摘要
针对传统灰狼优化算法易陷入局部最优解和狼群多样性贫乏等问题,提出了一种改进型的灰狼优化算法(ISSAGWO)。该算法通过混沌映射初始化灰狼种群,提高了全局最优值求解过程中的狼群多样性。为避免算法陷入局部最优解,引入了一种改进自适应收敛因子。对4种国际基准函数进行MATLAB仿真实验,实验结果表明,与其他群智能优化算法对比,ISSAGWO在求解精度和稳定性上有明显优势。此外,通过ulysses22标准数据集求解旅行商(Travelling Salesman Problem,TSP)问题,验证了该算法的可行性。
Aiming at the problems of the traditional grey wolf optimizer being easy to fall into the local optimal solution and the poor diversity of wolves,an improved grey wolf optimization algorithm(ISSAGWO) is proposed.The algorithm initializes the gray wolf population through chaotic mapping,which improves the diversity of the wolf population in the process of solving the global optimal value.In order to avoid the algorithm falling into the local optimal solution,an improved adaptive convergence factor is introduced.MATLAB simulation experiments are performed on four international benchmark functions.The experimental results show that compared with other swarm intelligence optimization algorithms,ISSAGWO has obvious advantages in solution accuracy and stability.In addition,the ulysses22 standard data set is used to solve the travelling salesman problem(TSP) problem,which verifies the feasibility of the algorithm.
作者
李新宇
LI Xinyu(Anhui University of Science&Technology,Huainan Anhui 232000,China)
出处
《信息与电脑》
2021年第24期91-94,共4页
Information & Computer
