结合精英初始化和K近邻的蛇优化算法

Snake optimization algorithm combining elite initialisation and K-nearest neighbors

蛇优化算法(SO)是一种受自然界中蛇生存行为启发产生的元启发式优化算法。原始蛇优化算法存在收敛速度慢、易陷入局部最优的问题,因此提出了一种结合精英初始化和K近邻的改进蛇优化算法(elite initia-lization and K-nearest neighbors improved snake optimizer,EKISO)。首先,为了提高初始种群质量,在种群初始化阶段提出精英初始化的方法,根据种群精英个体产生优质初始种群个体;其次,通过振荡因子优化螺旋觅食策略扩大全局勘探阶段的搜索范围、提高算法的局部逃逸能力;最后,在局部开发阶段提出K近邻思想的位置更新方法,增强种群个体之间的信息交互能力,从而加快收敛速度、提高收敛精度。利用14个经典测试函数和4个CEC2017测试函数将该方法与其他7种优化算法进行对比,证明EKISO收敛速度更快、精度更高且不易陷入局部最优。为了进一步验证EKISO的实用性与可行性,将EKISO应用于压力容器设计问题中,通过实验对比分析可知,EKISO在处理实际优化问题上具有一定的优越性。

The survival behavior of snakes in nature generates the snake optimization(SO),a meta-heuristic optimization algorithm.But the original snake optimization algorithm suffers from slow convergence and easy to fall into the local optimum,so this paper proposed an improved snake optimization algorithm that combined elite initialization and K-nearest neighbors improved snake optimizer(EKISO).Firstly,in order to improve the quality of the initial population,it proposed elite initialization during the population initialization stage,which generated high-quality initial population individuals based on elite individuals of the population.Secondly,optimizing the spiral foraging strategy with a shock factor expanded the search range during the global exploration stage and enhanced the algorithm s local escape capability.Finally,in the local exploitation stage,it proposed a position update method based on the K-nearest neighbor concept,enhancing the information interaction capability among population individuals,thus accelerating convergence speed and improving convergence accuracy.This method compared with seven other optimization algorithms using 14 classical test functions and 4 CEC2017 test functions,which proves that EKISO converges faster,has higher accuracy and is not easy to fall into local optimum.To further validate the practicality and feasibility of EKISO,this paper applied it to pressure vessel design problems.Experimental comparative analysis reveals that EKISO possesses certain advantages in dealing with real optimization problems.