Multi-objective quantum Shuffled Frog Leaping Algorithm (SFLA) based water resource optimization and diversion method

Abstract

The invention discloses a multi-objective quantum Shuffled Frog Leaping Algorithm (SFLA) based water resource optimization and diversion method, comprising the following steps: firstly obtaining basic information data on water resources; establishing multi-objective optimization and diversion models for water resources; executing the shuffled Frog Leaping Algorithm (SFLA) to find the best solutions to Pareto of the multi-objective optimization and diversion of water resources; and finally according to a multi-objective decision theory, choosing the best theory to divert water resources by combining objective weights and subjective weights. According to the invention, optimization is achieved through an overall choosing process. Calculation efficiency is increased so as to meet the requirements for best multi-objective diversion programs in a water resource system.

Description

technical field [0001] The invention belongs to the technical field of water resource scheduling in the field of water conservancy and hydropower, in particular to a water resource optimal scheduling method based on a multi-objective quantum hybrid leapfrog algorithm. Background technique [0002] The optimal dispatch of water resources is a multi-constraint and multi-stage decision-making dynamic and optimal control problem for complex nonlinear systems, especially for multi-objective comprehensive utilization projects such as flood control, power generation, irrigation, water supply, shipping, and sand discharge. Optimize the complexity of the solution. The research on water resource optimal dispatching began in the 1940s with the reservoir optimal dispatching problem proposed by Masse. In the mid-1950s, system engineering technology was widely used in water resource optimal dispatching. In recent years, with the increasing improvement of mathematical programming theory a...

AI Technical Summary

Problems solved by technology

Similar to other intelligent optimization algorithms, the basic SFLA also has problems such as the algorithm’s optimization ability depends on parameter settings, it is easy to fall into a local optimal solution in the later stage, and the convergence speed is slow. In addition, in the initialization stage of SFLA, the distribution of the initial population will affect the entire population. Algorithm Convergence Performance

[0005] In recent years, many scholars at home and abroad have introduced the concept of quantum into SFLA, and proposed the Quantum Mixed Frog Leaping Algorithm (QSFLA). However, it has not been applied to the field of water resources optimization and dispatching, and the multi-objective quantum hybrid leapfrog algorithm proposed at this stage has poor initial population distribution, easy to fall into local optimal solution, slow convergence speed, etc. defect

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