In this paper,based on coupled network generated by chaotic logarithmic map,a novel algorithm for constructing hash functions is proposed,which can transform messages and can establish a mapping from the transformed m...In this paper,based on coupled network generated by chaotic logarithmic map,a novel algorithm for constructing hash functions is proposed,which can transform messages and can establish a mapping from the transformed messages to the coupled matrix of the network.The network model is carefully designed to ensure the network dynamics to be chaotic.Through the chaotic iterations of the network,quantization and exclusive-or (XOR) operations,the algorithm can construct hash value with arbitrary length.It is shown by simulations that the algorithm is extremely sensitive to the initial values and the coupled matrix of the network,and has excellent performance in one-way,confusion and diffusion,and collision resistance.展开更多
针对灰狼优化(grey wolf optimization,GWO)算法在求解复杂高维优化问题时存在解精度低、易陷入局部最优等缺点,提出一种基于对数函数描述收敛因子的改进GWO算法。采用佳点集方法初始化种群以保证个体尽可能均匀地分布在搜索空间中;提...针对灰狼优化(grey wolf optimization,GWO)算法在求解复杂高维优化问题时存在解精度低、易陷入局部最优等缺点,提出一种基于对数函数描述收敛因子的改进GWO算法。采用佳点集方法初始化种群以保证个体尽可能均匀地分布在搜索空间中;提出一种基于对数函数描述的非线性收敛因子替代线性递减收敛因子,以协调算法的勘探和开采能力;对当前最优的3个个体执行改进的精英反向学习策略产生精英反向个体,以避免算法出现早熟收敛。研究结果表明改进算法具有较好的寻优性能。展开更多
基金supported by the Program for New Century Excellent Talents in University of China(No.NCET-06-0510)National Natural Science Founda-tion of China(No. 60874091)Six Projects Sponsoring Talent Summits of Jiangsu Province(No. SJ209006)
文摘In this paper,based on coupled network generated by chaotic logarithmic map,a novel algorithm for constructing hash functions is proposed,which can transform messages and can establish a mapping from the transformed messages to the coupled matrix of the network.The network model is carefully designed to ensure the network dynamics to be chaotic.Through the chaotic iterations of the network,quantization and exclusive-or (XOR) operations,the algorithm can construct hash value with arbitrary length.It is shown by simulations that the algorithm is extremely sensitive to the initial values and the coupled matrix of the network,and has excellent performance in one-way,confusion and diffusion,and collision resistance.
文摘针对灰狼优化(grey wolf optimization,GWO)算法在求解复杂高维优化问题时存在解精度低、易陷入局部最优等缺点,提出一种基于对数函数描述收敛因子的改进GWO算法。采用佳点集方法初始化种群以保证个体尽可能均匀地分布在搜索空间中;提出一种基于对数函数描述的非线性收敛因子替代线性递减收敛因子,以协调算法的勘探和开采能力;对当前最优的3个个体执行改进的精英反向学习策略产生精英反向个体,以避免算法出现早熟收敛。研究结果表明改进算法具有较好的寻优性能。
