second to optimally design the network components. In this articl...
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The optimal design of sewer networks typically comprises two sub-problems. The first is to determine an optimal layout of the network elements, and the second to optimally design the network components. In this article the focus is on the optimisation of gravity sewer network layouts, which requires simultaneous optimisation of hydraulic design. The layout is optimised using ant colony algorithms with four proposed node and edge-based selection strategies, while a heuristic optimisation algorithm is used for the hydraulic optimisation. The resulting simultaneous optimisation algorithm is shown to perform very well. The selection strategies are shown to be effective, but no clear best strategy is identified, as the performance of the layout algorithms is shown to depend heavily on characteristics of the network under consideration. However, some strategies are shown to perform inconsistently and worse than others on average.
Based in a,generalised recursive tree-building algorithm for populations partitioned into strata a method to obtain simple descriptions of strata is presented. Also strata with a common rule are obtained. Common predi...
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Based in a,generalised recursive tree-building algorithm for populations partitioned into strata a method to obtain simple descriptions of strata is presented. Also strata with a common rule are obtained. Common predictors and criterion variable describe population in all strata or classes of individuals. algorithm considers strata structure in tree-building algorithm and combines in each step maximisation of an information content measure for the criterion variable in a new binary partition of the population and selection of decisional nodes, based in quality of prediction for subsets of strata. Each decisional tree node is composed of a set of strata and a rule for individuals in these strata that will jointly explain the criterion variable. Symbolic data analysis fits the method. Input of the algorithm is composed of classes of individuals. algorithm is extended to individuals described by probabilistic symbolic objects. As output, symbolic objects describe tree, decisional nodes and strata.
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