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Efficiency of Evolutionary Algorithms in Water Network Pipe Sizing

机译:水管尺寸计算中进化算法的效率

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摘要

The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking. A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm. The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems.
机译:通过进化算法确定水网络的管道尺寸非常重要,因为它允许选择满足一组设计要求的替代经济解决方案。然而,可用的进化方法很多,并且除了针对给定问题获得最小解决方案之外,目前还缺少用于比较这些方法的性能的方法。本文介绍了一种比较基于效率(E)的算法的方法,并将其应用于四个中型基准网络(河内,纽约隧道,高阳和R-9 Joao Pessoa)的管道尺寸确定问题。 E通过数值确定给定算法的性能,同时还要考虑获得的解决方案的质量和所需的计算工作量。从广泛的可用进化算法中,选择了四种算法来实施该方法:伪遗传算法(PGA),粒子群优化(PSO),和声搜索和改进的改组蛙跳算法(SFLA)。经过超过500,000次仿真,基于每种算法需要操作的特定参数进行了统计分析,最后,对每个网络和算法分析了E。效率度量表明,PGA是解决复杂性最高的问题的最有效算法,而HS是解决复杂性较小的问题的最有效算法。但是,这项工作的主要贡献在于,提出的效率比提供了一种中立的策略来比较优化算法,并且可能在将来为不同类型的优化问题选择最合适的算法时很有用。

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