新型差分进化模型的多等级子群杂草优化算法
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摘要
针对标准杂草优化算法易出现的早熟、后期收敛速度慢、易陷于局部最优等问题,提出基于新型差分进化模型的多等级子群杂草优化算法(DEMIWO)。首先,引入一种改进型的混合混沌系统对种群进行初始化,提高初始种群的多样性;其次,提出一种按等级分类的组群策略,将种群按适应度分为优、良、中、差四个等级;最后,在繁殖进化阶段,提出新型差分进化模型,对模型中的交叉变异概率进行指数式的非线性动态调整,提高算法的全局寻优能力以及收敛精度。在8个标准测试函数上进行的仿真实验表明,与标准IWO算法及其他常用算法相比,所提算法具有更快的收敛速度和更高的寻优精度,同时能有效避免陷入局部最优。
Aiming at addressing the problems of standard invasive weed optimization algorithm such as premature convergence, slow convergence rate and easily falling into local optimum, a multi-level sub-population Invasive Weed Optimization Algorithm with New Differential Evolution Model(DEMIWO)is proposed. Firstly, an improved mixed chaotic system is adopted to increase the diversity of the initial population. Secondly, a new grouping strategy of different levels is proposed, which divides the population into four subgroups according to fitness. Meanwhile, a new differential evolution model is proposed by dynamically adjusting the crossover probability and mutation probability in the model. In the end,simulation experiment on 8 benchmark test functions shows that the proposed algorithm not only has faster convergence speed and higher convergence accuracy, but also can avoid falling into local optimum effectively compared with the standard IWO algorithm and other optimization algorithms.
Aiming at addressing the problems of standard invasive weed optimization algorithm such as premature convergence, slow convergence rate and easily falling into local optimum, a multi-level sub-population Invasive Weed Optimization Algorithm with New Differential Evolution Model(DEMIWO)is proposed. Firstly, an improved mixed chaotic system is adopted to increase the diversity of the initial population. Secondly, a new grouping strategy of different levels is proposed, which divides the population into four subgroups according to fitness. Meanwhile, a new differential evolution model is proposed by dynamically adjusting the crossover probability and mutation probability in the model. In the end,simulation experiment on 8 benchmark test functions shows that the proposed algorithm not only has faster convergence speed and higher convergence accuracy, but also can avoid falling into local optimum effectively compared with the standard IWO algorithm and other optimization algorithms.
引文
[1]Mehrabian A R,Lucas C.A novel numerical optimization algorithm inspired from weed colonization[J].Ecological Informatics,2006,1(4):355-366.
[2]钱武文,柴军瑞.基于复合形法的聚类遗传算法[J].计算机工程与应用,2017,53(3):87-94.
[3]张锦华,宋来锁,张元华,等.加权变异策略动态差分进化算法[J].计算机工程与应用,2017,53(4):156-162.
[4]Karaboga D,Basturk B.A powerful and efficient algorithm for numerical function optimization:artificial bee colony(ABC)algorithm[J].Journal of Global Optimization,2007,39(3):459-471.
[5]Naidu Y R,Ojha A K.Solving multiobjective optimization problems using hybrid cooperative invasive weed optimization with multiple populations[J].IEEE Transactions on Systems Man&Cybernetics Systems,2016(99):1-12.
[6]Humphrey D,Taubman D.A filtering approach to edge preserving MAP estimation of images[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2011,20(5):1234-1248.
[7]Vega-Pons S,Avesani P.On pruning the search space for clustering ensemble problems[J].Neurocomputing,2015,150(1):481-489.
[8]Panagiotakis C,Grinias I,Tziritas G.Natural image segmentation based on tree equipartition,Bayesian flooding and region merging[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2011,20(8):2276-2287.
[9]Hajimirsadeghi H,Lucas C.A hybrid IWO/PSO algorithm for fast and global optimization[C]//EUROCON 2009(EUROCON’09),2009:1964-1971.
[10]Kennedy J,Eberhart R.Particle swarm optimization[C]//IEEE International Conference on Neural Networks,1995(4):1942-1948.
[11]陈欢,周永权,赵光伟.基于混沌序列的多种群入侵杂草算法[J].计算机应用,2012,32(7):1958-1961.
[12]左旭坤,苏守宝.多子群入侵杂草优化算法研究及应用[J].计算机工程,2014,40(2):184-188.
[13]李森,任晓娜.采用差分进化策略的入侵杂草改进算法及函数优化应用[J].计算机应用与软件,2014,31(6):285-288.
[14]Ahmadi M,Mojallali H.Chaotic invasive weed optimization algorithm with application to parameter estimation of chaotic systems[J].Chaos Solitons&Fractals the Interdisciplinary Journal of Nonlinear Science&Nonequilibrium&Complex Phenomena,2012,45(9/10):1108-1120.
[15]高尚,杨静宇.混沌粒子群优化算法研究[J].模式识别与人工智能,2006,19(2):266-270.
[16]El-Abd M.Opposition-based artificial bee colony algorithm[C]//Genetic and Evolutionary Computation Conference(GECCO 2011),Dublin,Ireland,July 2011:109-111.
[17]陈志刚,梁涤青,邓小鸿,等.Logistic混沌映射性能分析与改进[J].电子与信息学报,2016,38(6):1547-1551.
[18]赵怀勋,甘剑.一种产生混沌序列的改进算法[J].计算机应用,2005,25(35):77-79.
[19]刘波,王凌,金以慧.差分进化算法研究进展[J].控制与决策,2007,22(7):721-729.
[20]杨启文,蔡亮,薛云灿.差分进化算法综述[J].模式识别与人工智能,2008,21(4):506-513.
[21]刘挺,王联国.一种带局部搜索功能的入侵杂草优化算法[J].传感器与微系统,2014,33(9):110-113.
[22]宋玉坚,叶春明,黄佐钘.多智能体入侵杂草算法[J].计算机应用研究,2014,31(10):2957-2961.
[23]任子武,伞冶.自适应遗传算法的改进及在系统辨识中应用研究[J].系统仿真学报,2006,18(1):41-43.
[2]钱武文,柴军瑞.基于复合形法的聚类遗传算法[J].计算机工程与应用,2017,53(3):87-94.
[3]张锦华,宋来锁,张元华,等.加权变异策略动态差分进化算法[J].计算机工程与应用,2017,53(4):156-162.
[4]Karaboga D,Basturk B.A powerful and efficient algorithm for numerical function optimization:artificial bee colony(ABC)algorithm[J].Journal of Global Optimization,2007,39(3):459-471.
[5]Naidu Y R,Ojha A K.Solving multiobjective optimization problems using hybrid cooperative invasive weed optimization with multiple populations[J].IEEE Transactions on Systems Man&Cybernetics Systems,2016(99):1-12.
[6]Humphrey D,Taubman D.A filtering approach to edge preserving MAP estimation of images[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2011,20(5):1234-1248.
[7]Vega-Pons S,Avesani P.On pruning the search space for clustering ensemble problems[J].Neurocomputing,2015,150(1):481-489.
[8]Panagiotakis C,Grinias I,Tziritas G.Natural image segmentation based on tree equipartition,Bayesian flooding and region merging[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2011,20(8):2276-2287.
[9]Hajimirsadeghi H,Lucas C.A hybrid IWO/PSO algorithm for fast and global optimization[C]//EUROCON 2009(EUROCON’09),2009:1964-1971.
[10]Kennedy J,Eberhart R.Particle swarm optimization[C]//IEEE International Conference on Neural Networks,1995(4):1942-1948.
[11]陈欢,周永权,赵光伟.基于混沌序列的多种群入侵杂草算法[J].计算机应用,2012,32(7):1958-1961.
[12]左旭坤,苏守宝.多子群入侵杂草优化算法研究及应用[J].计算机工程,2014,40(2):184-188.
[13]李森,任晓娜.采用差分进化策略的入侵杂草改进算法及函数优化应用[J].计算机应用与软件,2014,31(6):285-288.
[14]Ahmadi M,Mojallali H.Chaotic invasive weed optimization algorithm with application to parameter estimation of chaotic systems[J].Chaos Solitons&Fractals the Interdisciplinary Journal of Nonlinear Science&Nonequilibrium&Complex Phenomena,2012,45(9/10):1108-1120.
[15]高尚,杨静宇.混沌粒子群优化算法研究[J].模式识别与人工智能,2006,19(2):266-270.
[16]El-Abd M.Opposition-based artificial bee colony algorithm[C]//Genetic and Evolutionary Computation Conference(GECCO 2011),Dublin,Ireland,July 2011:109-111.
[17]陈志刚,梁涤青,邓小鸿,等.Logistic混沌映射性能分析与改进[J].电子与信息学报,2016,38(6):1547-1551.
[18]赵怀勋,甘剑.一种产生混沌序列的改进算法[J].计算机应用,2005,25(35):77-79.
[19]刘波,王凌,金以慧.差分进化算法研究进展[J].控制与决策,2007,22(7):721-729.
[20]杨启文,蔡亮,薛云灿.差分进化算法综述[J].模式识别与人工智能,2008,21(4):506-513.
[21]刘挺,王联国.一种带局部搜索功能的入侵杂草优化算法[J].传感器与微系统,2014,33(9):110-113.
[22]宋玉坚,叶春明,黄佐钘.多智能体入侵杂草算法[J].计算机应用研究,2014,31(10):2957-2961.
[23]任子武,伞冶.自适应遗传算法的改进及在系统辨识中应用研究[J].系统仿真学报,2006,18(1):41-43.