论文部分内容阅读
针对人工鱼群算法运算速度慢,收敛精度低,易陷入局部最优等问题,基于膜计算思想,通过引入差异因子,提出一种改进的自适应人工鱼群算法.算法采用膜计算的层次结构和交流规则,以保持鱼群的多样性,并克服其易陷入局部最优的缺陷.此外通过简化觅食行为,并根据种群中不同个体与种群规模的比例定义差异因子,对算法的视距、步长、拥挤度因子、尝试次数等进行自适应调整,改善算法的收敛精度和运算速度.实验证明,本文所提算法能够有效提高计算效率和收敛精度.
Aiming at the problem that the artificial fish swarm algorithm is slow in computation, low in convergence precision and easy to fall into the local optimum, an improved adaptive artificial fish swarm algorithm is proposed by introducing difference factor based on the idea of membrane computing. Exchange rules in order to maintain the diversity of fish stocks and to overcome its easy to fall into the local optimum shortcomings.In addition, by simplifying foraging behavior, and according to the proportion of different individuals in the population and the size of the population to define the difference factor, the algorithm of the line of sight, Step size, congestion degree factor and the number of attempts to improve the convergence accuracy and speed of the algorithm.Experiments show that the proposed algorithm can effectively improve the computational efficiency and convergence accuracy.