摘要
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.
出处
《小型微型计算机系统》
CSCD
北大核心
2014年第5期1142-1146,共5页
Journal of Chinese Computer Systems
基金
国家自然科学基金计划项目(61203135)资助
教育部博士点基金项目(20120191110047)资助
重庆市自然科学基金重点项目(2012JJB40002)资助
重庆市科委工程中心研究计项目(2011pt-gc30005)资助
关键词
人工鱼群算法
膜计算
差异因子
自适应
artificial fish swarm algorithm
membrane computing
differential factor
self adaption