基于随机收敛因子和差分变异的改进灰狼优化算法
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摘要
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。
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 highdimensional complex optimization problems. Inspired by particle swarm optimization( PSO) algorithm,a random adjustment strategy of convergence factor a 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.
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 highdimensional complex optimization problems. Inspired by particle swarm optimization( PSO) algorithm,a random adjustment strategy of convergence factor a 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.
引文
1郭四稳,闫涛,雄伟程.频域滤波模型的粒子群优化算法.科学技术与工程,2017;17(15):96-103Guo Siwen,Yan Tao,Xiong Weicheng.A new PSO algorithm based on frequency domain filtering model.Science Technology and Engineering,2017;17(15):96-103
2 Mirjalili S,Mirjalili S M,Lewis A.Grey wolf optimizer.Advances in Engineering Software,2014;69:46-61
3 Zhang S,Zhou Y,Li Z,et al.Grey wolf optimizer for unmanned combat aerial vehicle path planning.Advances in Engineering Software,2016;99:121-136
4 Long W,Jiao J,Liang X,et al.An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization.Engineering Applications of Artificial Intelligence,2018;68:63-80
5金星,邵珠超,王盛慧.一种基于差分进化和灰狼算法的混合优化算法.科学技术与工程,2017;17(16):266-269Jin Xing,Shao Zhuchao,Wang Shenghui.A hybrid optimization algorithm based on differential evolution and grey wolf optimizer.Science Technology and Engineering,2017;17(16):266-269
6 Heidari A A,Pahlavani P.An efficient modified grey wolf optimizer with Lévy flight for optimization tasks.Applied Soft Computing,2017;60:115-134
7 Kumar V,Kumar D.An astrophsics-inspired grey wolf algorithm for numerical optimization and its appli-cation to engineering design problems.Advances in Engineering Software,2017;112:231-254
8龙文,伍铁斌.协调探索和开发能力的改进灰狼优化算法.控制与决策,2017;32(10):1749-1757Long Wen,Wu Tiebin.Improved grey wolf optimizer algorithm coordinating the ability of exploration and exploitation.Control and Decision,2017;32(10):1749-1757
9王敏,唐明珠.一种新型非线性收敛因子的灰狼优化算法.计算机应用研究,2016;33(12):3648-3653Wang Min,Tang Mingzhu.Novel grey wolf optimization algorithm based on nonlinear convergence factor.Applications Research of Computers,2016;33(12):3648-3653
10魏政磊,赵辉,李牧东,等.控制参数值非线性调整策略的灰狼优化算法.空军工程大学学报(自然科学版),2016;17(3):68-72Wei Zhenglei,Zhao Hui,Li Mudong,et al.A grey wolf optimization algorithm based on nonlinear adjustment strategy of control parameter.Journal of Air Force Engineering University(Natural Science Edition),2016;17(3):68-72
2 Mirjalili S,Mirjalili S M,Lewis A.Grey wolf optimizer.Advances in Engineering Software,2014;69:46-61
3 Zhang S,Zhou Y,Li Z,et al.Grey wolf optimizer for unmanned combat aerial vehicle path planning.Advances in Engineering Software,2016;99:121-136
4 Long W,Jiao J,Liang X,et al.An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization.Engineering Applications of Artificial Intelligence,2018;68:63-80
5金星,邵珠超,王盛慧.一种基于差分进化和灰狼算法的混合优化算法.科学技术与工程,2017;17(16):266-269Jin Xing,Shao Zhuchao,Wang Shenghui.A hybrid optimization algorithm based on differential evolution and grey wolf optimizer.Science Technology and Engineering,2017;17(16):266-269
6 Heidari A A,Pahlavani P.An efficient modified grey wolf optimizer with Lévy flight for optimization tasks.Applied Soft Computing,2017;60:115-134
7 Kumar V,Kumar D.An astrophsics-inspired grey wolf algorithm for numerical optimization and its appli-cation to engineering design problems.Advances in Engineering Software,2017;112:231-254
8龙文,伍铁斌.协调探索和开发能力的改进灰狼优化算法.控制与决策,2017;32(10):1749-1757Long Wen,Wu Tiebin.Improved grey wolf optimizer algorithm coordinating the ability of exploration and exploitation.Control and Decision,2017;32(10):1749-1757
9王敏,唐明珠.一种新型非线性收敛因子的灰狼优化算法.计算机应用研究,2016;33(12):3648-3653Wang Min,Tang Mingzhu.Novel grey wolf optimization algorithm based on nonlinear convergence factor.Applications Research of Computers,2016;33(12):3648-3653
10魏政磊,赵辉,李牧东,等.控制参数值非线性调整策略的灰狼优化算法.空军工程大学学报(自然科学版),2016;17(3):68-72Wei Zhenglei,Zhao Hui,Li Mudong,et al.A grey wolf optimization algorithm based on nonlinear adjustment strategy of control parameter.Journal of Air Force Engineering University(Natural Science Edition),2016;17(3):68-72