Having systematic simulation and optimization models with high computational accuracy is one of the most important problems in developing decision support systems. In the present research, a specific methodology was proposed for decentralized calibration of complex water resources system models by using the structural capabilities of the melody search algorithm. This methodology was implemented in the framework of a self-adaptive simulation–optimization model that helps fine-tune complex water resources models by introducing a new definition of the way sub-memories are related to each. The introduced structure aims to achieve the highest possible level of consistency, which is estimated by using different criteria, between model results and observed data at several control points of surface flows. The introduced strategy was put to the test in developing a water resources model for the Great Karun Watershed, Iran, and was found to produce accurate results compared to some other well-known optimization algorithms such as GA, HS, PSO, SGHS, EMPSO, and SaMeS. In an attempt to determine the effect of calibration on water resources system modeling, 16 calibration models of different dimensions are developed and their computational costs are compared in terms of their computation time and effects on the accuracy of the results.

具有高计算精度的系统仿真和优化模型是开发决策支持系统最重要的问题之一。在本研究中，提出了一种利用旋律搜索算法的结构能力对复杂水资源系统模型进行分散校准的具体方法。这种方法是在自适应模拟优化模型的框架内实施的，该模型通过引入子存储器与每个模型相关的新定义来帮助微调复杂的水资源模型。引入的结构旨在实现模型结果与地表流的几个控制点的观测数据之间的最高可能的一致性水平，这是通过使用不同的标准来估计的。引入的策略在为伊朗大卡伦流域开发水资源模型时进行了测试，发现与其他一些著名的优化算法（如 GA、HS、PSO、SGHS、EMPSO、和萨梅斯。为了确定校准对水资源系统建模的影响，开发了 16 个不同维度的校准模型，并在计算时间和对结果准确性的影响方面比较了它们的计算成本。