一种基于膜计算的改进人工鱼群算法

An Improved Artificial Fish Swarm Algorithm Based on Membrane Computing

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.

In order to overcome the shortcomings that the artificial fish swarm algorithm （ AFSA ） traps into local optima easily and has slow computational speed and low convergence accuracy, an improved adaptive AFSA algorithm based on differential factor and mem- brane computing （ MC ） is proposed. The algorithm keeps the diversity of fish swarm and overcomes the problem of trapping into lo- cal optima easily by using the framework of MC and rules. In addition, the algorithm simplifies prey behavior and enhances the per- formance of speed and accuracy by using differential factor to adjust visual, step, delta and attempts. Experimental results show that this algorithm can improve the calculation efficiency and accuracy effectively.