基于多核机群的人工鱼群并行算法  

Artificial fish swarm parallel algorithm based on multi-core cluster

作　　者：李双[1] 李文敬[1] 孙环龙[1] 林中明[1] 

机构地区：[1]广西师范学院计算机与信息工程学院,南宁530023

出　　处：《计算机应用》2013年第12期3380-3384,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61163012);广西自然科学基金资助项目(2012GXNSFAA053218);广西高校科学技术研究项目(2013YB147)

摘　　要：针对人工鱼群算法在复杂多峰函数优化问题上寻优精度低、后期搜索能力减弱且运行时间长等问题,提出一种基于多核机群的人工鱼群并行算法(PDN-AFS)。首先对人工鱼群算法的优势与不足进行分析,采用动态权衡因子策略并适时引入小生境机制,提出一种新的人工鱼群(DN-AFS)算法;然后根据多核机群的并行编程模型(MPI+OpenMP),对DN-AFS算法进行并行设计与分析,提出基于多核机群的人工鱼群并行算法;最后在多核机群环境下进行仿真实验。实验结果表明:该算法有效地提高了复杂多峰函数优化问题的收敛速度和寻优性能,并获得了较高的加速比。Concerning the problems of low accuracy, limitations of stagnation and slow convergence speed in the later evolution process of Artificial Fish Swarm Algorithm （AFSA）, a Parallel Dynamic weigh Niches Artificial Fish Swarm （ PDN- AFS} algorithm based on multi-core cluster was proposed. Firstly, the advantages and disadvantages of AFSA were analyzed, and dynamic weighting factor strategy and niche mechanism were adopted, hence a new Dynamic weigh Niches Artificial Fish Swarm （DN-AFS） algorithm was put forward. Then parallel design and analysis of DN-AFS algorithm based on parallel programming model （ MPI ＋ OpenMP） were introduced. Finally, the simulation experiments on multi-core cluster environment were given. The experimental results show that PDN-AFS can effectively improve the convergence speed and optimization performance of the complex multimodal function optimization problem, and achieve high speed ratio.

关 键 词：人工鱼群算法 动态权衡因子 小生境 并行算法 MPI+OPENMP 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构] TP18[自动化与计算机技术—计算机科学与技术]