改进遗传算法及其在机组优化组合中的应用  

Improved Genetic Algorithm and Its Application in Unit Commitment Optimization

作　　者：范宏[1] 韦化[1] 

机构地区：[1]广西大学电气工程学院,南宁530004

出　　处：《电力系统及其自动化学报》2004年第4期46-49,63,共5页Proceedings of the CSU-EPSA

摘　　要：提出了一种新的基于浮点数和二进制数统一编码的改进遗传算法。该算法有效利用了浮点数编码FGA( Float-coaded Genetic Algorithm)收敛迅速、不易陷入局部最优解、具有较高收敛精度的优点 ,同时结合二进制编码 BGA( Binary-coaded Genetic Algorithm)模拟机组启停状态的优点 ,解决了机组优化组合的 0 -1混合整数非线性规划问题。该算法已成功地运用到 1 6台机组的优化组合中 。This paper presents a new genetic algorithm for unit commitment based on the float-coded and the binary-coded methods.It effectively combines the Float-coded Genetic Algorithm(FGA)'s perfect advantage and the Binary-coded Genetic Algorithm (BGA)'s similating advantage together.This algorithm is more effective and faster than other algorithms which based on one code style.This algorithm has successfully solved Unit Commitment problem and has gained an excellent result through the experiments.

关 键 词：浮点数编码 二进制编码 改进遗传算法 机组优化组合 

分 类 号：TM76[电气工程—电力系统及自动化]