An issue regarding near-optimal solutions identified by evolutionary algorithms (EAs) is that their absolute deviations from the global optima are often unknown, and hence an EA's performance in handling real-world problems remains unclear. To this end, this paper investigates how close optimal solutions from simple EAs can approach the global optimal for water distribution system (WDS) design problems through an experiment with the number of decision variables ranging from 21 to 3,400. Three simple EAs are considered: the standard differential evolution, the standard genetic algorithm and the creeping genetic algorithm (CGA). The CGA consistently identifies optimal solutions with deviations lower than 50% to the global optimal, even for the WDS with 3,400 decision variables, but the performance of the other two EAs is heavily case study dependent. Results obtained build knowledge regarding these simple EAs’ ability in handling WDS design problems with different sizes. We must acknowledge that these results are conditioned on the WDSs and the parameterization strategies used, and future studies should focus on generalizing the findings obtained in this paper.

关于由进化算法（EA）识别的近乎最佳解决方案的问题是，它们与全局最优值的绝对偏差通常是未知的，因此EA在处理实际问题时的性能仍然不清楚。为此，本文通过决策变量数量在21到3,400之间的实验，研究了简单EA的最佳最优解决方案如何解决水分配系统（WDS）设计问题的全局最优问题。考虑了三个简单的EA：标准差分进化，标准遗传算法和爬行遗传算法（CGA）。即使对于具有3400个决策变量的WDS，CGA始终可以确定偏差小于全局最优值50％的最优解决方案，但是其他两个EA的性能在很大程度上取决于案例研究。获得的结果积累了有关这些简单EA处理不同大小的WDS设计问题的能力的知识。我们必须承认，这些结果取决于WDS和所使用的参数化策略，并且未来的研究应集中于概括本文中获得的发现。