基于随机收敛因子和差分变异的改进灰狼优化算法

针对基本灰狼优化算法在求解高维复杂优化问题时存在解精度低和易陷入局部最优的缺点,提出一种改进的灰狼优化算法。受粒子群优化算法的启发,设计一种收敛因子a随机动态调整策略以协调算法的全局勘探和局部开采能力;为了增强种群多样性和降低算法陷入局部最优的概率,受差分进化算法的启发,构建一种随机差分变异策略产生新个体。选取6个标准测试函数进行仿真实验。结果表明:在相同的适应度函数评价次数条件下,此算法在求解精度和收敛速度上均优于其他算法。

Improved Grey Wolf Optimizer based on Stochastic Convergence Factor and Differential Mutation

In order to overcome these disadvantages that the basic grey wolf optimizer (GWO) algorithm is easy to fall into local optimum and has low precision, an improved grey wolf optimizer (IGWO) was used to solve high-dimensional complex optimization problems. Inspired by particle swarm optimization (PSO) algorithm, a random adjustment strategy of convergence factor was designed to coordinate between global exploration and local exploitation. To enhance the diversity of population and avoid falling into the optimal solution, inspired by differential evolution (DE) algorithm, a stochastic differential mutation strategy was constructed to generate new individual. 6 benchmark test functions were selected to verify the effectiveness of IGWO. The results show that the proposed IGWO has significantly improved the accuracy and convergence speed.