用于机组优化组合的改进单亲遗传算法

AN IMPROVED PARTHENO-GENETIC ALGORITHM FOR UNIT OPTIMAL COMMITMENT

为了有效地解决火电厂机组优化组合问题 ,作者提出了一种改进的单亲遗传算法。该算法使用实数编码 ,不使用在两条染色体之间操作的交叉算子 ,所有遗传操作全部在一条染色体上进行 ,简化了遗传操作过程 ,提高了计算效率 ,且不要求初始群体中的个体具有多样性 ,也不存在“早熟”收敛现象。与传统的机组优化组合方法相比 ,该方法能方便地处理机组优化组合问题的复杂约束条件。

To effectively solve the unit optimal commitment of power system,an improved partheno genetic algorithm (IPGA) is presented. In IPGA the decimal number strings are used and the crossover operators operating between two chromosomes are repealed. It means that all of the genetic operations occur only in one chromosome, therefore, the genetic operation of IPGA is simple and more efficient, the individual in its initial population need not the property of variety and there is no 'immature convergence'. Comparing with traditional unit optimal commitment method,this method can manage the complicated constraints of unit optimal commitment expediently. The results of calculating examples show that IPGA is effective.