摘要
为解决布谷鸟搜索算法存在的后期收敛速度慢,求解精度低以及容易陷入局部最优点等问题,提出了一种改进的布谷鸟搜索算法:CS-EO搜索算法。在该搜索算法中,通过将布谷鸟算法收敛速度快和全局搜索的优点与极值动力学优化算法强大的局部搜索能力进行有机的结合,在保证布谷鸟算法求解速度的前提下,提高了布谷鸟算法的求解精度。函数寻优测试的仿真结果表明改进的布谷鸟搜索算法相较于布谷鸟搜索算法以及粒子群算法都具有更好的寻优性能。最后将此算法应用于微电网的负荷优化调度中,取得了较为令人满意的结果。
In order to solve the shortcomings in cuckoo search algorithm, such as slow convergence, low accuracy and easy to fall into the local optimal solution, an improved cuckoo search algorithm is proposed： CS-EO search algorithm. In this search algorithm, the fast convergence speed and global searching advantage of CS algorithm and the strong local search ability of EO algorithm are combined organically. Under the premise of guaranteeing the solving speed, the accuracy of the solution is improved. The results of optimization test functions show that CS- EO algorithm has better optimization performance compared with CS algorithm and PSO algorithm. The algorithm is applied to the load optimal dispatch of micro grid, and the result is satisfying.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2018年第3期930-936,共7页
Journal of System Simulation
基金
华能集团科技项目(HNKJ15-H16)
中央高校基本科研业务费专项资金(9163116001)
