猴群空翻机制作用下的自适应人工鱼群算法

Adaptive Articificial Fish Swarm Algorithm Based on Monkey Somersault

针对人工鱼群算法在函数优化中存在陷入局部最优、后期收敛速度过慢及人工鱼群寻优精度低等问题,对动态分组方案的人工鱼群算法进行了研究,提出一种新的自适应人工鱼群算法。该算法利用猴群算法中的空翻行为替代鱼群的聚群和追尾行为,同时引入模糊函数,自适应调整鱼群算法的视野及步长,提高了算法的运行效率,更好地平衡了全局搜索与局部搜索之间的关系。算法在后期避免提前收敛,能够快速跳出局部最优位置,保证了寻优质量。仿真实验表明,该算法明显优于基于动态分组方案的人工鱼群算法,有效提高了寻优精度和寻优质量,避免了人工鱼群的早熟现象。

The Artificial Fish Swarm Algorithm （AFSA） in function optimization problems has some defectives such as falling into local optimum value converging slowly in the later period and lower fish accuracy. This paper proposed a new adaptive artificial fish swarm algorithm on the basis of dynamic dividing plan of adapting artificial fish-swarm algorithm（DTAFSA）. The algorithm uses the somersault behavior in the monkey algorithm to replace the clustering and trailing behavior of the artificial fish swarm algorithm. At the same time, the fuzzy function is used to adjust the field of view and the step size of the fish swarm algorithm, and the operation efficiency of the algorithm is improved to a great extent. Better balance the relationship between global search and local search, so that the algorithm in the late to avoid advance convergence, can quickly jump out of the local optimal position, to ensure the quality of the search. The simulation results show that this algorithm is superior to the artificial fish swarm algorithm based on dynamic dividing plan, at the same time, keeping the accuracy and quality of fish to avoid earlymaturing.