融合多策略改进的灰狼优化算法

Grey Wolf Optimization Algorithm with Multiple Strategy Improvements

求解复杂优化问题时,灰狼优化算法存在收敛速度慢、容易陷入局部极值的缺点。针对此问题,提出了一种融合多策略改进的灰狼优化算法。首先采用混沌序列产生在解空间均匀分布的初始种群;然后结合精英反向学习机制进行最优解的搜索,引入收敛停滞监测策略,提升算法整体抗停滞能力,保持种群多样性;最后提出一种收敛因子非线性动态调整策略,提高算法的全局收敛速度和稳定性。对10个经典高维测试函数进行仿真实验,结果表明,改进算法能有效摆脱局部极值点,其全局优化性能优于标准灰狼优化算法。

When solving complex optimization problems,the grey wolf optimization algorithm has the disadvantages of slow convergence speed and easy falling into local extremes.To address these issues,a grey wolf optimization algorithm that integrated multiple strategy improvements was proposed.Firstly,a chaotic sequence was used to generate an initial population that was uniformly distributed in the solution space.Then,combined with the elite reverse learning mechanism,the optimal solution was searched,and a convergence stagnation monitoring strategy was introduced to improve the overall antistagnation ability of the algorithm and maintain population diversity.Finally,a non-linear dynamic adjustment strategy for convergence factors was proposed to improve the global convergence speed and stability of the algorithm,and simulation experiments were conducted on 10 classic high-dimensional test functions.The experimental results show that the improved algorithm can effectively eliminate local extreme points,and its global optimization performance is better than the standard grey wolf optimization algorithm.