Genetic-based minimum classification error mapping for accurate identifying Peer-to-Peer applications in the internet traffic

In this paper, we propose a hybrid approach using genetic algorithm and neural networks to classify Peer-to-Peer (P2P) traffic in IP networks. We first compute the minimum classification error (MCE) matrix using genetic algorithm. The MCE matrix is then used during the pre-processing step to map the original dataset into a new space. The mapped data set is then fed to three different classifiers: distance-based, K-Nearest Neighbors, and neural networks classifiers. We measure three different indexes, namely mutual information, Dunn, and SD to evaluate the extent of separation of the data points before and after mapping is performed. The experimental results demonstrate that with the proposed mapping scheme we achieve, on average, 8% higher accuracy in classification of the P2P traffic compare to the previous solutions. Moreover, the genetic-based MCE matrix increases the classification accuracy more than what the basic MCE does.     

Heuristics for the variable sized bin-packing problem

We investigate the one-dimensional variable-sized bin-packing problem. This problem requires packing a set of items into a minimum-cost set of bins of unequal sizes and costs. Six optimization-based heuristics for this problem are presented and compared. We analyze their empirical performance on a large set of randomly generated test instances with up to 2000 items and seven bin types. The first contribution of this paper is to provide evidence that a set covering heuristic proves to be highly effective and capable of delivering very-high quality solutions within short CPU times. In addition, we found that a simple subset-sum problem-based heuristic consistently outperforms heuristics from the literature while requiring extremely short CPU times.     

Distributed topology control algorithm based on one- and two-hop neighbors’ information for ad hoc networks

In this paper, we present a topology control algorithm for ad hoc networks. By considering the weight of the links, each node orders its one-hop neighbors in an ordered list and then the ordered lists are exchanged between the neighbors. This information enables the nodes to compute their transmission radius on the basis of its one- and two-hop neighbors’ information. We demonstrate that compared to the best known algorithms, the degree and transmission radius of the nodes in the topology produced by the proposed algorithm are smaller. In addition to Euclidean graphs, the algorithm works correctly on general weighted graphs. Also an extension of our algorithm is proposed which adapts its topology to network changes. Finally, we use the four metrics, node degree, transmission radius, the power stretch factor and, packet loss ratio to measure the performance improvements of the presented algorithms through simulations.     

A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.     

Building extraction from high-resolution satellite images in urban areas: recent methods and strategies against significant challenges

Building extraction from high-resolution satellite images (HRSI) in urban areas is an intricate problem. Recent studies proposed different methods during 2005–2015. However, in HRSI, they have not investigated the effects of challenges altogether. This paper studies the effects of non-building features which are the main drawbacks in building extraction. To overcome each challenge, it reviews recent strategies between 2005 and 2015. The pros and cons of each strategy are discussed, and proper strategies are combined to generate hybrid methods. Lower cost and fewer strategies are efficient attributes to recognize the best hybrid methods. Hybrid methods can be useful for different case studies in the future.     

Slippage control in soft finger grasping and manipulation

Handling objects with robotic soft fingers without considering the odds of slippage are not realistic. Grasping and manipulation algorithms have to be tested under such conditions for evaluating their robustness. In this paper, a dynamic analysis of rigid object manipulation with slippage control is studied using a two-link finger with soft hemispherical tip. Dependency on contact forces applied by a soft finger while grasping a rigid object is examined experimentally. A power-law model combined with a linear viscous damper is used to model the elastic behavior and damping effect of the soft tip, respectively. In order to obtain precise dynamic equations governing the system, two second-order differential equations with variable coefficients have been designed to describe the different possible states of the contact forces accordingly. A controller is designed based on the rigid fingertip model using the concept of feedback linearization for each phase of the system dynamics. Numerical simulations are used to evaluate the performance of the controller. The results reveal that the designed controller shows acceptable performance for both soft and rigid finger manipulation in reducing and canceling slippage. Furthermore, simulations indicate that the applied force in the soft finger manipulation is considerably less than the rigid “one.”.     

Design of sliding mode and model reference adaptive control strategies for multivariable tape transport mechanism: a performance comparison

This paper presents sliding mode control and model reference adaptive control strategies for the tape transport mechanism. A nonlinear multivariable MIMO model of the process, consisting of take-up and supply reel servos for tape tension control and capstan servo for speed control is considered. The sliding mode control is applied for the nonlinear dynamic model of the process, while the model reference adaptive control deals with the linearized one. Moreover, in order to associate with the realistic model of system, design of controllers is accomplished with respect to parametric uncertainties. It is shown that both control strategies can guarantee asymptotic stability of the closed-loop system and tracking of desired outputs with the appropriate pace in the presence of uncertainties. Finally, simulation results demonstrate the effectiveness of the proposed controllers.     

Commitments and interaction norms in organisations

In an organisational setting such as an online marketplace, an entity called the ‘organisation’ or ‘institution’ defines interaction protocols, monitors agent interaction, and intervenes to enforce the interaction protocols. The organisation might be a software system that thus regulates the marketplace, for example. In this article we abstract over application-specific protocols and consider commitment lifecycles as generic interaction protocols. We model interaction protocols by explicitly-represented norms, such that we can operationalise the enforcement of protocols by means of norm enforcement, and we can analyse the protocols by a logical analysis of the norms. We adopt insights and methods from commitment-based approaches to agent interaction as well as from norm-based approaches to agent behaviour governance. First, we show how to use explicitly-represented norms to model commitment dynamics (lifecycles). Second, we introduce an operational semantics to operationalise norm enforcement. Third, we show how to logically analyse interaction protocols by means of commitment dynamics and norm enforcement. The model, semantics, and analysis are illustrated by a running example from a vehicle insurance domain.     

A high-order non-oscillatory central scheme with non-staggered grids for hyperbolic conservation laws

In this work, we present a scheme which is based on non-staggered grids. This scheme is a new family of non-staggered central schemes for hyperbolic conservation laws. Motivation of this work is a staggered central scheme recently introduced by A.A.I. Peer et al. [A new fourth-order non-oscillatory central scheme for hyperbolic conservation laws, Appl. Numer. Math. 58 (2008) 674–688]. The most important properties of the technique developed in the current paper are simplicity, high-resolution and avoiding the use of staggered grids and hence is simpler to implement in frameworks which involve complex geometries and boundary conditions. Numerical implementation of the new scheme is carried out on the scalar conservation laws with linear, non-linear flux and systems of hyperbolic conservation laws. The numerical results confirm the expected accuracy and high-resolution properties of the scheme.     

QoS-based cooperative algorithm for integral multi-commodity flow problem

This paper treats with integral multi-commodity flow through a network. To enhance the Quality of Service (QoS) for channels, it is necessary to minimize delay and congestion. Decreasing the end-to-end delay and consumption of bandwidth across channels are dependent and may be considered in very complex mathematical equations. To capture with this problem, a multi-commodity flow model is introduced whose targets are minimizing delay and congestion in one model. The flow through the network such as packets, also needs to get integral values. A model covering these concepts, is NP-hard while it is very important to find transmission strategies in real-time. For this aim, we extend a cooperative algorithm including traditional mathematical programming such as path enumeration and a meta-heuristic algorithm such as genetic algorithm. To find integral solution satisfying demands of nodes, we generalize a hybrid genetic algorithm to assign the integral commodities where they are needed. In this hybrid algorithm, we use feasible encoding and try to keep feasibility of chromosomes over iterations. By considering some random networks, we show that the proposed algorithm yields reasonable results in a few number of iterations. Also, because this algorithm can be applied in a wide range of objective functions in terms of delay and congestion, it is possible to find some routs for each commodity with high QoS. Due to these outcomes, the presented model and algorithm can be utilized in a variety of application in computer networks and transportation systems to decrease the congestion and increase the usage of channels.