摘要
针对人工鱼群算法(AFSA)在函数优化问题中易陷入局部极值和求解精度较低的缺点,提出了一种在基本人工鱼群算法中引入水流作用机制的改进方案。通过水流作用机制中的持续性水流和周期性水流对鱼群施加的有益影响来改进原有算法。持续性水流影响鱼群的体力变化从而控制视野和步长参数的自适应调整以提高求解精度;周期性水流冲击鱼群并改变部分鱼的位置,从而保持鱼群的种群多样性以利于全局收敛。仿真实验结果表明:本文的改进算法具有更高的求解精度和更好的全局搜索性能,并验证了算法的有效性。
In this paper, the water flow mechanism is introduced to improve the performance of artificial fish swarm algorithm （AFSA）. Water flow mechanism simulates the natural phenomenon of running water flow in rivers, including persistent water flow and periodic water flow. Persistent water flow affects fish＇s physical power continuously, which is utilized to adjust the visual and step parameters self-adaptively. Periodic water flow rushes fish swarm effectively and changes the position of some fishes. Therefore, water flow mechanism can improve the search precision and maintain the diversity of population. The experiment results via benchmark function illustrate the effectiveness of the improved algorithm.
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2015年第3期364-370,共7页
Journal of East China University of Science and Technology
基金
国家自然科学基金(61073107)
关键词
人工鱼群算法
水流作用机制
函数优化
artificial fish swarm algorithm
water flow mechanism
function optimization
