The optimization of large gap-midgap ratio photonic crystal with improved Bisection-Particle Swarm Optimization

Photonic crystals possessing large gap-midgap ratios are designed using the improved Bisection-Particle Swarm Optimization (BPSO) algorithm and plane-wave expansion method. The optimal gap-midgap ratios of three kinds of photonic crystals, such as ring rods, square rods connected by veins, and rotating square rods, are calculated, and the corresponding geometry parameters are also represented. Compared with the traditional dimension-by-dimension scanning method, the improved BPSO algorithm greatly reduces the computation time, and its accuracy is controllable.     

基于混合生物地理优化的混沌系统参数估计

混沌系统的参数估计本质上是多维参数的优化问题. 结合和声搜索方法和对立学习机理, 提出一种混合生物地理优化算法, 用于解决混沌系统的参数估计问题. 利用对立学习机理增加初始群体的多样性, 并引入和声搜索以增强局部寻优能力, 从而提升整体寻优性能. 以典型混沌系统为例进行了未知参数估计的数值仿真, 结果验证了所提出混合生物地理优化方法的有效性和鲁棒性.     

基于混沌蚂蚁群算法的Lorenz混沌系统的参数估计

通过构造一个适当的适应度函数，首先将混沌系统的参数估计问题转化为参数的寻优问题，之后利用混沌蚂蚁群算法的全局优化搜索能力对这个问题进行求解.以典型的Lorenz混沌系统为例进行了数值模拟.实验数值仿真结果表明，使用该方法可以对混沌系统的未知参数进行有效地估计.     

On a recursive method for the estimation of unknown parameters of partially observed chaotic systems

We investigate a recently proposed method for on-line parameter estimation and synchronization in chaotic systems. This novel technique has been shown effective to estimate a single unknown parameter of a primary chaotic system with known functional form that is only partially observed through a scalar time series. It works by periodically updating the parameter of interest in a secondary system, with the same functional form as the primary one but no explicit coupling between their dynamic variables, in order to minimize a suitably defined cost function. In this paper, we review the basics of the method, and investigate its robustness and new extensions. In particular, we study the performance of the novel technique in the presence of noise (either observational, i.e., an additive contamination of the observed time series, or dynamical, i.e., a random perturbation of the system dynamics) and when there is a mismatch between the primary and secondary systems. Numerical results, including comparisons with other techniques, are presented. Finally, we investigate the extension of the original method to perform the estimation of two unknown parameters and illustrate its effectiveness by means of computer simulations.     

Parameter estimation for chaotic systems with and without noise using differential evolution-based method

We present an approach in which the differential evolution (DE) algorithm is used to address identification problems in chaotic systems with or without delay terms. Unlike existing considerations, the scheme is able to simultaneously extract (i) the commonly considered parameters, (ii) the delay, and (iii) the initial state. The main goal is to present and verify the robustness against the common white Guassian noise of the DE-based method. Results of the time-delay logistic system, the Mackey-Glass system and the Lorenz system are also presented.     

The evolution of cooperation within the multigame environment based on the Particle Swarm Optimization algorithm

This paper studies the evolution of cooperation in a so-called multigame environment based on the Particle Swarm Optimization (PSO) algorithm. In a multigame environment, players use different game payoff matrices and acquire their utilities from their interactions with their neighbors. According to the PSO algorithm, each player updates its strategy according to both the strategy adopted by the player with the highest utility in its neighborhood and the most profitable strategy in their own past actions. Simulation results show that the multigame environment is conducive to the promotion of cooperation. Besides, within the multigame environment, for any player, imitating the most profitable strategy in its past actions promotes cooperation more effectively than imitating the strategy adopted by the player with the highest utility in its neighborhood.     

Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method

We study the parameter estimation of a nonlinear chaotic system,which can be essentially formulated as a multidimensional optimization problem.In this paper,an orthogonal learning cuckoo search algorithm is used to estimate the parameters of chaotic systems.This algorithm can combine the stochastic exploration of the cuckoo search and the exploitation capability of the orthogonal learning strategy.Experiments are conducted on the Lorenz system and the Chen system.The proposed algorithm is used to estimate the parameters for these two systems.Simulation results and comparisons demonstrate that the proposed algorithm is better or at least comparable to the particle swarm optimization and the genetic algorithm when considering the quality of the solutions obtained.     

基于改进粒子群优化算法的混沌系统参数估计方法

估计混沌系统的未知参数是混沌控制与同步中必须解决的关键问题.利用群集智能的新进展粒子群优化算法(PSO)的全局搜索能力，从初始粒子群的产生、目标函数的处理的角度改进PSO，将改进的PSO引入混沌系统参数估计和在线估计.仿真试验表明，改进算法具有良好的适应性、较高的收敛可靠性及精度，对信号叠加噪声的情形也具有较高的鲁棒性，是混沌系统参数估计的一种成功算法. 关键词： 混沌系统 参数估计 在线估计 粒子群优化算法     

混合粒子群算法ADPSO对月球物理参数反演的改进

常规粒子群算法PSO对低敏感参数较难进行最优估计,本文提出联合自适应惯性权重和变异的混合粒子群算法ADPSO.结果表明：最佳拟合效果不随粒子数和变异概率的增大而加强.就四参数估计而言,粒子数取400个,变异概率小于0.03,可实现最优拟合.采用最佳粒子数和较小的变异概率,利用新近月球重力场模型GL0990d和LRO激光测高数据,对月球南半球部分高地的物理参数进行最优估计.发现研究区域的模型导纳谱很好地拟合了观测值,模型重力异常与观测重力异常的残差较小,符合参数估计的要求,验证了算法的有效性.ADPSO适合于月球物理参数估计,为大规模的反演运算提供了参考.     

A tri-objective Particle Swarm Optimizer for designing line defect Photonic Crystal Waveguides

This paper proposes a novel tri-objective approach for optimizing the structure of line defect Photonic Crystal Waveguides (PCW). A nature-inspired algorithm called Multi-Objective Particle Swarm Optimization (MOPSO) is employed as the optimizer. The three objectives considered are maximization of group index, maximization of bandwidth, and minimization of Group Velocity Dispersion (GVD). In addition, the optimization process is subject to five constraints in order to guarantee the feasibility of the structures obtained and prevent bad mixing in the final optimized structures. The results show that the tri-objective MOPSO is able to find 20 optimized structures for line defect PCW. The comparative study verifies the significant improvement of the optimized structures compared to current structures. Moreover, post analysis of the results reveals the importance of holes and their possible physical behaviours in line defect PCW. Finally, the implementation considerations and investigations show that the optimized structures are feasible for manufacturing with a resolution of 1 nm.     

A hybrid Particle Chemical Reaction Optimization for biological image matching based on lateral inhibition

In this paper, a hybrid Particle Chemical Reaction Optimization (PCRO) algorithm and lateral inhibition is proposed to solve the image matching problem. Lateral inhibition has the ability to enhance the characters of image, which can help to improve the accuracy of image matching. In order to overcome the shortcomings of basic Chemical Reaction Optimization (CRO) algorithm, we improve CRO by proposing PCRO which inspired from the thought of Particle Swarm Optimization (PSO). Comparative experimental results in image matching show that our proposed hybrid method performs much better than other bio-inspired algorithms.     

基于混合差分进化算法的混沌系统参数估计

混沌系统参数估计是混沌系统控制与同步的关键问题. 通过构造一个适当的适应度函数,将混沌系统的参数估计问题转化为一个多维优化问题,然后利用混合差分进化算法的全局搜索能力求解该优化问题. 以典型的Lorenz混沌系统为例进行了数值仿真,结果表明了混合差分进化算法的有效性和鲁棒性,是一种有效的混沌系统参数估计方法. 关键词： 混沌系统 参数估计 混合差分进化算法     

Full-order and reduced-order observer-based synchronization for chaotic systems with unknown disturbances and parameters

The Letter deals with the problem of synchronization of chaotic dynamic system with unknown disturbances and parameters based on observer. First, under some assumptions for drive system, a kind of full-order observer-based synchronization method is summarized. The response system is a robust adaptive full-order observer with adaptation laws for the unknown disturbances and parameters. Second, under the same assumptions, a reduced-order observer-based response system which can synchronize part states of drive system is developed. By choosing a special reduced-order gain matrix, the reduced-order observer-based response system is able to eliminate the influence of the unknown disturbances and parameters directly, so it is unnecessary for one to design the adaptation laws of them. Finally, some numerical simulations for Lorenz chaotic system are design and the simulation results are analyzed in detail.     

基于量子粒子群算法的混沌系统同步控制及参数辨识

针对异结构且参数未知混沌系统的同步控制,设计同步控制器并对误差系统的稳定性进行理论分析.通过合理选择优化目标函数,利用量子粒子群算法高效的全局寻优能力,实现混沌系统的未知参数辨识,有效降低同步控制器的开发难度.以Rössler混沌系统与Lorenz混沌系统的异结构同步为研究对象,进行数值仿真实验,结果表明该方法能够实现驱动-响应系统的快速混沌同步及系统未知参数的准确辨识,并验证该方法的可行性和有效性.     

Globally exponential multi switching-combination synchronization control of chaotic systems for secure communications

This article introduces the global exponential multi switching combination synchronization (GEMSCS) for three different chaotic systems with known parameters in the master-slave system configuration. The proposed GEMSCS scheme establishes the global exponential stability of the synchronization error at the origin with different combinations of state variables of the two master chaotic systems with the state variables of a slave chaotic system in diverse manners. Consequently, it increases the complexity level of the information signal in secure communications. To study the GEMSCS, an efficient nonlinear control algorithm is designed. The Lyapunov direct theorem is used to accomplish the global exponential stability of the synchronization error at the origin. The stability conditions are derived analytically. To show the effectiveness and advantages of the proposed GEMSCS control approach, two numerical examples are presented. The computer based simulation results are compared with the reported works in the relevant literature. This article also extends the idea of GEMSCS to the secure communication using the chaotic masking technique. Using the GEMSCS strategy, the information signal is recovered at the receiving system with good accuracy and high speed while the parameters of the transmitter and receiver systems mismatch. At the end, some future research problems related to this work are suggested.     

粒子群算法的近红外光谱定性分析预处理及特征提取参数优化方法研究

在近红外光谱定性分析时,为取得良好识别效果,预处理及特征提取是不可或缺环节.预处理主要是为消除各种干扰因素对光谱数据影响,常用预处理方法有平滑、一阶导、归一化等;而特征提取方法能剔除数据中的无关信息,保留有效信息,常用特征提取方法有偏最小二乘、主成分分析、线性判别分析等.不同预处理及特征提取方法具有不同特点,构建定性分...     

一种基于混沌系统部分序列参数辨识的混沌保密通信方法

本文提出一种混沌保密通信方法,即混沌系统的部分序列用于混沌系统参数辨识其他序列用于通信保密.利用混沌蚁群优化算法对部分序列混沌系统进行参数辨识,以达到了解混沌系统全部信息的目的.在参数辨识过程中引入参数空间和蚁群空间,通过空间变换函数使参数空间与蚁群空间之间相互变换.文中使用Lorenz系统进行数值试验,其结果验证混沌系统部分序列参数辨识及混沌保密通信的可行性.     

基于量子并行粒子群优化算法的分数阶混沌系统参数估计

分数阶混沌系统参数估计的本质是多维参数优化问题, 其对于实现分数阶混沌控制与同步至关重要. 提出一种基于量子并行特性的粒子群优化新算法, 用于解决分数阶混沌的系统参数估计问题. 利用量子计算的并行特性, 设计出了一种新的量子编码, 使每代运算的可计算次数呈指数增加. 在此基础上, 构建了由量子当前旋转角、个体最优旋转角和全局最优旋转角共同组成的粒子演化方程, 以约束粒子在量子空间中的运动行为, 使算法的搜索能力得到了较大提高. 以分数阶Lorenz混沌系统和分数阶Chen混沌系统的参数估计为例, 进行了未知参数估计的数值仿真, 结果显示本算法具有良好的有效性、鲁棒性和通用性.     

A comparative study between LMS and PSO algorithms on the optical channel estimation for radio over fiber systems

In this paper the particle swarm optimization (PSO) and least mean square (LMS) algorithms are comparatively studied to estimate the optical communication channel parameters for radio over fiber systems. It is observed that especially in low noise one tap optical channels, the convergence of LMS algorithm is approximately same with PSO algorithm. On the other hand, as a communication medium, selecting high noisy fiber optical channels or free space optical channels; PSO reaches better mean square error values. The computational complexity which is one of the most important features for optimization algorithms has also been taken into account.     

基于混合交叉进化算法的混沌系统参数估计

提出一种混合交叉进化算法 来估计混沌系统的未知参数. 首先通过构造一个适当的适应度函数, 将混沌系统的参数估计问题转化为一个多维的优化问题. 在混合交叉进化算法中, 利用佳点集方法初始化种群, 增加了算法的稳定性和全局搜索能力. 在进化过程中, 混合交叉操作既能指导种群个体向最优解子空间靠近, 又能提高算法跳出局部最优的能力, 从而协调了算法的勘探和开采能力. 以几个标准测试函数和典型的Lorenz混沌系统为例进行仿真实验, 结果表明了该方法的有效性.