基于MA的水电站机组组合优化问题的研究

Study on unit combinatorial optimization with Memetic algorithm for hydropower plants

在日负荷任务下,建立了以耗水量最小为目标的水电站机组组合优化数学模型。设计了文化基因算法(Memetic Algorithm,MA)的工程实现方法,包括编码设计、适应度函数设计等。提出了二进制与浮点数的混和编码以及交叉和变异的双重遗传操作方式,设计了个体合法化的流程,采用模拟退火算法作为局部搜索策略。绘制了算法对问题的求解流程,并编制了基于MATLAB语言的优化计算程序。仿真结果表明:MA具有比GA更优的收敛性能,更有效降低机组切换频率。

This paper develops a mathematic model of unit combinatorial optimization of minimum water consumption for hydropower plants under daily load task,and designs application techniques of Memetic algorithm（MA） to code design,fitness function design,etc.We put forth methods of hybrid binary-real coding and double operation of crossover and mutation and design a process of individual legalization,using simulated annealing（SA） as local search strategy.A MATLAB code was written and tested for the optimization calculation,and a solution process with MA application is given in this paper.The simulation results show that the convergence of MA is faster than genetic algorithm（GA） and that the proposed new method can effectively reduces the switching frequency of units.