Enhanced ants system and its application

NP-hard combinational optimization problem is not solved very well until now. One enhanced ants system based on ants system is advanced after analysis of the deficiencies of existing ants systems. Some improvements are made in state transfer rule and local modification rule. Furthermore, the enhanced ants system can solve NP-hard combinational optimization problem with restraints and condition path. The successful application of TSP problem and transportation net problem indicates that the proposed system has stronger function and higher efficiency than the original system.

Micro-Arthropods in the Surroundings of Nests of Messor arenarius Ants in the Negev Desert in Israel

The present research investigated a segment of the micro-arthropod populations residing within nests of Messor arenarius ants in the Negev Desert of Israel. The total frequencies of micro-arthropods in the chaff of those ants’ nests were found to be higher than in the surrounding soil of the same nests. Acari (mites) were observed to be more abundant during the spring season, whereas their presence decreased during the summer months. Springtails (Collembola) were found to follow the Acari pattern, commonly found within the nests of those ants during spring but were absent during summer. Psocoptera order inhabiting soil habitats were infrequently encountered during spring, but their prevalence increased significantly during summer, particularly within the chaff of the ants’ nests, suggesting that chaff is their primary food source in the Negev Desert. Our research suggests that shifts in seasonality have important consequences on the distribution of soil invertebrate communities with implications on nutrient cycling.

A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation performance of MCT.To solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm design.Firstly,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated RSP.The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective.Secondly,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection strategy.On the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible solutions.On the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution.To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is conducted.Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.

主动网络工具集ANTS的主动包格式分析

目前国内发表的一些论文上关于ANTS的主动包格式介绍的不是很清楚、甚至是有些出入 ,而国外的有关文献中也没有相关内容的详细介绍。本文介绍了通过在搭建的ANTS主动网络平台上运行主动Ping命令 ,而后捕获该主动Ping数据包进行分析的方法 ,得出了ANTS的主动包 (封装体 )详细格式。

基于ANTS的主动路由支撑框架的设计与实现

在主动网工具包ANTS的基础上实现了一个QCAR原型系统,通过拓展ANTS的框架结构使其支持主动路由服务和沿途的QoS保障,并在该框架的基础上给出了一个主动路由的应用TraceRtApplication,通过具体的主动路由示例程序的实施,说明了如何在QCAR系统上开发新的应用。该文的研究和实现证明,与传统网络相比,在主动网基础上通过向网络中注入用户定制的、面向应用的路由需求代码,可以方便地实现主动路由定制和QoS保障。

主动网络执行环境ANTS的扩展设计及应用研究

由于国内在主动网络操作系统和执行环境方面的研究的薄弱,主动网络的应用研究受到了一定影响。基于该现状的考虑,分析了ANTS主动网络执行环境的优势与不足,针对其不足设计开发了一种构建灵活、能满足多种移动代码加载机制的执行环境EANTS,最后利用该平台进行了主动网络的管理应用研究,并实现了主动网络管理原型系统,研究成果为进一步进行主动网络应用研究提供了一种可选择的平台。

基于AntStream用户聚类的协同过滤推荐系统

系统规模的逐步扩大和用户兴趣的发展变化给传统协同过滤推荐系统带来了实时性减弱和准确性降低的问题。基于K-Means用户聚类的协同过滤技术虽然能在一定程度上解决这两个问题,算法本身却带有局部最优的缺陷。在保证实时性的前提下,为克服K-Means算法的缺陷,提出使用AntClass蚁群算法对用户聚类。同时提出将用户评分看作数据流,利用金字塔时间框架预处理数据,从而体现用户兴趣随时间的变化。于是,将AntClass蚁群算法和利用金字塔时间框架预处理过的数据流相结合,最终形成文中的AntStream算法。实验表明,AntStream算法不仅改善了传统协同过滤推荐系统的实时性问题,而且更大程度提高了推荐质量。

鲤鱼(Cyprinuscarpio,Cyprinidae)ants基因及其对鳞片发育的影响

ants(adenine nucleotide translocators),又称为ATP/ADP载体,利用Genefishing差异筛选技术对普通鲤鱼和德国镜鲤的皮肤转录产物进行筛选,得到了其中一个ant基因的部分片段,利用RT-PCR技术对皮肤中的ants基因进行钓取,得到6种不同的CDS序列。利用CLUSTALW对这6条翻译后的序列进行比对,发现这6条序列编码4种不同的氨基酸序列,表明ants基因至少有6种不同的转录产物(isoforms),但是在蛋白质水平上可能存在4种不同的翻译产物。利用NCBI上的blast对氨基酸序列进行同源性检索发现这4个蛋白产物与斑马鱼ant2最为相似,其相似度分别达到97%,97%,96%,98%。对处于不同分类地位的物种的ants基因与鲤鱼的4个ants表达产物进行进化树分析发现,这4个蛋白产物在93/100再现性水平上明显聚类为ant2,说明皮肤中表达的这些ants基因很可能是ant2。原位杂交结果显示,ant2在受精后4.5 h和6 h不表达,从受精后10 h开始在外包处强烈特异表达,在受精后12 h的胚胎中很弱表达,在受精后24 h的体节中特异强烈表达,在受精后48 h胚胎无表达,但是在着生鳞片的部位特异强烈表达,而在皮肤的其他部位则表达较弱或者不表达。以上的结果表明,ant2基因可能是通过不同的转录产物在精细调控鳞片的发育。

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。

ORDER-PICKING OPTIMIZATION FOR AUTOMATED PICKING SYSTEM WITH PARALLEL DISPENSERS

Based on the characteristics of parallel dispensers in automated picking system, an order-picking optimization problem is presented. Firstly, the working principle of parallel dispensers is introduced, which implies the time cost of picking each order is influenced by the order-picking sequence. So the order-picking optimization problem can be classified as a dynamic traveling salesman problem （TSP）. Then a mathematical model of the problem is established and an improved max-min ant system （MMAS） is adopted to solve the model. The improvement includes two aspects. One is that the initial assignment of ants depends on a probabilistic formula instead of a random deployment; the other is that the heuristic factor is expressed by the extra picking time of each order instead of the total. At last, an actual simulation is made on an automated picking system with parallel dispensers. The simulation results proved the optimization value and the validity of improvement on MMAS.

Satellite Constellation Design with Adaptively Continuous Ant System Algorithm

The ant system algorithm （ASA） has proved to be a novel meta-heuristic algorithm to solve many multivariable problems. In this paper, the earth coverage of satellite constellation is analyzed and a n ＋ 1^ -fold coverage rate is put forward to evaluate the coverage performance of a satellite constellation. An optimization model of constellation parameters is established on the basis of the coverage performance. As a newly developed method, ASA can be applied to optimize the constellation parameters. In order to improve the ASA, a rule for adaptive number of ants is proposed, by which the search range is obviously enlarged and the convergence speed increased. Simulation results have shown that the ASA is more quick and efficient than other methodV211.71s.

Design of PID controller with incomplete derivation based on ant system algorithm

A new and intelligent design method for PID controller with incomplete derivation is proposed based on the ant system algorithm ( ASA) . For a given control system with this kind of PID controller, a group of optimal PID controller parameters K p * , T i * , and T d * can be obtained by taking the overshoot, settling time, and steady-state error of the system's unit step response as the performance indexes and by use of our improved ant system algorithm. K p * , T i * , and T d * can be used in real-time control. This kind of controller is called the ASA-PID controller with incomplete derivation. To verify the performance of the ASA-PID controller, three different typical transfer functions were tested, and three existing typical tuning methods of PID controller parameters, including the Ziegler-Nichols method (ZN),the genetic algorithm (GA),and the simulated annealing (SA), were adopted for comparison. The simulation results showed that the ASA-PID controller can be used to control different objects and has better performance compared with the ZN-PID and GA-PID controllers, and comparable performance compared with the SA-PID controller.

Multi-type ant system algorithm for the time dependent vehicle routing problem with time windows

The time dependent vehicle routing problem with time windows（TDVRPTW） is considered. A multi-type ant system（MTAS） algorithm hybridized with the ant colony system（ACS）and the max-min ant system（MMAS） algorithms is proposed. This combination absorbs the merits of the two algorithms in solutions construction and optimization separately. In order to improve the efficiency of the insertion procedure, a nearest neighbor selection（NNS） mechanism, an insertion local search procedure and a local optimization procedure are specified in detail. And in order to find a balance between good scouting performance and fast convergence rate, an adaptive pheromone updating strategy is proposed in the MTAS. Computational results confirm the MTAS algorithm＇s good performance with all these strategies on classic vehicle routing problem with time windows（VRPTW） benchmark instances and the TDVRPTW instances, and some better results especially for the number of vehicles and travel times of the best solutions are obtained in comparison with the previous research.

Finding the Minimum Ratio Traveling Salesman Tour by Artificial Ants

Ants of artificial colony are able to generate good solutions to the famous traveling salesman problem (TSP). We propose an artificial ants algorithm for solving the minimum ratio TSP, which is more general than the standard TSP in combinatorial optimization area. In the minimum ratio TSP, another criterion concerning each edge is added, that is, the traveling salesman can have a benefit if he travels from one city to another. The objective is to minimize the ratio between total costs or distances and total benefits. The idea of this type of optimization is in some sense quite similar to that of traditional cost-benefit analysis in management science. Computational results substantiate the solution quality and efficiency of the algorithm.

Assembly Auxiliary System for Narrow Cabins of Spacecraft

Due to the narrow space and complex structure of spacecraft cabin, the existing asssembly systems can not well suit for the assembly process of cabin products. This paper aims to introduce an assembly auxiliary system for cabin products. A hierarchical-classification method is proposed to re-adjust the initial assembly relationship of cabin into a new hierarchical structure for efficient assembly planning. An improved ant colony algorithm based on three assembly principles is established for searching a optimizational assembly sequence of cabin parts. A mixed reality assembly environment is constructed with enhanced inforamtion to promote interaction efficiency of assembly training and guidance. Based on the machine vision technology, the inspection of left redundant objects and measurement of parts distance in inner cabin are efficiently performed. The proposed system has been applied to the assembly work of a spacecraft cabin with 107 parts, which includes cabin assembly planning, assembly training and assembly quality inspection. The application result indicates that the proposed system can be an effective assistant tool to cabin assembly works and provide an intuitive and real assembly experience for workers. This paper presents an assembly auxiliary system for spacecraft cabin products, which can provide technical support to the spacecraft cabin assembly industry.

Modeling river water quality parameters using modified adaptive neuro fuzzy inference system

Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO) and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.

A Survey on the Vehicle Routing Problem and Its Variants

In this paper, we have conducted a literature review on the recent developments and publications involving the vehicle routing problem and its variants, namely vehicle routing problem with time windows (VRPTW) and the capacitated vehicle routing problem (CVRP) and also their variants. The VRP is classified as an NP-hard problem. Hence, the use of exact optimization methods may be difficult to solve these problems in acceptable CPU times, when the problem involves real-world data sets that are very large. The vehicle routing problem comes under combinatorial problem. Hence, to get solutions in determining routes which are realistic and very close to the optimal solution, we use heuristics and meta-heuristics. In this paper we discuss the various exact methods and the heuristics and meta-heuristics used to solve the VRP and its variants.

An adaptive ant colony system algorithm for continuous-space optimization problems

Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates.Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.

A new algorithm of bearings-only multi-target tracking of bistatic system

Much research mainly focuses on the batch processing method （e.g. maximum likelihood method） when bearings-only multiple targets tracking of bistatic sonar system is considered. In this paper, the idea of recursive processing method is presented and employed, and corresponding data association algorithms, i.e. a multi-objective ant-colony-based optimization algorithm and an easy fast assignment algorithm are developed to solve the measurements-to-measurements and measurements-to-tracks data association problems of bistatic sonar system, respectively. Monte-Carlo simulations are induced to evaluate the effectiveness of the proposed methods.

Hopfield neural network based on ant system

Hopfield neural network is a single layer feedforward neural network. Hopfield network requires some control parameters to be carefully selected, else the network is apt to converge to local minimum. An ant system is a nature inspired meta heuristic algorithm. It has been applied to several combinatorial optimization problems such as Traveling Salesman Problem, Scheduling Problems, etc. This paper will show an ant system may be used in tuning the network control parameters by a group of cooperated ants. The major advantage of this network is to adjust the network parameters automatically, avoiding a blind search for the set of control parameters. This network was tested on two TSP problems, 5 cities and 10 cities. The results have shown an obvious improvement.