改进人工蜂群算法及在风电场群调度中的应用  

Improved artificial bee swarm algorithm and its application in optimal operation of wind-power generators

作　　者：赵志[1] 黄文杰[1] 

机构地区：[1]华北电力大学经济与管理学院,北京102206

出　　处：《中南大学学报（自然科学版）》2011年第10期3101-3104,共4页Journal of Central South University:Science and Technology

基　　金：国家自然科学基金资助项目(70971039)

摘　　要：针对遗传算法的"停滞时间长"以及粒子群算法容易陷入"局部极值"问题,引入一种EA和SWARM交叉算法即蜂群遗传算法(BSGA)。在蜂群算法局域搜索部分,应用遍历性较强的混沌搜索算法进行局域优化,以最大限度地避免最优解被排除的问题。将改进后的算法(BSGA-CAO)应用于我国内蒙古中西部地区风电场群的调度优化问题,与BSGA算法、粒子群算法以及遗传算法相比较,运行时间分别缩短69,23和40 s,总发电量实验结果改进率分别为8.49%,5.29%和3.36%。To solve the long-time stopping problem of the genetic algorithm（GA） and the local extreme problem of the particle swarm optimization（PSO）,a new algorithm which hybridized the artificial bee swarm with the genetic algorithm（BSGA） was introduced.The chaotic searching algorithm was applied with the good ergodicity performance in the local optimization sector to avoid the problem of the best solution elimination.Then,the improved algorithm（BSGA-CAO） was applied in the optimal operation of wind-power generators in the center-west inner mongolia.The results show that the improved algorithm gets the better optimal total power generation by 8.49%,5.29%,3.36%,respectively,the time cost reduces 69,23 and 40 s,respectively compared with those of BSGA,PSO and GA.

关 键 词：人工蜂群 遗传算法 混沌搜索 风电场群 调度优化 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]