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.”.     

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.     

Processing Optimal Sequenced Route Queries Using Voronoi Diagrams

The Optimal Sequenced Route (OSR) query strives to find a route of minimum length starting from a given source location and passing through a number of typed locations in a specific sequence imposed on the types of the locations. In this paper, we propose a pre-computation approach to OSR query in both vector and metric spaces. We exploit the geometric properties of the solution space and theoretically prove its relation to additively weighted Voronoi diagrams. Our approach recursively accesses these diagrams to incrementally build the OSR. Introducing the analogous diagrams for the space of road networks, we show that our approach is also efficiently applicable to this metric space. Our experimental results verify that our pre-computation approach outperforms the previous index-based approaches in terms of query response time. This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC), IIS-0238560 (PECASE), IIS-0324955 (ITR), IIS-0534761, and unrestricted cash gifts from Google and Microsoft. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.

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Hydrological Drought Assessment in Northwestern Iran Based on Streamflow Drought Index (SDI)

Drought is a natural and worldwide phenomenon that occurs when water availability is significantly below normal levels during a significant period of time and cannot meet demand. This work focused on the hydrologic drought defined by the streamflow drought index (SDI) for overlapping periods of 3, 6, 9 and 12 months at 14 hydrometric stations in the northwest of Iran over the period 1975–2009. It was found that some of the streamflow volume series did not follow the normal distribution. The ability of the log-normal, exponential and uniform probability distributions was examined in order to choose the most suitable distribution, and the log-normal distribution was used to fit the long-term streamflow data. The results of the hydrological drought analysis based on the SDI showed that almost all the stations suffered from extreme droughts during the study period. Additionally, extreme droughts occurred most frequently in the last 12 years from 1997–1998 to 2008–2009.