Abstract: 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.

Key words: Artificial Fish Swarm Algorithm (AFSA), dynamic weighting factor, niche, parallel algorithm, MPI OpenMP

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。

关键词: 人工鱼群算法, 动态权衡因子, 小生境, 并行算法, MPI OpenMP