一种改进的灰狼优化算法
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摘要
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.
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.
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.
引文
[1]MIRJALILI S,MIRJALILI S M,LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Softw are,2014,69(3):46-61.
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[3]GUHA D,ROY P K,BANERJEE S. Load frequency control of interconnected pow er system using grey w olf optimization[J]. Sw arm and Evolutionary Computation,2016,27:97-115.
[4]姚鹏,王宏伦.基于改进流体扰动算法与灰狼优化的无人机三维航路规划[J].控制与决策,2016,31(4):701-708.YAO Peng,WANG Hong-lun. Three-dimensional path planning for UAV based on improved interfered fluid dynamical system and grey w olf optimizer[J]. Control and Decision,2016,31(4):701-708.(in Chinese)
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[6]GUPTA E,SAXENA A. Robust generation control strategy based on grey w olf optimizer[J]. Journal of Electrical Systems,2015,11(2):174-188.
[7]KOMAKI G,KAYVANFAR V. Grey wolf optimizer algorithm for the tw o-stage assembly flow shop scheduling problem w ith release time[J]. Journal of Computational Science,2015,8(3):109-120.
[8]ZHU A,XU C,LI Z,et al. Hybridizing grey wolf optimization w ith differential evolution for global optimization and test scheduling for 3D stacked SoC[J]. Journal of Systems Engineering and Electronics,2015,26(2):317-328.
[9]龙文,伍铁斌.协调探索和开发能力的改进灰狼优化算法[J].控制与决策,2017,32(10):1749-1757.LONG Wen,WU Tiebin. Improved grey w olf optimization algorithm coordinating the ability of exploration and exploitation[J]. Control and Decision,2017,32(10):1749-1757.(in Chinese)
[10]徐松金,龙文.嵌入遗传算子的改进灰狼优化算法[J].兰州理工大学学报,2016,42(4):102-108.XU Song-jin,LONG Wen. Improved grey w olf optimization embedded w ith genetic operators[J]. Journal of Lanzhou University of Technology,2016,42(4):102-108.(in Chinese)
[11]MIRJALILI S. How effective is the grey wolf optimizer in training multilayer perceptrons[J]. Applied Intelligence,2015,42(2):608-619.
[12]周凌云,丁立新,彭虎,等.一种邻域重心反向学习的粒子群优化算法[J].电子学报,2017,45(11):2815-2824.ZHOU Ling-yun,DING Li-xin,PENG Hu,et al.,Neighborhood centroid opposition-based particle sw arm optimization[J]. Acta Electronica Sinica,2017,45(11):2815-2824.(in Chinese)
[13]江善和,王其申,江巨浪.一种新型Skew Tent映射的混沌混合优化算法[J].控制理论与应用,2007,24(2):269-273.JIANG Shan-he,WANG Qi-shen,JIANG Ju-lang. Chaotic hybrid optimization algorithm of a new Skew Tent map[J]. Control Theory&Applications,2007,24(2):269-273.(in Chinese)
[2] LONG W,LIANG X,JIAO J,et al.,An exploration-enhanced grey w olf optimizer to solve high-dimensional numerical optimization[J]. Engineering Application of Artificial Intelligence,2018,68:63-80.
[3]GUHA D,ROY P K,BANERJEE S. Load frequency control of interconnected pow er system using grey w olf optimization[J]. Sw arm and Evolutionary Computation,2016,27:97-115.
[4]姚鹏,王宏伦.基于改进流体扰动算法与灰狼优化的无人机三维航路规划[J].控制与决策,2016,31(4):701-708.YAO Peng,WANG Hong-lun. Three-dimensional path planning for UAV based on improved interfered fluid dynamical system and grey w olf optimizer[J]. Control and Decision,2016,31(4):701-708.(in Chinese)
[5]SONG H,SULAIMAN M,MOHAMED M. An application of grey w olf optimizer for solving combined economic emission dispatch problems[J]. International Review on M odeling and Simulation,2014,7(5):838-844.
[6]GUPTA E,SAXENA A. Robust generation control strategy based on grey w olf optimizer[J]. Journal of Electrical Systems,2015,11(2):174-188.
[7]KOMAKI G,KAYVANFAR V. Grey wolf optimizer algorithm for the tw o-stage assembly flow shop scheduling problem w ith release time[J]. Journal of Computational Science,2015,8(3):109-120.
[8]ZHU A,XU C,LI Z,et al. Hybridizing grey wolf optimization w ith differential evolution for global optimization and test scheduling for 3D stacked SoC[J]. Journal of Systems Engineering and Electronics,2015,26(2):317-328.
[9]龙文,伍铁斌.协调探索和开发能力的改进灰狼优化算法[J].控制与决策,2017,32(10):1749-1757.LONG Wen,WU Tiebin. Improved grey w olf optimization algorithm coordinating the ability of exploration and exploitation[J]. Control and Decision,2017,32(10):1749-1757.(in Chinese)
[10]徐松金,龙文.嵌入遗传算子的改进灰狼优化算法[J].兰州理工大学学报,2016,42(4):102-108.XU Song-jin,LONG Wen. Improved grey w olf optimization embedded w ith genetic operators[J]. Journal of Lanzhou University of Technology,2016,42(4):102-108.(in Chinese)
[11]MIRJALILI S. How effective is the grey wolf optimizer in training multilayer perceptrons[J]. Applied Intelligence,2015,42(2):608-619.
[12]周凌云,丁立新,彭虎,等.一种邻域重心反向学习的粒子群优化算法[J].电子学报,2017,45(11):2815-2824.ZHOU Ling-yun,DING Li-xin,PENG Hu,et al.,Neighborhood centroid opposition-based particle sw arm optimization[J]. Acta Electronica Sinica,2017,45(11):2815-2824.(in Chinese)
[13]江善和,王其申,江巨浪.一种新型Skew Tent映射的混沌混合优化算法[J].控制理论与应用,2007,24(2):269-273.JIANG Shan-he,WANG Qi-shen,JIANG Ju-lang. Chaotic hybrid optimization algorithm of a new Skew Tent map[J]. Control Theory&Applications,2007,24(2):269-273.(in Chinese)