基于克隆选择算法的MANET簇化

以权值簇化算法——WCA为基础,利用人工免疫系统中的克隆选择算法来提高移动Ad-hoc网络的簇化性能,从而实现用尽可能少的全局优化簇头来管理尽可能多的簇成员,以保证MANET拓扑结构的相对稳定和合理。仿真结果表明,利用克隆选择算法优化后的WCA在簇的数量等许多方面均优于WCA算法。     

基于克隆选择的移动Ad hoc网络簇化的优化方法

在权值簇化算法WCA的基础上,利用人工免疫系统中的克隆选择原理来提高移动Ad-hoc网络的簇化性能,从而实现用尽可能少的簇头来管理尽可能多的簇成员,以保证网络拓扑结构的相对稳定。仿真结果表明,利用克隆选择原理优化的方法在簇头的数量、更新次数等多方面均优于WCA算法。     

EWCA簇算法的权重研究

对大规模Ad Hoc网络采用分层管理已经成为一种趋势,而分簇算法是关键.EWCA权值分簇算法在保留WCA优势的基础上,弥补WCA算法存在的缺陷,寻找出更适合的系统描述参数和开销更小的成簇步骤.提出一种算法,使用对比权重、效用权重、期望权重对EWCA各权值因素进行计算、设置和调整,以适应多种环境,也使得基于权值的EWCA分簇算法权重计算有据可依.为此,综合考虑了网络结点复杂的外部环境,在挖掘网络状态影响因素的基础上,深入分析了EWCA算法的权值计算公式因子及其相互关系,从而保证了权重确定的合理性和有效性.     

稳定且负载均衡的移动AdHoc网络加权分簇算法

针对移动AdHoc网络中传统加权分簇算法(WCA)的缺陷,提出一种稳定且负载均衡的改进型WCA(SLB-WCA).在节点组合权值计算中,增加了考虑节点的稳定性,并提出一种"相对典型节点度"代替传统WCA中的单纯节点度因素,同时根据各个节点的节点度制定本地簇大小约束,代替传统算法中的全局约束.SLB-WCA能够更加合理的计算节点权值和控制簇大小,均衡网络负载.通过实验与传统WCA相比,SLB-WCA形成的簇数目较少且具有良好的覆盖率,提高了网络的生命周期.     

基于簇头选择的移动传感网拓扑控制算法研究

针对移动传感网节点的移动性、能量有限性、动态变化性特点,提出了一种移动传感网分簇拓扑控制算法NACA.NACA算法吸收最小ID算法简便的优点,将其改进,提出新概念响应率,同时考虑了能量有限、移动速度和邻居节点数目等因素.通过实例分析,将NACA算法和WCA算法、HD算法进行比较,分析表明该算法初次收敛快,能够使得移动传...     

基于ARIMA模型的Ad-hoc网络节点位置预测加权分簇算法

在加权分簇算法(WCA)中引入预测机制,即在算法的路由维护阶段嵌入时间序列模型(ARIMA),用以预测网络节点的地理位置。利用ARIMA模型实时预测出节点下一时刻的地理位置,并以此计算出节点的累计保持时间预测值。将通过预测得到的累计保持时间值与时间预警阈值进行比较,在簇结构即将不稳定时,即在链路断开之前,提前启动预修复过程,寻找新的路由,降低网络拓扑动态变化的影响,维护簇结构的稳定。仿真结果表明,相对于LO-WID以及没有加入预测机制的RLWCA,ARP-LWCA算法大幅度提高了网络的分组投递率,降低了网络的归一化开销,并且使得路由中断次数有了明显减少,改善了网络的整体性能。     

一种应用于无线传感器网络的层次型拓扑结构生成算法

在无线传感器网络中,层次型的拓扑结构将整个网络划分为不同的簇,并通过一定机制选择簇头来负责数据转发和融合。本文在LEACH等现有层次型拓扑控制算法的基础上,借鉴ad hoc网络层次拓扑生成算法WCA的设计原理,提出一种应用于无线传感器网络的新型层次型拓扑结构生成算法(HTGA)。该算法综合考虑节点的能量和位置状况,为每个节点定义不同的权值,从中选出性能优越的节点担任簇首,同时通过设置节点度参数来确保最优的拓扑结构。仿真实验结果显示,新算法在降低能耗、延长网络生存时间以及保证监测覆盖度等方面比LEACH具有更加优良的性能。     

一种适合移动自组网的分簇算法

分级结构的Ad hoc网络可以减少路由开销,满足网络规模扩充的需要。提出了一种基于最小ID分簇算法的改进算法,根据节点移动性和电量消耗重新分配ID,然后利用分簇快速、操作简单、效率高的最小ID分簇算法进行重新分簇,仿真实验显示与传统最小ID分簇算法（LID）、最大节点度分簇算法（HD）和基于权值的分簇算法（WCA）相比,算法能使网内节点电量消耗更平衡,簇结构更稳定,能够延长网络的服务时间。     

WSN中一种基于能量的层次型拓扑生成算法

通过考虑无线传感器网络节点的能量问题确定了单层拓扑结构中簇头节点的最优个数,结合WCA算法提出了一种基于能量的无线传感器网络的层次型拓扑结构生成算法,并评估了该算法的各项性能指标。经过算法复杂度分析得出该算法的时间复杂度和网络节点的个数相关,适合生成中小型规模的网络。仿真结果表明,使用该算法可以生成具有最优簇头个数的网络拓扑结构,能大大节省网络节点能量的消耗,且延长了网络的生存周期。     

基于簇的动态源路由协议研究

AdHoc网络是一种自组织的、由移动节点组成的、动态变化的多跳无线网络。该文首先对簇进行了介绍，并提出CD-SR是基于WCA的DSR路由协议；然后讲解了CDSR的主要设计思想：路由发现是在簇头之间进行的，簇间的通信需要借助于簇头（不存在网关）；同时用GloMoSim模拟仿真工具，对CDSR与DSR的性能进行了分析和评价，并且得出CDSR协议较DSR协议具有高的投递率和吞吐率、低的延迟及较好的扩展性，更适合大规模自组无线网络；最后讨论了它的缺点。     

Modified water cycle algorithm for optimal direction overcurrent relays coordination

The optimization model of Directional Over Current Relays (DOCRs) coordination is considered non-linear optimization problem with a large number of operating constraints. This paper proposes a modified version for Water Cycle Algorithm (WCA), referred to as MWCA to effectively solve the optimal coordination problem of DOCRs. The main goal is to minimize the summation of operating times of all relays when they act as primary protective devices. The operating time of a relay depends on time dial setting and pickup current setting or plug setting, which they are considered as decision variables. In the proposed technique, the search space has been reduced by increasing the C-value of traditional WCA, which effects on the balance between explorative and exploitative phases, gradually during the iterative process in order to find the global minimum. The performance of proposed algorithm is assessed using standard test systems; 8-bus, 9-bus, 15-bus, and 30-bus. The obtained results by the proposed algorithm are compared with those obtained by other well-known optimization techniques. In addition, the proposed algorithm has been validated using benchmark DIgSILENT PowerFactory. The results show the effectiveness and superiority of the proposed algorithm to solve DOCRs coordination problem, compared with traditional WCA and other optimization techniques.     

An efficient chaotic water cycle algorithm for optimization tasks

Water cycle algorithm (WCA) is a new population-based meta-heuristic technique. It is originally inspired by idealized hydrological cycle observed in natural environment. The conventional WCA is capable to demonstrate a superior performance compared to other well-established techniques in solving constrained and also unconstrained problems. Similar to other meta-heuristics, premature convergence to local optima may still be happened in dealing with some specific optimization tasks. Similar to chaos in real water cycle behavior, this article incorporates chaotic patterns into stochastic processes of WCA to improve the performance of conventional algorithm and to mitigate its premature convergence problem. First, different chaotic signal functions along with various chaotic-enhanced WCA strategies (totally 39 meta-heuristics) are implemented, and the best signal is preferred as the most appropriate chaotic technique for modification of WCA. Second, the chaotic algorithm is employed to tackle various benchmark problems published in the specialized literature and also training of neural networks. The comparative statistical results of new technique vividly demonstrate that premature convergence problem is relieved significantly. Chaotic WCA with sinusoidal map and chaotic-enhanced operators not only can exploit high-quality solutions efficiently but can outperform WCA optimizer and other investigated algorithms.

     

Hierarchical Learning Water Cycle Algorithm

In order to improve the global searching ability of Water Cycle Algorithm (WCA), the hierarchical learning concept is introduced and the Hierarchical Learning WCA (HLWCA) is proposed in this paper. The underlying idea of HLWCA is to divide the solutions into collections and give these collections with hierarchy differences. One of the collections has a higher hierarchy than others and utilizes an exploration-inclined updating mechanism. The solutions in this high hierarchy collection are the exemplars of other collections. The other collections are sorted according to the exemplars’ function value and the solutions in these collections actively choose whether to follow their own exemplar or not. Through different updating mechanisms of collections, the global searching ability is improved while the fast convergence and strong local search ability of WCA are retained. The proposed HLWCA is firstly experimented on IEEE CEC 2017 benchmark suite to testify its performance on complex numerical optimization tasks. Then, it is tested on four practical design benchmark problems to verify its ability of solving real-world problems. The experimental results illustrate the efficiency of the proposed algorithm.     

Gradient-based Water Cycle Algorithm with evaporation rate applied to chaos suppression

Water Cycle Algorithm (WCA) is a nature-inspired population-based metaheuristic algorithm, which has been successfully applied to solve a wide range of benchmarks and real-world optimization problems. In this paper, an extended version of WCA, namely Gradient-based Water Cycle Algorithm (GWCA) with evaporation rate, is introduced to enhance the performance of the standard WCA by incorporating a local optimization operator so-called gradient-based approach. The idea of GWCA is underlined using the concept of moving (flowing) individuals along the steepest direction slope under a certain criterion. In order to demonstrate parameters influence on the performance of GWCA, an extensive sensitivity analysis is also carried out. To verify the performance of the GWCA, twelve well-known benchmark functions are adopted from the literature in the experiments. Both value-based and ranked-based methods are conducted to compare the performance of reported algorithms on the whole test suite. To this reason, the mean best and standard deviation of the results are provided and the Friedman test is utilized to determine average ranking of the algorithms based on their performances in each experiment. Corresponding results indicate that the proposed GWCA has outstanding performance in comparison with some state-of-art optimization algorithms. Finally, the chaos suppression problem using backstepping control as a real case study was adopted to confirm the efficiency of GWCA. The experimental results demonstrate the feasibility and efficiency of the proposed GWCA.     

Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems

This paper presents a modified version of the water cycle algorithm (WCA). The fundamental concepts and ideas which underlie the WCA are inspired based on the observation of water cycle process and how rivers and streams flow to the sea. New concept of evaporation rate for different rivers and streams is defined so called evaporation rate based WCA (ER-WCA), which offers improvement in search. Furthermore, the evaporation condition is also applied for streams that directly flow to sea based on the new approach. The ER-WCA shows a better balance between exploration and exploitation phases compared to the standard WCA. It is shown that the ER-WCA offers high potential in finding all global optima of multimodal and benchmark functions. The WCA and ER-WCA are tested using several multimodal benchmark functions and the obtained optimization results show that in most cases the ER-WCA converges to the global solution faster and offers more accurate results than the WCA and other considered optimizers. Based on the performance of ER-WCA on a number of well-known benchmark functions, the efficiency of the proposed method with respect to the number of function evaluations (computational effort) and accuracy of function value are represented.