Gate dam group joint scheduling method and system based on improved elitism strategy genetic algorithm

Abstract

The invention provides a gate dam group joint scheduling method and system based on an improved elitism strategy genetic algorithm. The optimization efficiency and convergence speed in gate dam group multi-target joint scheduling model solving can be effectively improved. The gate dam group joint scheduling method is characterized by comprising the following steps: 1, establishing a model, a, determining an objective function, b, setting constraint conditions, and setting related variables to be non-negative; step 2, acquiring data of each parameter related to the model from historical data and actually measured data of the gate dam group; 3, substituting the data obtained in the step 2, and solving the model by adopting an improved elitism strategy genetic algorithm; and step 4, performing gate dam group joint scheduling according to a solving result of the model in the step 1 in the step 3.

Description

technical field [0001] The invention belongs to the technical field of gate and dam scheduling, and in particular relates to a joint scheduling method and system for gate and dam groups based on an improved elite retention strategy genetic algorithm. [0002] technical background [0003] Water is the material basis for human survival and reproduction. As the most important fresh water resource, rivers not only maintain the stability of the natural ecological environment system, but also have great significance for the improvement of human living environment. However, with the rapid increase of global population and the continuous acceleration of urbanization, the demand for water resources is also increasing day by day. The resulting shortage of water resources, water environmental pollution and water ecological degradation seriously threaten global water security. [0004] The gate and dam are located in the middle and lower reaches of the river, and have the characteristic...

AI Technical Summary

Problems solved by technology

But they still have the following deficiencies: First, the generation of the initial population is relatively random, often with poor diversity and feasibility, reducing computational efficiency and convergence speed; second, due to the constraints of reservoir water balance and other conditions, the cross Many infeasible solutions will also be generated when operating with mutations, and a lot of computing time will be wasted in the subsequent running process

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