摘要
人工鱼群算法是一种群智能全局随机优化算法,算法在优化前期大约100多次迭代时有较快的收敛速度,但后期算法陷入局部最优,效率不佳.针对这一不足,在人工鱼群算法的基础上,每迭代100次就调节一次视野和步长,加强聚群算子和追尾算子,提高鱼群之间个体交互行为,使鱼群跳出局部最优,继续向更高精度收敛.数值试验结果表明,所得改进人工鱼群算法不仅运算量减少,而且收敛速度和收敛精度都有所提高.
Artificial fish swarm algorithm is a swarm intelligence global stochastic optimization algorithm. The algorithm has a faster convergence speed in the first 100 iterations. However, the latter algorithm falls into local optimum and the efficiency is poor. In order to solve this problem, based on the artificial fish swarm algorithm, each iteration for 100 times has to adjust the vision and step once, strengthen the cluster operator and the collision operator, improve the interaction between individual fish, help the fish swarm to jump out of local optimum, and continue to converge to a higher precision. Numerical results show that the improved artificial fish swarm algorithm not only reduces the computational complexity, but also improves the convergence speed and convergence accuracy.
出处
《北京建筑大学学报》
2017年第2期47-53,共7页
Journal of Beijing University of Civil Engineering and Architecture
基金
国家自然科学基金项目(61463009)
北京自然科学基金项目(4122022)
中央支持地方科研创新团队项目(PXM2013_014210_000173)
关键词
人工鱼群算法
群体智能
适应度函数
数值试验
artificial fish swarm algorithm
group intelligence
fitness function
numerical experiment
