一种结合模拟退火和贪心策略的社团识别算法

为了提高复杂网络社团识别的精度和速度,文中结合模拟退火和贪心策略识别社团结构的优势,提出一种新的社团识别算法。该算法利用贪心策略引导模拟退火搜索最优解过程中单个结点的无规则盲目移动,消除了大量无效移动,在搜索到全局最优解的情况下,将搜索时间大幅缩减。实验表明,SAGA具有强大的搜索能力和较快的模拟退火执行速度,可获得较高的模块度,达到较为准确的社团分割,且具有一定的应用价值。     

一种针对频率分配问题的改进ANTS算法

频率分配问题是近年来通信领域研究的热点。针对FAP问题提出了一种结合模拟退火算法的改进ANTS算法。运用模拟退火算法产生次优解,利用次优解分配初始信息素,并利用ANTS算法来寻求最佳方案。在ANTS算法的每个蚂蚁寻找局部最优过程中,为了加快运算速度,对局部寻优过程进行了改进。实验结果表明,在解质量相当的情况下,该算法能够大大地加快收敛速度,特别是针对一些较复杂的分配情况,效果明显。     

A tabu search algorithm for routing optimization in mobile ad-hoc networks

In this paper, we propose a routing optimization algorithm to efficiently determine an optimal path from a source to a destination in mobile ad-hoc networks. To determine an optimal path for the nodes is important for transmitting data between nodes in densely deployed networks. In order to efficiently transmit data to its destination, the appropriate routing algorithms must be implemented in mobile ad-hoc networks. The proposed algorithm is designed by using a tabu search mechanism that is a representative meta-heuristic algorithm. The proposed tabu search algorithm carries out two neighborhood generating operations in order to determine an optimal path and minimize algorithm execution time. We compare the proposed tabu search algorithm with other meta-heuristic algorithms, which are the genetic algorithm and the simulated annealing, in terms of the routing cost and algorithm execution time. The comparison results show that the proposed tabu search algorithm outperforms the other algorithms and that it is suitable for adapting the routing optimization problem.     

基于函数复杂度的自适应模拟退火和禁忌搜索新算法

在求解多峰复杂函数的过程中,传统的模拟退火算法和禁忌搜索算法经常出现算法快速收敛于局部最优解、后期收敛速度变慢和搜索能力变差等问题.为解决这些问题,本文给出函数复杂度的定义,并提出基于函数复杂度的自适应模拟退火和禁忌搜索算法.该算法首先根据函数复杂度自适应调整步长控制参数,然后根据调整后步长求得函数的粗糙解,在此基础上再使用初始步长求得全局最优解.实验表明,该算法不仅可以跳出局部最优解的限制,并且减少了迭代次数,有效地提高了全局和局部搜索能力.     

一种矩形邻域结构的教学优化算法

为了克服原始教学优化算法在求解复杂多峰函数时全局寻优精度不高和过早收敛的缺点,提出一种矩形邻域结构和个体扰动的教学优化算法.算法将种群空间设计为矩形结构,个体的矩形邻域由矩形厚度和围绕其的矩形区域个体决定,教和学两个阶段都使用邻域最优个体引导搜索,加强了算法勘探新解和开发局部最优解的能力;为了防止算法过早陷入局部最优,增加了基于搜索边界信息引导的个体扰动阶段,使得种群即使在进化的后期仍能保持较好的多样性.对带有偏移和旋转的复杂函数进行仿真测试,结果表明新算法在求解精度和稳定性方面,在绝大多数情况下优于原始教学算法和其他一些近来的优秀改进教学算法.     

一种新的数字调制信号分类特征选择算法

针对调制信号分类特征选择问题,提出了自适应惯性权重模拟退火二进制离散粒子群算法。该算法将模拟退火算法嵌入到离散粒子群算法循环体中,利用模拟退火算法具有较强的局部搜索能力和避免陷入局部最优解的特点,解决了简单智能优化算法早熟收敛和局部搜索能力弱等问题。仿真结果表明,该算法能有效选取最优特征,性能优于简单离散粒子群算法和遗传算法。     

不确定性环境下基于进化算法的强化学习

不确定性和隐状态是目前强化学习所要面对的重要难题.本文提出了一种新的算法MA-Q-learning算法来求解带有这种不确定性的POMDP问题近似最优策略.利用Memetic算法来进化策略,而Q学习算法得到预测奖励来指出进化策略的适应度值.针对隐状态问题,通过记忆agent最近经历的确定性的有限步历史信息,与表示所有可能状态上的概率分布的信度状态相结合,共同决策当前的最优策略.利用一种混合搜索方法来提高搜索效率,其中调整因子被用于保持种群的多样性,并且指导组合式交叉操作与变异操作.在POMDP的Benchmark实例上的实验结果证明本文提出的算法性能优于其他的POMDP近似算法.     

一种降低OFDM系统峰均比的快速算法

OFDM技术具有较高的频谱利用率和良好的抗多径衰落性能,目前已在很多领域得到了应用,但它存在的高峰均比问题对系统的性能产生了一定影响。采用优化的模拟退火PTS算法,通过保存中间最优解,控制降温幅度,设置双阈值和链接补充搜索过程,不仅加快了普通模拟退火算法的收敛速度,而且具备良好的搜索精度。仿真结果表明,所提算法既保持较低的计算复杂度,同时具有良好的峰均功率比性能。     

Numerical annealing of low-redundancy linear arrays

An algorithm is developed that estimates the optimal distribution of antenna elements in a minimum redundancy linear array. These distributions are used in thinned array interferometric imagers to synthesize effective antenna apertures much larger than the physical aperture. The optimal selection of antenna locations is extremely time consuming when large numbers of antennas are involved. This algorithm uses a numerical implementation of the annealing process to guide a random search for the optimal array configuration. Highly thinned low-redundancy arrays are computed for up to 30 array elements. These arrays are equivalent to the optimal solutions that are known for up to 11 elements. The arrays computed for 12-30 elements have the fewest redundancies reported to date     

一种抗NLOS的TDOA定位算法

本文提出了一种抗非视线传播(Non-Line-of-Sight)干扰的TDOA定位算法。该算法采用退火与遗传算法相结合的方式，在满足给定条件的空间内搜索源点坐标值。数值仿真表明，该算法具备抗NLOS干扰的能力，与传统TDOA算法相比，该算法无需知道各基站的非视线传播干扰分布，是一种实用有效的定位算法。     

数字同轴全息对刀技术中微细铣刀的自动聚焦方法

数字同轴全息对刀技术中微细铣刀全息再现像的自动聚焦是实现高精度自动对刀的关键技术,其中聚焦评价函数是判别图像质量的依据.通过比较几种常用的聚焦评价函数的评价性能,探讨了数字全息自动对焦过程中刀具的成像特点以及适用的聚焦评价方法,发现小波变换聚焦评价函数能适应高精度聚焦的需要.针对自动调焦问题,提出一种分段递进搜索方法,将搜索过程分为两个过程:大步距粗调和小步距精调,分段搜索最优解.然后比较分析了所提搜索方法与经典的模拟退火算法和Levenberg-Marquardt算法的搜索性能.实验结果表明,所提分段递进搜索方法适用性更强,并通过计算机模拟实验进一步验证了其有效性.     

模拟退火法用于气体红外光谱数据的识别分类

本文介绍了一种新的分类方法,它克服了传统的线性局部搜索的缺点,引入了跳跃式搜索,使得算法能够跳出局部极优解的陷阱,从而找到全局最优解。文中详细地介绍了其原理,并利用模拟退火的跳跃式搜索方法进行算法训练。通过实例比较,说明本方法应用于大气遥感红外光谱数据的分类和识别时,是行之有效的。     

An adaptive hybrid memetic algorithm for thermal-aware non-slicing VLSI floorplanning

Floorplanning is a crucial step in very large scale integration design flow. It provides valuable insights into the hardware decisions and estimates a floorplan with different cost metrics. In this paper, to handle a multi-objective thermal-aware non-slicing floorplanning optimization problem efficiently, an adaptive hybrid memetic algorithm is presented to optimize the area, the total wirelength, the maximum temperature and the average temperature of a chip. In the proposed algorithm, a genetic search algorithm is used as a global search method to explore the search space as much as possible, and a modified simulated annealing search algorithm is used as a local search method to exploit information in the search region. The global exploration and local exploitation are balanced by a death probability strategy. In this strategy, according to the natural mechanisms, each individual in the population is endowed with an actual age and a dynamic survival age. Experimental results on the standard tested benchmarks show that the proposed algorithm is efficient to obtain floorplans, with decreasing the average and the peak temperature.     

超磁致伸缩执行器磁滞模型的参数辨识

准确辨识磁滞模型参数是保证超磁致伸缩执行器位移控制精度的关键,而单一算法难以实现对超磁致非线性模型参数的精确辨识。该文提出了一种新型混合优化策略,即改进的遗传退火算法,并将其应用于对超磁致伸缩执行器位移磁滞模型参数的辨识。该算法兼顾了遗传算法和模拟退火算法的优点,同时还引入了机器学习原理,将模拟退火算法作为遗传算法中的种群变异算子,并将模拟退火算法中的抽样过程与遗传算法相结合。此算法不仅充分发挥了遗传算法并行搜索能力强的特点,且增强和改进了遗传算法的进化能力,同时提高了系统的收敛性和收敛速度,避免最优解的丢失。通过仿真和试验研究表明,该算法相对于遗传算法有更高的精度,可有效精确辨识超磁致伸缩执行器磁滞模型的参数。     

A hybrid elephant herding optimization and cultural algorithm for energy‐balanced cluster head selection scheme to extend the lifetime in WSNs

Clustering‐based optimal cluster head selection in wireless sensor networks (WSNs) is considered as the efficient technique essential for improving the network lifetime. But enforcing optimal cluster head selection based on energy stabilization, reduced delay, and minimized distance between sensor nodes always remain a crucial challenge for prolonging the network lifetime in WSNs. In this paper, a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO‐CA‐OCHS) scheme is proposed to extend the lifetime. This proposed HEHO‐CA‐OCHS scheme utilizes the merits of belief space framed by the cultural algorithm for defining a separating operator that is potent in constructing new local optimal solutions in the search space. Further, the inclusion of belief space aids in maintaining the balance between an optimal exploitation and exploration process with enhanced search capabilities under optimal cluster head selection. This proposed HEHO‐CA‐OCHS scheme improves the characteristic properties of the algorithm by incorporating separating and clan updating operators for effective selection of cluster head with the view to increase the lifetime of the network. The simulation results of the proposed HEHO‐CA‐OCHS scheme were estimated to be superior in percentage of alive nodes by 11.21%, percentage of dead nodes by 13.84%, residual energy by 16.38%, throughput by 13.94%, and network lifetime by 19.42% compared to the benchmarked cluster head selection schemes.     

一种基于混合智能算法的协作中继选择新方法

针对多用户多中继场景下协作通信系统的中继选择问题,提出了一种基于混合智能算法的协作中继选择新方法。不同于现有的为每个源节点分配一个中继节点的中继选择方法,新方法建立了为每个源节点分配一个或多个中继节点的优化模型,以最大化多用户多中继协作系统的最小接收信噪比为优化目标,采用结合了模拟退火与遗传算法的混合智能算法来搜寻中继选择问题的最优解。仿真结果表明,所提方法可显著提高目的端的接收信噪比,且算法具有较强的全局搜索和快速寻优能力。     

Optimal radio channel assignment through a new binary dynamic simulated annealing algorithm

The radio channel assignment problem (CAP) is classified as an NP-complete binary optimization problem, which creates the need for faster, yet optimal optimization algorithms to reduce the time of computation when solving such a complex problem. Simulated annealing (SA), a powerful optimal combinatorial search algorithm, was found to be very suitable for CAP. This paper extends the standard capabilities of SA and proposes a new CAP-oriented, quicker binary SA, the binary dynamic SA (BDSA) algorithm, as part of a newly proposed radio channel assignment approach. Simulation results proved that the proposed BDSA has very fast convergence as a stand-alone algorithm and even faster convergence with the newly proposed radio channel assignment approach. © 1998 John Wiley & Sons, Ltd.     

Design, modeling, and optimization of mechanically reconfigurableaperture antennas

The work in this study develops the framework for placement and actuation of novel reconfigurable dual-offset contour beam reflector antennas (DCBRA). Toward that end, the methodology for the antennas' design is defined. In addition, two separate optimization problems are stated and solved: actuator position optimization and actuation value optimization. For the former, a method termed as greatest error suppression method is proposed where the position of each actuator is decided one by one after each evaluation of the error between the desired subreflector shape and the actual subreflector shape. For the second problem, a mathematical analysis shows that there exists only one optimal configuration. Two optimization techniques are used for the second problem: the simulated annealing algorithm and a simple univariate optimization technique. The univariate technique always generates the same optimal configuration for different initial configurations and it gives the low bound in the evaluation of the error. The simulated annealing algorithm is a stochastic technique used to search for global optimum point. Finally, as an example the results of the proposed optimization techniques are presented for the generation of a subreflector shape corresponding to the geographical outline of Brazil     

改进的量子遗传算法及其在WMSN覆盖优化中的应用

对量子遗传算法进行了研究。量子遗传算法只使用一个最优染色体来指导种群的进化,极易陷入局部最优,本文对此进行了改进,提出使用多个精英染色体来指导整个种群的进化。讨论了精英染色体的产生、维护与作用,并在此基础之上提出了一种基于精英组的量子遗传算法(elite groupbased quantum genetic algorithm,EQGA)。最后,将EQGA应用到无线多媒体传感器网络的覆盖优化问题中。对比测试表明,EQGA求出的解比遗传算法和量子遗传算法求出的解都要好。     

基于模糊C均值聚类的锦标赛选择机制与多目标优化研究

本文提出了一种用于多目标优化的进化算法--基于模糊C均值聚类的进化算法（A Fuzzy C-Means Clustering Based Evolutionary Algorithm,FCEA）.在算法的迭代过程中,先利用模糊C均值聚类算法寻找种群的分布结构,通过对每一代种群进行模糊划分,获得每个个体隶属于每一类的隶属度,然后本文设计了一种基于隶属度的锦标赛选择算子,用于从整个种群中选择相似个体进行重组,引导算法进行搜索.实验结果表明,基于隶属度的锦标赛选择算子的应用能够提升算法的性能,与MOEA/D-DE、NSGAⅡ、SPEA2、SMS-EMOA等先进的优化算法进行比较的结果表明,FCEA在求解具有复杂Pareto前沿的多目标优化问题（GLT系列）时具有一定的竞争力.