一种改进的灰狼优化算法

灰狼优化算法是最近提出的一种较有竞争力的优化技术.然而，它的位置更新方程存在开发能力强而探索能力弱的缺点.受差分进化和粒子群优化算法的启发，构建一个修改的个体位置更新方程以增强算法的探索能力；受粒子群优化算法的启发，提出一种控制参数a随机动态调整策略.此外，为了提高算法的全局收敛速度，用混沌初始化方法产生初始种群.采用18个高维测试函数进行仿真实验，结果表明：对于绝大多数情形，在相同最大适应度函数评价次数下，本文算法的性能明显优于标准灰狼优化算法.

An Improved Grey Wolf Optimization Algorithm

Grey wolf optimization (GWO) algorithm is a relatively novel optimization technique which has been shown to be competitive to other population-based algorithms.However,there is still an insufficiency in canonical GWO regarding its position update equation,which is good at exploitation but poor at exploration.Inspired by differential evolution and particle swarm optimization,the personal best information and the random selected individual from population are used to construct a modified position update equation for enhancing the exploration.Inspired by particle swarm optimization,a random adjustment strategy of control parameterais proposed.In addition,to enhance the global convergence,when producing the initial population,the chaos method is employed.Simulation experiments were conducted on the 18 high-dimensional conventional test functions.The simulation results show that the proposed algorithm provides better performance than basic GWO algorithms in the same or less number of maximum fitness function evaluation in most cases.