An Improved Genetic Algorithm for Power Grid
Youchan Zhu, Xueying Guo, Jing Li · 2009
In power grid, we focus on one kind of high performance computing applications, that is, power system computing and Simulation (PCS) applications. PCS applications are always broken down into several sub-tasks depending on each other, which can be represented as a DAG. Genetic algorithm (GA) has been widely used to solve the dependent tasks scheduling. However the conventional GA is too slow to be used in power grid due to its time-consuming iteration. This paper applies an improved genetic algorithm (IGA) to dependent tasks scheduling in power grid. Based on the characteristic of Power Grid scheduling, we design detail the chromosome presentation, fitness function, and evolutionary process. And, this algorithm increases search efficiency with limited number of iteration by improving the evolutionary process while meeting a feasible result. An simulation study was conducted to evaluate the performance of the algorithm. It showed the general suitability of the presented algorithm within power grid.