Draining pipeline network optimizing method based on generalized reverse learning difference algorithm

Abstract

The invention relates to a draining pipeline network optimizing method based on a generalized reverse learning difference algorithm and aims to solve the problems that draining pipeline optimization is complex in calculation and low in calculation precision, and unreasonable and infeasible schemes cannot be excluded thoroughly. The improved self-adaption difference algorithm is utilized to obtain the optimal value of a pipeline manufacturing cost function. The optimal value includes hydraulic parameters such as flow rate, slope and burial depth, satisfying a hydraulic constraint condition, of each pipeline section. A reserve learning method is used for initializing the self-adaption algorithm, the initialized population of the algorithm is processed before variation, probability theory knowledge is utilized to refine initial solution while population number is not increased, population variety is increased, and probability for finding global optimal solution is increased. In addition, few variables are related to the algorithm, variation strategies are selected through self-adaption, and simple algorithm operation, fast convergence and high optimization precision are achieved.

Description

technical field [0001] The invention belongs to the field of drainage pipe network optimization, and relates to a drainage pipe network optimization method based on a generalized reverse learning difference algorithm. The generalized reverse learning method is used to improve the self-adaptive difference algorithm, which is used for the cost optimization design of the drainage pipe network. Background technique [0002] The drainage system usually consists of a drainage network and a sewage treatment plant. The drainage network is a facility for collecting and transporting domestic sewage, industrial wastewater, and rain and snow water, and is responsible for transporting these wastes to sewage plants or other water outlets. As an indispensable part of urban infrastructure construction, the system is mainly composed of drainage pipes, drainage pumping stations, inspection wells and other engineering facilities. In order to meet the material needs of the people and further im...

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Problems solved by technology

[0006] Aiming at the problems of large preprocessing workload and complex solution process in the optimization of drainage pipe network, the present invention proposes a method for optimizing drainage pipe network based on generalized reverse learning difference algorithm

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