基于混合编码的遗传算法在多约束机组组合优化问题中的应用.doc

【摘要】 现今的电力市场需要在火电厂内各机组间合理安排生产计划，以便达到最佳的经济效应。火力发电厂的发电机组组合优化问题作为其中的主要问题，其具有高维数、非凸、离散、非线性、多约束的特点，增加了求解的复杂性。 针对电力系统机组组合优化问题，本文结合算例采用遗传算法，对基于混合编码的多约束机组组合优化问题进行求解。在整个遗传算法操作中，有选择地引用上一代最优个体作为新种群中的个体。在初始化的过程中直接解决所有的约束问题。并在交叉和变异操作完之后对新产生的染色体进行约束处理的方式。这样可以保证算法的快速收敛。不但如此，该算法还是能在交叉和遗传操作作用下进行优化的一个过程。在仿真分析中，本文通过对不同的种群数、不同的遗传进化代数、不同的交叉概率、不同的变异概率以及在初始化过程中强制开启不同数量的机组时的进化结果进行比较。得到了一组在该问题中能得到最优结果的一组参数。其表明了遗传算法具有较强的全局有哪些信誉好的足球投注网站能力，并且不同的参数对于整个算法的结果是有非常大的影响作用，是解决多约束机组组合问题的一种有效地方法。 关键字：电力系统；机组组合；遗传算法；组合优化；混合编码  【abstract】 odays electricity market in the thermal power plant, reasonable arrangements have been needed between the various units of production planning in order to achieve the best possible economic effects. Thermal power generating units of the combinatorial optimization problem is one of the main problems with high dimension, non-convex, discrete, nonlinear, multi-binding characteristics, increasing the complexity of the solution. This paper presented a new Genetic Algorithm by introducing new hybrid code, which is applied in power system unit combinatorial optimization problems. GA is usually used to solve this kind of the multi-constraint combinatorial optimization problem. Under the genetic algorithm process, recommend the optimal generation of individual as the individual in the new population selectively. In the process of initialization, the new chromosomes is generated by dealing with the constraints. In this way, it ensures that algorithm can converge rapidly. Not only that, this algorithm is also a process of optimized by the function of the cross operator and the mutation operator. In the simulations in matlab, this paper discussed and analized the impacts on GA by changing the number of different populations, different genetic evolution of algebra, different crossover probability, mutation probability, and a different number of units which be forced to run in the initialization process. The results obtained from simulation is that a set of parameters which can be got the be