An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design
An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design
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水资源问题的进化算法算子与问题特征之间的接口分析。
DOI：
10.1016/j.envsoft.2014.12.023
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发表时间：
2015
影响因子：
4.9
通讯作者：
McClymont K
中科院分区：
文献类型：
--
作者：
McClymont K
近二十年来，进化算法被广泛应用于水资源问题的求解，并取得了很大的成功。然而，最近的研究hyperproblemistics提出了开发优化器，适应正在解决的问题的特点的可能性。为了选择合适的运营商，这样的优化，有必要首先了解运营商和问题之间的相互作用。本文探讨了EA运营商行为的概念，在真实的世界的应用，通过实证研究的性能，供水管网（WDN）作为一个案例研究。创建人工网络来体现特定的WDN功能，然后使用这些功能来评估网络功能对运营商性能的影响。该方法提取封装在WDN的自然特征中的问题的关键属性，例如拓扑和资产，可以在其上测试不同的EA运营商。该方法使用专门设计的小型样本网络进行演示，以便隔离各个功能。一组运营商进行测试，这些人工网络和他们的行为特征。这个过程提供了一个系统的和定量的方法来建立有关算法的适用性，以优化某些类型的问题的详细信息。然后在真实世界的启发网络上重复实验，结果与预期结果相符。
Evolutionary Algorithms (EAs) have been widely employed to solve water resources problems for nearly two decades with much success. However, recent research in hyperheuristics has raised the possibility of developing optimisers that adapt to the characteristics of the problem being solved. In order to select appropriate operators for such optimisers it is necessary to first understand the interaction between operator and problem. This paper explores the concept of EA operator behaviour in real world applications through the empirical study of performance using water distribution networks (WDN) as a case study. Artificial networks are created to embody specific WDN features which are then used to evaluate the impact of network features on operator performance. The method extracts key attributes of the problem which are encapsulated in the natural features of a WDN, such as topologies and assets, on which different EA operators can be tested. The method is demonstrated using small exemplar networks designed specifically so that they isolate individual features. A set of operators are tested on these artificial networks and their behaviour characterised. This process provides a systematic and quantitative approach to establishing detailed information about an algorithm's suitability to optimise certain types of problem. The experiment is then repeated on real-world inspired networks and the results are shown to fit with the expected results.
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