摘要
果蝇优化算法(FOA)是较新的群智能算法,其应用领域越来越广泛。但该算法在求解问题时存在早熟收敛,收敛速度慢和收敛精度低的缺点,为改善算法性能本文提出了分层协调进化的果蝇优化算法(HCFOA)。根据味道浓度值将果蝇种群分层,并结合果蝇个体的进化信息及精英个体对种群位置的影响,进行分层协同进化。HCFOA和FOA分别求解4个测试函数,仿真结果表明,HCFOA算法有效改善了局部搜索能力,提高了收敛精度。
Fruit Fly Optimization Algorithm（FOA） is a relatively new Swarm intelligence algorithm, its ap- plication field is more and more widely. In solving the problem using this algorithm, low convergence precision and easily relapsing into local optimum, there are the faults of Premature convergence, slow convergence speed and low convergence precision. Hierarchical Coevolutionary Fruit Fly Optimization Algorithm （HCFOA） is pro- posed in order to improve the algorithm performance in this paper. Fruit fly population is layered according to smell, and coevolutioned with the evolution information and the effects of elite individual on population loca- tion. The simulation results of four test functions show that HCFOA has the advantages of better local searching ability, and more precise convergence than FOA.
出处
《浙江海洋学院学报（自然科学版）》
CAS
2015年第5期491-496,共6页
Journal of Zhejiang Ocean University(Natural Science Edition)
基金
浙江省科技厅公益技术研究社会发展项目(2015C33082)
关键词
果蝇优化算法
分层协同进化
收敛精度
fruit fly optimization algorithm （FOA）
fierarchical coevolutionary
convergence precision
