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首页> 外文期刊>Advanced Science Letters >Parallel Particle Swarm Optimization for Determining Pressure on Water Distribution Systems in R
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Parallel Particle Swarm Optimization for Determining Pressure on Water Distribution Systems in R

机译：并行粒子群优化用于确定R的水分配系统压力

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Particle Swarm Optimization (PSO), which is one method in Swarm Intelligence based on meta-heuristics algorithms and inspired by a flock of bird movements, has been widely used for optimization on many fields since it has been providing good performances. Therefore, in this researchwe utilized it for determining pressure distribution on water pipeline networks. It can be done by finding roots of non-linear equations containing the Hazem William formula representing the fluid dynamic model of water pipelines. Because the finding roots is not an easy task on non-linearequations, we transform it into an optimization problem. In this paper, we solved the optimization by using PSO. Moreover, to reduce computational time, we proposed two models of Parallel Particle Swarm Optimization (PPSO, i.e., Fully PPSO and Partly PPSO) by utilizing R packages in parallelcomputing, called “foreach” and “doParallel.” Some experiments and their analysis are conducted to validate the model and implementation. The results shows that both models of PPSO can be used to improve performance effectively.
机译：粒子群优化（PSO）是基于Meta-heuristics算法的群体智能的一种方法，并受到一群鸟儿运动的启发，已广泛用于许多领域的优化，因为它已经提供了良好的性能。因此，在该研究中，利用它来确定水管网络网络上的压力分布。它可以通过找到包含表示水管道流体动力学模型的Hazem William公式的非线性方程的根来完成。因为查找根源不是非线性等级的简单任务，所以我们将其转换为优化问题。在本文中，我们通过使用PSO解决了优化。此外，为了减少计算时间，我们通过在并行计算中利用R包装，称为“Foreach”和“多达”，提出了两种并联粒子群优化（PPSO，即，完全PPSO和部分PPSO）的两种平行粒子群优化（PPSO，即，完全PPSO）。进行了一些实验及其分析以验证模型和实施。结果表明，两种型号的PPSO可用于有效地提高性能。

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