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首页> 外文期刊>Journal of Hydroinformatics >Dynamic penalty function as a strategy in solving water resources combinatorial optimization problems with honey-bee mating optimization (HBMO) algorithm
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Dynamic penalty function as a strategy in solving water resources combinatorial optimization problems with honey-bee mating optimization (HBMO) algorithm

机译：动态惩罚函数作为一种利用蜜蜂交配优化（HBMO）算法解决水资源组合优化问题的策略

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Because of the complexity of some optimization problems, evolutionary and meta-heuristic algorithms are sometimes more applicable than the traditional optimization methods. Some difficulties in solving design-operation problems in the field of engineering are due to the multi-modality of the solution region of these problems. Since the design variables usually are specified as discrete variables and other continuous decision variables have to be set according to the range of the discrete ones, the possibility of trapping the final solution into some local optimum increases. In such cases, the capability of both traditional and evolutionary algorithms decreases. Thus, the development of a strategy to overcome this problem is the subject of this paper. For water utilities, one of the greatest potential areas for energy cost-savings is the effective scheduling of daily pump operations. Optimum design operation of pumping stations is a potential problem in this area that performs a wide background of solutions to this problem with different methods. Computation in all methods is driven by an objective function that includes operating and capital costs subject to various performances and hydraulic constraints. This paper achieves the optimal control and operation of an irrigation pumping station system by one of the latest tools used in optimization problems, which is the honey-bees mating optimization (HBMO) algorithm and is tested with a practical design. The HBMO algorithm with dynamic penalty function is presented and compared with two other well-known optimization tools which are the Lagrange multipliers (LM) method and genetic algorithms (GA) as well as with the previous results of the HBMO algorithm with constant penalty function for the same problem. The LM, GA and HBMO approaches simultaneously determine the least total annual cost of the pumping station and its operation. The solution includes the selection of pump type, capacity and the number of units, as well as scheduling the operation of irrigation pumps that results in minimum design and operating cost for a set of water demand curves. In this paper, the HBMO algorithm is applied and the dynamic penalty function is tested to demonstrate the efficiency of this combination simultaneously. The results are very promising and prove the ability of combining the dynamic penalty function with the HBMO algorithm for solving combinatorial design-operation optimization problems. Application of all these models to a real-world project shows not only considerable savings in cost and energy but also highlights the efficiency and capability of the dynamic penalty function in the HBMO algorithm for solving complex problems of this type.
机译：由于某些优化问题的复杂性，进化算法和元启发式算法有时比传统的优化方法更适用。在工程领域中解决设计-操作问题的一些困难是由于这些问题的解决区域的多模式性。由于通常将设计变量指定为离散变量，并且必须根据离散变量的范围设置其他连续决策变量，因此将最终解陷入某些局部最优值的可能性增加。在这种情况下，传统算法和进化算法的能力都会下降。因此，开发克服这一问题的策略是本文的主题。对于自来水公司而言，节能的最大潜力领域之一是有效安排日常泵的运行。泵站的最佳设计运行是该领域的潜在问题，该问题以不同的方法为该问题提供了广泛的解决方案。所有方法的计算均由目标函数驱动，该函数包括受各种性能和液压约束的运营和资本成本。本文通过最优化问题中使用的最新工具之一，即蜜蜂交配优化（HBMO）算法，实现了灌溉泵站系统的最优控制和运行，并通过实际设计进行了测试。提出了具有动态罚函数的HBMO算法，并将其与其他两个著名的优化工具（拉格朗日乘数（LM）方法和遗传算法（GA））以及具有恒定罚函数的HBMO算法的先前结果进行了比较。同样的问题。 LM，GA和HBMO方法同时确定泵站及其运营的年度总成本最低。该解决方案包括选择泵的类型，容量和单元数量，以及安排灌溉泵的运行时间，从而使一组需水曲线的设计和运行成本降至最低。在本文中，应用了HBMO算法并测试了动态罚函数以证明这种组合的有效性。结果是非常有希望的，并证明了将动态惩罚函数与HBMO算法结合起来解决组合设计-操作优化问题的能力。所有这些模型在实际项目中的应用不仅显示出可观的成本和能源节省，而且还强调了HBMO算法中动态罚函数解决此类复杂问题的效率和能力。

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