基于拉丁超立方体的改进白骨顶鸡算法

Improved COOT algorithm based on Latin Hypercube

针对白骨顶鸡算法求解工程问题时收敛速度慢,易陷入局部最优等不足,提出一种基于拉丁超立方体的改进白骨顶鸡算法。使用拉丁超立方体抽样增强初始种群的均匀性和多样性;引入非线性决策因子和自适应动态边界机制,提高算法全局搜索和局部开发能力;利用柯西变异对最优解进行扰动,帮助算法跳出局部最优。在16个基准函数、高维函数和工程问题进行仿真,其结果验证,该算法收敛速度和寻优精度良好,在工程问题上具有可行性和有效性。

Aiming at the shortcomings of the COOT algorithm that it has low convergence speed and is easy to fall into local optimization when solving engineering problems,an improved COOT algorithm based on Latin Hypercube was proposed.Latin Hypercube sampling was used to enhance the uniformity and diversity of the initial population.Nonlinear decision factors and adaptive dynamic boundary mechanisms were introduced to improve global search and local development capabilities.Cauchy variation was used to perturb the optimal solution to help the algorithm jump out of the local optimum.Through experimental simulation of 16 benchmark functions,high-dimensional functions and engineering problems,the simulation results show that the algorithm convergence speed and optimization accuracy are good,and it is feasible and effective in engineering problems.