改进细菌群体趋药性算法在无功优化中的应用

Application of Improved Bacterial Colony Chemotaxis Algorithm in Reactive Power Optimization

为了克服细菌群体趋药性BCC(bacterial colony chemotaxis)算法容易陷入局部最优的缺点,在自适应调整细菌移动速度和感知范围的基础上引入了混沌优化。先将部分重叠或者陷入局部极值点的菌群映射为混沌序列,使其可以重新更优质的遍历分布于空间;然后通过逆映射得到菌群新的适应度值,提高了算法的全局搜索能力,并成功将其应用到电力系统的无功优化中;对Rastrigin函数进行仿真以及IEEE33节点配电系统进行计算分析。结果表明改进的算法具有很好的全局搜索能力,能有效降低系统有功网损,该算法是可行的。

In order to tackle the shortcoming of the bacterial colony chemotaxis algorithm that is liable to fall into local optimum, this paper introduces chaotic optimization on the basis that the perception range and the speed of movement of bacteria are adjusted adaptively. This paper initiallyfirstly maps bacteria which overlap or get into local extreme point onto chaotic sequence, making them in ergodic and superior distribution again, then gets the new fitness value through the inverse mapping which improves the global search capability, and applies the improved algorithm to reactive power optimization successfully. Simulation results of Rastrigin function and analysis of IEEE33 bus system showindicate that the improved algorithm is goodbetter in global search capability and can effectively reduce the network loss, so it is feasible.