改进的樽海鞘群算法

针对樽海鞘群算法在对函数优化问题求解上出现的求解精度不高、收敛速度慢的缺点,提出了一种改进的群海鞘群算法.对于领导者引入加权重心取代最优个体位置,防止过早聚集在最优个体附近;对于追随者引入自适应惯性权重平衡算法的全局搜索和局部寻优能力;最后对于个体进行逐维随机差分变异,减少维间干扰,提高了种群的多样性.仿真实验结果表明改进的樽海鞘群算法在均值、标准差和收敛曲线优于标准樽海鞘群算法和其他改进算法,说明改进后的算法提高了寻优性能,有较高的求解精度和较快的收敛速度.

Improved salp swarm algorithm

This paper proposed an improved salp swarm algorithm that aimed to solve the problem of low precision and slow convergence speed in solving function optimization problems. For leaders, this algorithm replaced the optimal individual position with weighted centroid to prevent premature aggregation near the optimal individual. It also introduced adaptive inertia weight to the followers to balance the global search and local optimization capabilities of the algorithm. Furthermore, this algorithm contributed to reducing the inter-dimensional interference and improving the diversity of the population by dimensional random difference mutation. The simulation results illustrate that the improved algorithm is better than the standard tympanum group algorithm and other improved algorithms in terms of mean, standard deviation and convergence curve. The results also show that the improved algorithm improves the performance of optimization, and has higher precision as well as faster convergence speed.