改进Hausdorff距离和量子遗传算法在激光制导中的应用

为了在激光成像制导中提高目标识别的精度和实时性,并在遮挡条件下进行有效识别,采用基于改进Hausdorff距离和量子遗传算法的激光图像匹配算法,选择图像的局部边缘特征为特征空间,针对传统Hausdorff算法及几种改进Hausdorff距离存在的问题,提出了一种新的改进Haussdorff距离作为相似性度量;在搜索策略上,选择量子遗传算法进行并行搜索,为防止种群过早收敛,提出了种群灾变策略,并应用动态的量子旋转角调节收敛的速度和方向。通过理论分析和实验验证,取得了不同参量条件下的目标识别对比数据。结果表明,该算法可以消除激光图像中局部遮挡、噪声以及出格点等因素影响,鲁棒性好、匹配精度高、计算速度快。     

基于异构多种群的一种改进遗传算法

针对简单遗传算法存在的问题，基于提高遗传算法搜索效率和收敛性，提出了一种基于异构多个种群的改进的遗传算法IMGA。该算法首先利用多个异构子种群并行进化的结果初步确定较好解，进化到一定时间后，把得到的优质解按照新的变异算子进一步寻找最优解。仿真结果验证了算法的良好性能。     

混合精英策略的元胞多目标遗传算法及其应用

为了提高Pareto解集的收敛性,平衡多目标优化的全局搜索和局部寻优的能力,提出一种混合精英策略的元胞多目标遗传算法。该算法在分析元胞种群结构的特点基础上,融入一种混合精英策略,提高算法的收敛性能。为了更好的平衡算法的全局搜索和局部寻优的能力,加入一种差分进化交叉算子。通过与同类算法在21个基准函数上对比实验,结果表明,引入混合精英策略和差分进化策略能够提高算法的性能,与其他优秀算法进行比较的结果说明,新算法有更好的收敛性和多样性。工程实例求解结果表明了算法的工程可行性。     

Genetic recurrent fuzzy system by coevolutionary computation with divide-and-conquer technique

A genetic recurrent fuzzy system which automates the design of recurrent fuzzy networks by a coevolutionary genetic algorithm with divide-and-conquer technique (CGA-DC) is proposed in this paper. To solve temporal problems, the recurrent fuzzy network constructed from a series of recurrent fuzzy if-then rules is adopted. In the CGA-DC, based on the structure of a recurrent fuzzy network, the design problem is divided into the design of individual subrules, including spatial and temporal, and that of the whole network. Then, three populations are created, among which two are created for spatial and temporal subrules searches, and the other for the whole network search. Evolution of the three populations are performed independently and concurrently to achieve a good design performance. To demonstrate the performance of CGA-DC, temporal problems on dynamic plant control and chaotic system processing are simulated. In this way, the efficacy and efficiency of CGA-DC can be evaluated as compared with other genetic-algorithm-based design approaches.     

基于多算子协同进化的自适应并行量子遗传算法

量子遗传算法具有种群规模小,全局搜索能力强的特点被广泛应用于各类优化问题的求解.为了进一步提高量子遗传算法的收敛速度和搜索稳定性,克服算法的早熟问题,本文改进了基于自适应机制的量子遗传算法.在自适应量子遗传算法的基础上根据种群的适应度定义了个体相似度评价算子、个体适应度评价算子和种群变异调整算子及相应算子的计算方法,利用多算子协同评价当前种群状态并根据进化代数的变化,自适应的改变个体的变异概率,提高了算法全局寻优能力和收敛速度,降低了算法陷入局部寻优的概率.此外,为了提高算法的时间效率,将算法采用并行多宇宙的方式实现.实验结果表明,本文提出的算法在全局搜索性能、收敛速度和时间效率方面有较好的综合表现.     

动态环境下基于多人工鱼群算法和避碰规则库的机器人路径规划

为了提高机器人路径规划的速度、环境适应能力和高效动态避碰问题,提出了一种基于多人工鱼群的机器人路径规划算法和基于避碰规则库的动态避障算法.该算法中,人工鱼以其与目标点的距离为食物浓度,两个邻近栅格的距离为步长,其觅食行作为默认行为,在一定条件下执行聚群或追尾动作,并采用两鱼群双向搜索机制在静态环境下规划出较优路径.在此基础上,机器人查询动态避障规则库获得避碰方法,从而实现与动态障碍的避碰.大量仿真实验结果表明,该方法具有较高的收敛速度和较强的搜索能力,能在非常复杂的动静态障碍环境中,迅速规划出一条安全避碰的优化路径.     

Misbehavior analysis of IEEE 802.11 MAC layer in mobile ad hoc network using stochastic reward nets

In this paper, we present a Petri net model for performance evaluation of IEEE 802.11 distributed coordination function as a popular media access control layer protocol in mobile ad hoc network. The goal of this evaluation is to examine this protocol under the existing of misbehavior nodes that selfishly try to grasp common channel in a neighbor area. The presented model consists of 2 separate models based on stochastic reward net (SRN), as a variation of stochastic Petri net. The first model, which is called one node operation model , is supposed for presenting all distributed coordination function operations in a single node such as collision avoidance, request to send/clear to send (RTS/CTS) handshake, and backoff mechanism. The next SRN model, all node operation model , is used for modeling nodes competition for occupying channel in a neighbor area. The models could be adjusted to a dynamic network with any number of nodes, dimension scale, and nodes' speed. For evaluation purpose, 4 distinct attack types implemented by modifying associated transitions in SRN models. The proposed SRN model has been quantified by deriving 2 performances metrics as Throughput and Delay . Both metrics are also compared to the value obtained from NS‐2 in terms of different number of nodes and 3 packet generation rates. Three additional metrics measuring the channel usage are also quantified in terms of different attack strategies using only presented SRN model.     

Joint packet scheduling and dynamic base station assignment for CDMA data networks

In current code division multiple access (CDMA) based wireless systems, a base station (BS) schedules packets independently of its neighbours, which may lead to resource wastage and the degradation of the system's performance. In wireless networks, in order to achieve an efficient packet scheduling, there are two conflicting performance metrics that have to be optimized: throughput and fairness. Their maximization is a key goal, particularly in next-generation wireless networks. This paper proposes joint packet scheduling and BS assignment schemes for a cluster of interdependent neighbouring BSs in CDMA-based wireless networks, in order to enhance the system performance through dynamic load balancing. The proposed schemes are based on sector subdivision in terms of average required resource per mobile station and utility function approach. The fairness is achieved by minimizing the variance of the delay for the remaining head-of-queue packets. Inter-cell and intra-cell interferences from scheduled packets are also minimized in order to increase the system capacity and performance. The simulation results show that our proposed schemes perform better than existing schemes available in the open literature. Copyright © 2007 John Wiley & Sons, Ltd.     

Identifying deficits of visual security metrics for images

Visual security metrics are deterministic measures with the (claimed) ability to assess whether an encryption method for visual data does achieve its defined goal. These metrics are usually developed together with a particular encryption method in order to provide an evaluation of said method based on its visual output. However, visual security metrics themselves are rarely evaluated and the claim to perform as a visual security metric is not tied to the specific encryption method for which they were developed. In this paper, we introduce a methodology for assessing the performance of security metrics based on common media encryption scenarios. We systematically evaluate visual security metrics proposed in the literature, along with conventional image metrics which are frequently used for the same task. We show that they are generally not suitable to perform their claimed task.     

基于遗传算法寻优的PID控制及MATLAB仿真

文中介绍了遗传算法和基于遗传算法寻优的PID控制设计,采用误差绝对值时间积分性能指标作为参数选择的最小目标函数,利用遗传算法的全局搜索能力,使得在无须先验知识的情况下实现对全局最优解的寻优,以降低PID参数整定的难度,达到总体上提高系统的控制精度和鲁棒性的目的。通过MATLAB仿真结果表明,根据遗传算法寻优设计的PID控制器具有较强的鲁棒性,即使在外部环境恶劣的条件下,系统的控制性能仍具有较好的动态品质和稳态精度。     

一种基于禁忌搜索的全局最优化模糊聚类算法

模糊C均值（FCM）算法是一种基于贪心思想的迭代算法,算法沿迭代序列收敛到一个极小值,但存在搜索能力弱、易陷入局部最优的缺点.本文提出了一种基于禁忌搜索的模糊聚类算法,该算法在一个解的邻域内使用禁忌搜索,并采用了基于FCM局部收敛性质的长期表禁忌策略,保证在不断移动搜索起点的同时避免重复搜索;其次使用混沌优化思想与动态步长策略来提升算法的全局搜索能力,以达到获取全局最优解的目的.实验结果表明,改进算法极大地提高了聚类准确率,并具有良好的稳定性,与群智算法和遗传算法的优化相比也具有一定的优势.     

改进的遗传算法求解背包问题

为提高遗传算法在解决背包问题时的局部搜索能力,在遗传算法中加入禁忌搜索的思想,用遗传算法做全局搜索,禁忌搜索辅助做局部搜索。文中阐述了遗传算法和禁忌搜索算法的基本思想,并给出了适用于背包问题的模型。通过具体事例测试改进的算法,其结果表明改进后的遗传算法拥有更好的性能和更快的收敛速度。     

基于Memetic算法的电路演化设计研究

 针对传统演化算法在设计数字逻辑电路时存在的演化速度缓慢和容易陷入局部最优解等问题,设计了一种Cartesian进化编程编码的电路演化Memetic算法,采用遗传算法作为全局搜索方法,并设计了适合电路演化的基本门种类局部搜索策略.通过一位全加器电路证明所提出的Memetic硬件演化算法的搜索能力,实验证明所提出的算法能够有效地完成进化任务,具有较强的全局快速和局部搜索能力.     

基于遗传-细菌觅食组合算法的非线性模型优化

文中提出一种遗传-细菌觅食组合优化算法以解决非线性模型优化问题。该方法先使用遗传算法进行全局搜索,并缩小最优解的搜索范围;再使用细菌觅食优化算法在该局部范围内执行局部搜索。这种组合搜索策略可以增强算法的收敛性,并能有效地均衡全局搜索和局部搜索。文中利用单峰、多峰和复杂多峰等非线性函数模型验证所提算法的性能。实验结果表明,组合算法的计算精度和效率分别比遗传算法和细菌觅食优化算法提高了30%和50%,表明该组合算法具有更快的收敛速度,更高的求解精度,适用于大规模多极值的非线性问题。     

量子计算与遗传算法的融合及其在计算机通信网优化中的应用

该文将量子计算与遗传算法进行融合,其核心是在常规遗传算法中将量子的态矢量引入遗传编码,并自适应地进行量子旋转门的调整以实现染色体的演化,使算法具有更好的种群多样性和全局寻优能力。通过求解计算机通信网优化问题的实例,结果表明:新方法比采用常规遗传算法具有明显的高效性。     

Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms

A recurrent fuzzy neural network (RFNN) controller based on real-time genetic algorithms (GAs) is developed for a linear induction motor (LIM) servo drive in this paper. First, the dynamic model of an indirect field-oriented LIM servo drive is derived. Then, an online training RFNN with a backpropagation algorithm is introduced as the tracking controller. Moreover, to guarantee the global convergence of tracking error, a real-time GA is developed to search the optimal learning rates of the RFNN online. The GA-based RFNN control system is proposed to control the mover of the LIM for periodic motion. The theoretical analyses for the proposed GA-based RFNN controller are described in detail. Finally, simulated and experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust with regard to plant parameter variations and external load disturbance     

Quantified Cost-Balanced routing scheme for overlay multicast

This paper focuses on the quantitative analysis issue of the routing metrics tradeoff problem, and presents a Quantified Cost-Balanced overlay multicast routing scheme （QCost-Balanced） to the metric tradeoff problem between overlay path delay and access bandwidth at Multicast Server Nodes （MSN） for real-time applications over Internet. Besides implementing a dynamic priority to MSNs by weighing the size of its service clients for better efficiency, QCost-Balanced tradeoffs these two metrics by a unified tradeoff metric based on quantitative analysis. Simulation experiments demonstrate that the scheme achieves a better tradeoff gain in both two metrics, and effective performance in metric quantitative control.     

Research on user-aware QoS based Web services composition

To improve user experience of composite Web services, a user-aware quality of service (QoS) based Web services composition model is proposed. Under such model, a Web services selection method based on quantum genetic algorithm is proposed. This algorithm uses quantum bit encoding, dynamic step-length quantum gate angle adjustment, neighborhood service search and dynamic punishment strategy to expand search scope and speed up convergence. Simulation experiment shows that this algorithm is more efficient than other existing algorithms in Web services selection.     

基于量子遗传算法的航空通信频率动态分配

为了更加有效地对航空通信频率进行分配,提出了一种基于量子遗传算法的航空通信频率动态分配方法。通过对频率动态分配思路进行分析,建立了频率动态分配框架,给出了频率动态分配的具体流程。在此基础上,讨论了航空通信频率动态分配问题,定义了航空通信频率动态分配约束条件,建立了航空通信频率动态分配模型。最后,运用量子遗传算法和遗传算法对算例进行仿真对比。结果表明：量子遗传算法在种群适应度和收敛速度上具备明显的优越性,频率动态分配模型能够根据不同种群数量条件动态调整适应度,能够较好满足航空通信频率分配问题动态性、准确性、时效性等实践运用要求。     

最小误码率准则盲源分离算法

针对通信混合信号的盲分离问题,结合通信系统中的误码率性能指标,本文提出一种基于最小误码率准则的盲源分离算法。本算法基本原理是,将推导的最小误码率准则结合最大似然原则建立盲源分离代价函数,形成一种最小误码率约束的代价函数,接着借助于自然梯度下降搜索,最小化代价函数实现盲源分离。仿真实验分析表明提出的最小误码率约束代价函数得到的盲源分离算法,比原有的最大似然原则代价函数得到的盲源分离算法,具有更好的收敛性能和分离精度。