Doa-based pumping station-lid joint multi-objective optimization method

A DOA (Direction of Arrival)-based pumping station-LID (Low Impact Development) joint multi-objective optimization method, wherein the method is stored in a non-transitory readable storage medium, the method comprising:

• Step 1:
• collecting and sorting basic data of a research region, wherein in order to acquire related basic data of the research region, it is necessary to determine the proportion of area of different types of land and the layout of LID measures, and determine unit investment costs of various drainage facilities by consulting relevant information and combining with the local actual conditions to calculate runoff coefficients of various types of land using one or more processors;
• meanwhile, planning should be carried out in accordance with the requirements of sponge city construction and flood control design standards;
• Step 2:
• fitting drainage parameter relationship expressions using the one or more processors:
• based on the basic data acquired in step 1, fitting expressions of relationships between installed flow capacities of a drainage pumping station and water surface rates, sunken green belt rates and permeable pavement rates by adopting a multivariable linear regression method using the one or more processors;
• Step 3:
• establishing an objective function and constraint conditions using the one or more processors:
• constructing a function by taking the minimum total cost of a drainage and storage project and the minimum runoff coefficient as objectives, and determining constraint conditions of all parameters based on the related data and fitting relationships obtained in step 1 and step 2;
• Step 4:
• determining optimal solutions of a single objective function using the one or more processors:
• based on the objective function and constraint conditions established in step 3, iteratively calculating variable values and optimal function values that minimize the total cost of the objective function drainage and storage project and minimize the runoff coefficient by using a fmincon function;
• Step 5:
• performing multi-objective function optimization calculation using the one or more processors:
• based on the multi-objective function and constraint conditions established in step 3 and the optimal solutions of the single objective function in step 4, iteratively calculating the multi-objective functions by using a Dingo Optimization Algorithm (DOA) to obtain an optimal solution set meeting the conditions;
• and
• Step 6:
• selecting an optimal solution using the one or more processors:
• based on the principle of minimizing the present value of the total cost of the drainage and storage project, selecting an optimal configuration solution for the installed flow capacity of the drainage pumping station, water surface rate, sunken green belt rate and permeable pavement rate from the optimal solution set obtained in step 5;
• controlling the drainage and storage project based on the optimal configuration solution for the installed flow capacity of the drainage pumping station, water surface rate, sunken green belt rate and permeable pavement rate.