Abstract
This paper presents a harmony search algorithm with opposition-based learning techniques (HS-OBL) to solve power system. To prevent the HS-OBL algorithm from being trapped into the local optimum effectively, an improved algorithm in this paper integrates the opposition-based learning operation with the improvisation process. After that, pitch adjusting rate (PAR) and harmony memory consideration rate (HMCR) are adjusted by a new adjusting strategy that is designed for dynamic adjustment to further improve the performance of algorithm. The HS-OBL is employed to solve 7units and 14units power system, the numerical results show that the HS-OBL has performed much better than harmony search (HS) algorithm and other improved algorithms that have been reported in recent literature. And the data has shown in table 4.
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