摘要
针对细菌觅食优化算法寻优过程中精度差、易陷入早熟收敛等缺点,提出一种变概率混合细菌觅食优化算法。借鉴粒子群算法的信息共享机制,采用能综合反映细菌自身学习及群体合作的趋化方向,以提高算法的寻优精度和效率;基于群体适应度方差理论引入变概率迁徙策略,帮助细菌快速跳出局部极值,避免了早熟收敛和精英细菌逃逸;采用改进型佳点集方法构造初始种群及迁徙后的新个体,保证了种群多样性和解空间随机性。实验结果表明,本文提出的算法在全局收敛能力及优化精度和速度方面均表现更优。
For the drawbacks such as low optimization precision and sticking to local optimum with the classic bacteria foraging optimization（BFO）,a variable probability and hybrid BFO（VHBFO）algorithm is presented.Emerged from the information sharing mechanism of particle swarm optimization（PSO）,the chemotaxis direction strategy reflecting the bacteria individual cognitive and group cooperation is proposed,so as to improve the precision and searching efficiency of the algorithm.The variable probability of migration operations based on the group fitness variance theory is introduced to avoid the premature convergence and help bacteria quickly jump out of local extremum and avoid elite bacteria escape.The improved good point set population is used to construct the initial population and new individuals after migration,which provides a more uniform and diversified solution space.Experiment results indicate that the algorithm outperforms the classic algorithm both in terms of the solution accuracy and the convergence speed.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2016年第4期960-964,共5页
Systems Engineering and Electronics
基金
国防"十二五"基础科研项目(A1120132007)资助课题
关键词
细菌觅食优化算法
信息共享
群体适应度方差
变概率
改进型佳点集
bacteria foraging optimization（BFO）algorithm
information sharing
group fitness variance
variable probability
improved good point set
