给水管网多目标间接和直接优化算法研究与应用

发布时间：2018-05-31 06:56

  本文选题：给水管网 + 多目标优化　； 参考：《山东科技大学》2017年硕士论文

【摘要】：近年来随着城镇化建设的推进,城市规模和人口数量日益增大,对其赖以生存的水资源的需求也越来越多,这对城镇给水管网系统的建设提出了更高的要求。给水管网系统是城镇生存和可持续发展的重要基础设施,其中给水管网的费用占系统总费用的70%-85%左右,其安全可靠性更是城市居民用水的重要保障。给水管网具有一次性投资费用高、社会意义重大等特点,因此针对给水管网的优化设计进行研究具有重要意义。本文的主要研究内容和成果如下:针对传统给水管网的优化设计中,存在的仅以经济性费用为目标的单目标模型、忽视可靠性保障的重要性以及施工方案单一等问题,本文提出以管网建造总费用年折算值最小、可靠性最大为目标函数,建立给水管网的多目标优化数学模型。其中,管网建造总费用包括一次性铺设建造费用、折旧大修费用的年折算值和泵站年运行动力费用等,并考虑利率年费的影响;管网供水的可靠性由节点富余水头和管网恢复力来衡量。针对给水管网的优化设计问题为复杂的多项式非确定性问题,按目标函数的构造方法可分为间接法和直接法。间接法是采用线性加权等方法将其转化为单目标数学模型再进行求解,直接法是直接对多个目标进行求解。但在间接法中采用加权法时常常不能合理地衡量多个目标之间的相互竞争关系,为此本文采用最大最小化法将经济性和可靠性统一到[0,1]的范围值,合理地改进了间接法的目标函数。为了间接法的程序实现,本文提出了一种新的改进遗传算法——育种遗传算法,该算法通过育种算子来加强算法的局部搜索能力和避免陷入局部最优。对于管网优化的直接法,本文采用经典的NSGA-Ⅱ算法来实现,该算法基于非劣排序和精英保留等方法,在求解最优非劣解时能得到分布均匀的Pareto最前沿的解集。本文采用MATLAB编程实现了管网优化的两种计算方法,通过2环管网模型和Hanoi管网模型对其合理性和有效性进行了检验,并将其应用于实际工程管网模型的优化中。结果表明,在小型管网中两种算法都能收敛到Pareto最优解,但在大型管网中育种遗传算法的收敛效果优于NSGA-Ⅱ算法。此外,育种遗传算法得到的最优解数量较少、分布性较差,而NSGA-Ⅱ算法解的分布性更好,因此就解的分布性来说,NSGA-Ⅱ算法要优于育种遗传算法。
[Abstract]:In recent years, with the development of urbanization, the scale of city and the number of population are increasing day by day, and the demand for water resources is increasing, which puts forward higher requirements for the construction of urban water supply network system. Water supply network system is an important infrastructure for the survival and sustainable development of cities and towns, in which the cost of water supply network accounts for about 70-85% of the total cost of the system, and its safety and reliability is also an important guarantee for urban residents to use water. Water supply network has the characteristics of high one-time investment cost and great social significance, so it is of great significance to study the optimal design of water supply network. The main research contents and achievements of this paper are as follows: in the optimization design of traditional water supply network, there are some problems such as single objective model which only aims at economic cost, ignoring the importance of reliability guarantee and single construction scheme, etc. In this paper, a multi-objective optimization mathematical model of water supply network is established by taking the minimum annual conversion value and the maximum reliability as the objective function of the total cost of water supply network. Among them, the total cost of pipe network construction includes the one-time construction cost, the annual conversion value of the depreciation overhaul cost and the annual operating power cost of the pump station, and the influence of the interest rate annual fee is taken into account. The reliability of pipe network water supply is measured by node surplus head and pipe network recovery force. In view of the complex polynomial uncertainty problem in the optimization design of water supply network, the method of constructing objective function can be divided into indirect method and direct method. Indirect method is a linear weighting method to transform it into a single objective mathematical model to be solved, and the direct method is to solve multiple objectives directly. However, when the weighting method is used in the indirect method, it is often impossible to reasonably measure the competitive relationship between multiple objectives. In this paper, the maximum minimization method is used to unify the economy and reliability to the range of [0]. The objective function of indirect method is improved reasonably. In order to realize the program of indirect method, a new improved genetic algorithm, breeding genetic algorithm, is proposed in this paper, which strengthens the local search ability of the algorithm and avoids falling into local optimum by breeding operator. For the direct method of pipe network optimization, the classical NSGA- 鈪，

本文编号：1958725