摘要
人工鱼群算法是一种基于动物行为的群体智能优化算法。该文提出一种改进的人工鱼群算法,在觅食行为中让人工鱼直接移动到较优位置,以加快算法的搜索速度,动态调整人工鱼的视野和步长,使其在算法运行初期保持最大值,并逐渐由大变小。该算法较好地平衡了全局搜索能力和局部搜索能力,提高了算法运行效率和精度。仿真结果表明,改进的人工鱼群算法收敛性能比原有算法提高了1倍以上。
The artificial fish swarm algorithm is a swarm intelligence optimization algorithm based on the animal behavior. An improved artificial fish swarm algorithm is presented. This algorithm directly moves artificial fishes to the superior position while searching food so that increasing the algorithm＇s searching speed. It dynamically adjusts the vision and step of artificial fish, makes the vision and step maintain maximum during the initial period of running, and then makes them smaller gradually. This algorithm can keep the balance between global and local search ability, and enhance the running efficiency and precision of the algorithm. The simulation results show that the improved artificial fish swarm algorithm＇s convergence performance is more than twice of the former algorithm.
出处
《计算机工程》
CAS
CSCD
北大核心
2008年第19期192-194,共3页
Computer Engineering
基金
甘肃省教育厅科研基金资助项目(0602-12)
关键词
人工鱼群算法
群体智能
优化
artificial fish swarm algorithm
swarm intelligence
optimization
