District Heating Network Design and Configuration Optimization with Genetic Algorithm
Directory of Open Access Journals (Sweden)
Hongwei Li
2013-12-01
Full Text Available In this paper, the configuration of a district heating network which connects from the heating plant to the end users is optimized. Each end user in the network represents a building block. The connections between the heat generation plant and the end users are represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding and is optimized in terms of the net present cost. The optimization results indicates that the optimal DH network configuration is determined by multiple factors such as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding the pressure and temperature limitation, as well as the corresponding network heat loss.
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.
Garro, Beatriz A; Vázquez, Roberto A
2015-01-01
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.
Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn.
Patra, Tarak K; Meenakshisundaram, Venkatesh; Hung, Jui-Hsiang; Simmons, David S
2017-02-13
Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting properties and performance beyond those previously achieved, machine learning approaches are frequently limited by two shortcomings. First, because they are intrinsically interpolative, they are better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require large pre-existing data sets, which are frequently unavailable and prohibitively expensive to produce. Here we describe a new strategy, the neural-network-biased genetic algorithm (NBGA), for combining genetic algorithms, machine learning, and high-throughput computation or experiment to discover materials with extremal properties in the absence of pre-existing data. Within this strategy, predictions from a progressively constructed artificial neural network are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct simulation or experiment. In effect, this strategy gives the evolutionary algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process. We test this algorithm against several standard optimization problems and polymer design problems and demonstrate that it matches and typically exceeds the efficiency and reproducibility of standard approaches including a direct-evaluation genetic algorithm and a neural-network-evaluated genetic algorithm. The success of this algorithm in a range of test problems indicates that the NBGA provides a robust strategy for employing informatics-accelerated high-throughput methods to accelerate materials design in the absence of pre-existing data.
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2013-01-01
and the pipe friction and heat loss formulations are non-linear. In order to find the optimal district heating network configuration, genetic algorithm which handles the mixed integer nonlinear programming problem is chosen. The network configuration is represented with binary and integer encoding...
Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design
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Z. H. Che
2014-01-01
Full Text Available In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA, Genetic Algorithm-Simulated Annealing (GA-SA, and Particle Swarm Optimization-Simulated Annealing (PSO-SA for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.
Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design
Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.
2014-01-01
In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057
Hybrid algorithms for fuzzy reverse supply chain network design.
Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua
2014-01-01
In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.
GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE
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Ashish Jain
2012-07-01
Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.
Kleinberg, Jon
2006-01-01
Algorithm Design introduces algorithms by looking at the real-world problems that motivate them. The book teaches students a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science.
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2011-01-01
the heating plant location is allowed to vary. The connection between the heat generation plant and the end users can be represented with mixed integer and the pipe friction and heat loss formulations are non-linear. In order to find the optimal DH distribution pipeline configuration, the genetic algorithm...... which handles the mixed integer nonlinear programming problem was chosen. The network configuration was represented through binary and integer encoding and was optimized in terms of the net present cost (NPC). The optimization results indicated that the optimal DH network configuration is determined...
Design of Supply Chain Networks with Supply Disruptions using Genetic Algorithm
Taha, Raghda; Abdallah, Khaled; Sadek, Yomma; El-Kharbotly, Amin; Afia, Nahid
2014-01-01
The design of supply chain networks subject to disruptions is tackled. A genetic algorithm with the objective of minimizing the design cost and regret cost is developed to achieve a reliable supply chain network. The improvement of supply chain network reliability is measured against the supply chain cost.
Algorithm Design for Assignment Frequency Channels for Services in CATV Network
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Jan Hlubik
2008-01-01
Full Text Available This article describes algorithm design for assignment frequency channels for services in CATV network. QAM modulated channels in designed system are dynamically switched and frequency adapted. On introduction, there is shortly described principle of providing analog video, digital video and data services in CATV network. Object of the article is focused on method of allocation individual TV and data channels on the frequency band. The conclusion compares advantages and disadvantages present and design system.
Improved Cost-Base Design of Water Distribution Networks using Genetic Algorithm
Moradzadeh Azar, Foad; Abghari, Hirad; Taghi Alami, Mohammad; Weijs, Steven
2010-05-01
Population growth and progressive extension of urbanization in different places of Iran cause an increasing demand for primary needs. The water, this vital liquid is the most important natural need for human life. Providing this natural need is requires the design and construction of water distribution networks, that incur enormous costs on the country's budget. Any reduction in these costs enable more people from society to access extreme profit least cost. Therefore, investment of Municipal councils need to maximize benefits or minimize expenditures. To achieve this purpose, the engineering design depends on the cost optimization techniques. This paper, presents optimization models based on genetic algorithm(GA) to find out the minimum design cost Mahabad City's (North West, Iran) water distribution network. By designing two models and comparing the resulting costs, the abilities of GA were determined. the GA based model could find optimum pipe diameters to reduce the design costs of network. Results show that the water distribution network design using Genetic Algorithm could lead to reduction of at least 7% in project costs in comparison to the classic model. Keywords: Genetic Algorithm, Optimum Design of Water Distribution Network, Mahabad City, Iran.
Evolving spiking neural networks: a novel growth algorithm exhibits unintelligent design
Schaffer, J. David
2015-06-01
Spiking neural networks (SNNs) have drawn considerable excitement because of their computational properties, believed to be superior to conventional von Neumann machines, and sharing properties with living brains. Yet progress building these systems has been limited because we lack a design methodology. We present a gene-driven network growth algorithm that enables a genetic algorithm (evolutionary computation) to generate and test SNNs. The genome for this algorithm grows O(n) where n is the number of neurons; n is also evolved. The genome not only specifies the network topology, but all its parameters as well. Experiments show the algorithm producing SNNs that effectively produce a robust spike bursting behavior given tonic inputs, an application suitable for central pattern generators. Even though evolution did not include perturbations of the input spike trains, the evolved networks showed remarkable robustness to such perturbations. In addition, the output spike patterns retain evidence of the specific perturbation of the inputs, a feature that could be exploited by network additions that could use this information for refined decision making if required. On a second task, a sequence detector, a discriminating design was found that might be considered an example of "unintelligent design"; extra non-functional neurons were included that, while inefficient, did not hamper its proper functioning.
Design Principles of Regulatory Networks: Searching for the Molecular Algorithms of the Cell
Lim, Wendell A.; Lee, Connie M.; Tang, Chao
2013-01-01
A challenge in biology is to understand how complex molecular networks in the cell execute sophisticated regulatory functions. Here we explore the idea that there are common and general principles that link network structures to biological functions, principles that constrain the design solutions that evolution can converge upon for accomplishing a given cellular task. We describe approaches for classifying networks based on abstract architectures and functions, rather than on the specific molecular components of the networks. For any common regulatory task, can we define the space of all possible molecular solutions? Such inverse approaches might ultimately allow the assembly of a design table of core molecular algorithms that could serve as a guide for building synthetic networks and modulating disease networks. PMID:23352241
Design principles of regulatory networks: searching for the molecular algorithms of the cell.
Lim, Wendell A; Lee, Connie M; Tang, Chao
2013-01-24
A challenge in biology is to understand how complex molecular networks in the cell execute sophisticated regulatory functions. Here we explore the idea that there are common and general principles that link network structures to biological functions, principles that constrain the design solutions that evolution can converge upon for accomplishing a given cellular task. We describe approaches for classifying networks based on abstract architectures and functions, rather than on the specific molecular components of the networks. For any common regulatory task, can we define the space of all possible molecular solutions? Such inverse approaches might ultimately allow the assembly of a design table of core molecular algorithms that could serve as a guide for building synthetic networks and modulating disease networks. Copyright © 2013 Elsevier Inc. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Souza, Bruno B.; Neto, Oriane M. [Universidade Federal de Minas Gerais - Department of Electrical Engineering (Brazil); Carrano, Eduardo G. [Centro Federal de Educacao Tecnologica de Minas Gerais - Department of Computer Engineering (Brazil); Takahashi, Ricardo H.C. [Universidade Federal de Minas Gerais - Department of Mathematics (Brazil)
2011-02-15
A recent paper, has proposed a methodology for taking into account uncertainties in the load evolution within the design of electric distribution networks. That paper has presented an immunological algorithm that is used for finding a set of solutions which are sub-optimal under the viewpoint of the ''mean scenario'' load conditions, and which are submitted to a sensitivity analysis for the load uncertainty. This paper presents a further development of the algorithm presented in, employing now a memetic algorithm (an algorithm endowed with local search operators) instead of the original immunological algorithm. The new algorithm is shown to present a better behavior, achieving a better set of candidate solutions, which dominate the solution set of the former algorithm. The solution set of the proposed algorithm is also stable, in the senses that: (i) the same set of solutions is found systematically; and (ii) the merit function values associated to those solutions vary smoothly from one solution to another one. It can be concluded that the design procedure proposed in should be performed preferentially with the algorithm proposed here. (author)
Watanabe, Shin; Takamatsu, Atsuko
2014-01-01
In this paper, we propose designing transportation network topology and traffic distribution under fluctuating conditions using a bio-inspired algorithm. The algorithm is inspired by the adaptive behavior observed in an amoeba-like organism, plasmodial slime mold, more formally known as plasmodium of Physarum plycephalum. This organism forms a transportation network to distribute its protoplasm, the fluidic contents of its cell, throughout its large cell body. In this process, the diameter of the transportation tubes adapts to the flux of the protoplasm. The Physarum algorithm, which mimics this adaptive behavior, has been widely applied to complex problems, such as maze solving and designing the topology of railroad grids, under static conditions. However, in most situations, environmental conditions fluctuate; for example, in power grids, the consumption of electric power shows daily, weekly, and annual periodicity depending on the lifestyles or the business needs of the individual consumers. This paper studies the design of network topology and traffic distribution with oscillatory input and output traffic flows. The network topology proposed by the Physarum algorithm is controlled by a parameter of the adaptation process of the tubes. We observe various rich topologies such as complete mesh, partial mesh, Y-shaped, and V-shaped networks depending on this adaptation parameter and evaluate them on the basis of three performance functions: loss, cost, and vulnerability. Our results indicate that consideration of the oscillatory conditions and the phase-lags in the multiple outputs of the network is important: The building and/or maintenance cost of the network can be reduced by introducing the oscillating condition, and when the phase-lag among the outputs is large, the transportation loss can also be reduced. We use stability analysis to reveal how the system exhibits various topologies depending on the parameter.
Directory of Open Access Journals (Sweden)
Shin Watanabe
Full Text Available In this paper, we propose designing transportation network topology and traffic distribution under fluctuating conditions using a bio-inspired algorithm. The algorithm is inspired by the adaptive behavior observed in an amoeba-like organism, plasmodial slime mold, more formally known as plasmodium of Physarum plycephalum. This organism forms a transportation network to distribute its protoplasm, the fluidic contents of its cell, throughout its large cell body. In this process, the diameter of the transportation tubes adapts to the flux of the protoplasm. The Physarum algorithm, which mimics this adaptive behavior, has been widely applied to complex problems, such as maze solving and designing the topology of railroad grids, under static conditions. However, in most situations, environmental conditions fluctuate; for example, in power grids, the consumption of electric power shows daily, weekly, and annual periodicity depending on the lifestyles or the business needs of the individual consumers. This paper studies the design of network topology and traffic distribution with oscillatory input and output traffic flows. The network topology proposed by the Physarum algorithm is controlled by a parameter of the adaptation process of the tubes. We observe various rich topologies such as complete mesh, partial mesh, Y-shaped, and V-shaped networks depending on this adaptation parameter and evaluate them on the basis of three performance functions: loss, cost, and vulnerability. Our results indicate that consideration of the oscillatory conditions and the phase-lags in the multiple outputs of the network is important: The building and/or maintenance cost of the network can be reduced by introducing the oscillating condition, and when the phase-lag among the outputs is large, the transportation loss can also be reduced. We use stability analysis to reveal how the system exhibits various topologies depending on the parameter.
Energy Technology Data Exchange (ETDEWEB)
Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)
1993-07-01
A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic
Li, Ning; Martínez, José-Fernán; Díaz, Vicente Hernández
2015-01-01
Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters’ dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively. PMID:26266412
The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic.
Li, Ning; Martínez, José-Fernán; Hernández Díaz, Vicente
2015-08-10
Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters' dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively.
Highway Passenger Transport Based Express Parcel Service Network Design: Model and Algorithm
Directory of Open Access Journals (Sweden)
Yuan Jiang
2017-01-01
Full Text Available Highway passenger transport based express parcel service (HPTB-EPS is an emerging business that uses unutilised room of coach trunk to ship parcels between major cities. While it is reaping more and more express market, the managers are facing difficult decisions to design the service network. This paper investigates the HPTB-EPS network design problem and analyses the time-space characteristics of such network. A mixed-integer programming model is formulated integrating the service decision, frequency, and network flow distribution. To solve the model, a decomposition-based heuristic algorithm is designed by decomposing the problem as three steps: construction of service network, service path selection, and distribution of network flow. Numerical experiment using real data from our partner company demonstrates the effectiveness of our model and algorithm. We found that our solution could reduce the total cost by up to 16.3% compared to the carrier’s solution. The sensitivity analysis demonstrates the robustness and flexibility of the solutions of the model.
DEFF Research Database (Denmark)
Bilardi, Gianfranco; Pietracaprina, Andrea; Pucci, Geppino
2016-01-01
A framework is proposed for the design and analysis of network-oblivious algorithms, namely algorithms that can run unchanged, yet efficiently, on a variety of machines characterized by different degrees of parallelism and communication capabilities. The framework prescribes that a network...... in the latter model implies optimality in the decomposable bulk synchronous parallel model, which is known to effectively describe a wide and significant class of parallel platforms. The proposed framework can be regarded as an attempt to port the notion of obliviousness, well established in the context...
An Iterated Local Search Algorithm for Multi-Period Water Distribution Network Design Optimization
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Annelies De Corte
2016-08-01
Full Text Available Water distribution networks consist of different components, such as reservoirs and pipes, and exist to provide users (households, agriculture, industry with high-quality water at adequate pressure and flow. Water distribution network design optimization aims to find optimal diameters for every pipe, chosen from a limited set of commercially available diameters. This combinatorial optimization problem has received a lot of attention over the past forty years. In this paper, the well-studied single-period problem is extended to a multi-period setting in which time varying demand patterns occur. Moreover, an additional constraint—which sets a maximum water velocity—is imposed. A metaheuristic technique called iterated local search is applied to tackle this challenging optimization problem. A full-factorial experiment is conducted to validate the added value of the algorithm components and to configure optimal parameter settings. The algorithm is tested on a broad range of 150 different (freely available test networks.
Directory of Open Access Journals (Sweden)
Jun Huang
2015-01-01
Full Text Available The design of wireless sensor networks (WSNs in the Internet of Things (IoT faces many new challenges that must be addressed through an optimization of multiple design objectives. Therefore, multiobjective optimization is an important research topic in this field. In this paper, we develop a new efficient multiobjective optimization algorithm based on the chaotic ant swarm (CAS. Unlike the ant colony optimization (ACO algorithm, CAS takes advantage of both the chaotic behavior of a single ant and the self-organization behavior of the ant colony. We first describe the CAS and its nonlinear dynamic model and then extend it to a multiobjective optimizer. Specifically, we first adopt the concepts of “nondominated sorting” and “crowding distance” to allow the algorithm to obtain the true or near optimum. Next, we redefine the rule of “neighbor” selection for each individual (ant to enable the algorithm to converge and to distribute the solutions evenly. Also, we collect the current best individuals within each generation and employ the “archive-based” approach to expedite the convergence of the algorithm. The numerical experiments show that the proposed algorithm outperforms two leading algorithms on most well-known test instances in terms of Generational Distance, Error Ratio, and Spacing.
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Daniel Antonio Molina
2015-09-01
Full Text Available In this paper we apply to solve the Radio Network Design problem (RND a series of the non-conventional genetic algorithms called Cross generational elitist selection Heterogeneous recombination Cataclysmic mutation (CHC. A set of genetic algorithms is used to perform a comparative performance of the proposed algorithms. An objective function based on signal coverage efficiency is used. Genetic variability of the population is used for both, as a parameter of convergence and detection of incest. Furthermore the variability of the best individual is proposed as a shaking mechanism. This allows generating dynamic populations according to the most promising solutions generating different search spaces. The results obtained by the proposed algorithms are satisfactory.
A Novel SWMM Based Algorithm Application to Storm Sewer Network Design
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Zhiyu Shao
2017-09-01
Full Text Available An automated algorithm based on the dynamic hydrological and hydraulic simulation modules in Storm Water Management Model (SWMM was developed to aid the design of storm sewer networks, provided that a layout is given. Numerical performance of the proposed algorithm was compared with the existing design methods with two application cases. The automated computation process of the sewer network design was divided into two stages and solved iteratively, determining pipe diameter and pipe slope, respectively. In the first stage, starting with a set of initial values including pipe diameter, pipe cover depth, and ground elevation at manholes, the iteration was carried out from the downstream to the upstream while the pipe slopes of the network were assumed to be fixed and the diameter of each pipe segment was calculated. In the second stage, pipe diameters calculated from the first stage were fixed and the pipe slopes were calculated successively from the downstream pipe segment to the upstream pipe segment. Every time the diameter or slope of a pipe segment was adjusted, the pipe flow rate, velocity, and flow depth were obtained by running SWMM hydrological and hydraulic simulation modules. The iteration terminated once the combination scheme of pipe diameters and slopes met the design ordinance which requires the pipe flows full under gravity in a design return period. A real urban sewer system in a hilly city and a benchmark sewer network from the literature were tested to validate the proposed automated algorithm, and good performance was shown. The automated design results explicitly show that the proposed storm sewer design approach leads to a quality solution with reduced computational effort.
A Genetic Algorithm for the Bi-Level Topological Design of Local Area Networks.
Directory of Open Access Journals (Sweden)
José-Fernando Camacho-Vallejo
Full Text Available Local access networks (LAN are commonly used as communication infrastructures which meet the demand of a set of users in the local environment. Usually these networks consist of several LAN segments connected by bridges. The topological LAN design bi-level problem consists on assigning users to clusters and the union of clusters by bridges in order to obtain a minimum response time network with minimum connection cost. Therefore, the decision of optimally assigning users to clusters will be made by the leader and the follower will make the decision of connecting all the clusters while forming a spanning tree. In this paper, we propose a genetic algorithm for solving the bi-level topological design of a Local Access Network. Our solution method considers the Stackelberg equilibrium to solve the bi-level problem. The Stackelberg-Genetic algorithm procedure deals with the fact that the follower's problem cannot be optimally solved in a straightforward manner. The computational results obtained from two different sets of instances show that the performance of the developed algorithm is efficient and that it is more suitable for solving the bi-level problem than a previous Nash-Genetic approach.
Designing a neural network for closed thermosyphon with nanofluid using a genetic algorithm
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H. Salehi
2011-03-01
Full Text Available Heat transfer of a silver/water nanofluid in a two-phase closed thermosyphon that is thermally enhanced by magnetic field has been predicted by an optimized artificial Neural Network. Artificial neural network is a technique with flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data. A multi-layer perception neural network was used to estimate the thermal efficiency and resistance of a thermosyphon during application of a magnetic field and using nanoparticles in the water as the working fluid. The magnetic field strength, volume fraction of nanofluid in water and inlet power were used as input parameters and the thermal efficiency and thermal resistance were used as output parameters. The results were compared with experimental data and it was found that the thermal efficiency and resistance estimated by the multi-layer perception neural network are accurate. The GA-ANN (Genetic Algorithm-Artificial Neural network predicts the thermosyphon behavior correctly within the given range of the training data. In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm, has been used to predict collection output of a closed thermosyphon.
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Ali Akbar Hasani
2016-11-01
Full Text Available In this paper, a comprehensive model is proposed to design a network for multi-period, multi-echelon, and multi-product inventory controlled the supply chain. Various marketing strategies and guerrilla marketing approaches are considered in the design process under the static competition condition. The goal of the proposed model is to efficiently respond to the customers’ demands in the presence of the pre-existing competitors and the price inelasticity of demands. The proposed optimization model considers multiple objectives that incorporate both market share and total profit of the considered supply chain network, simultaneously. To tackle the proposed multi-objective mixed-integer nonlinear programming model, an efficient hybrid meta-heuristic algorithm is developed that incorporates a Taguchi-based non-dominated sorting genetic algorithm-II and a particle swarm optimization. A variable neighborhood decomposition search is applied to enhance a local search process of the proposed hybrid solution algorithm. Computational results illustrate that the proposed model and solution algorithm are notably efficient in dealing with the competitive pressure by adopting the proper marketing strategies.
The Bilevel Design Problem for Communication Networks on Trains: Model, Algorithm, and Verification
Directory of Open Access Journals (Sweden)
Yin Tian
2014-01-01
Full Text Available This paper proposes a novel method to solve the problem of train communication network design. Firstly, we put forward a general description of such problem. Then, taking advantage of the bilevel programming theory, we created the cost-reliability-delay model (CRD model that consisted of two parts: the physical topology part aimed at obtaining the networks with the maximum reliability under constrained cost, while the logical topology part focused on the communication paths yielding minimum delay based on the physical topology delivered from upper level. We also suggested a method to solve the CRD model, which combined the genetic algorithm and the Floyd-Warshall algorithm. Finally, we used a practical example to verify the accuracy and the effectiveness of the CRD model and further applied the novel method on a train with six carriages.
Directory of Open Access Journals (Sweden)
Pedro L. Iglesias
2007-12-01
Full Text Available The Genetic Algorithms (GAs are a technique of optimization used for water distribution networks design. This work has been made with a modified pseudo genetic algorithm (PGA, whose main variation with a classical GA is a change in the codification of the chromosomes, which is made of numerical form instead of the binary codification. This variation entails a series of special characteristics in the codification and in the definition of the operations of mutation and crossover. Initially, the work displays the results of the PGA on a water network studied in the literature. The results show the kindness of the method. Also is made a statistical analysis of the obtained solutions. This analysis allows verifying the values of mutation and crossing probability more suitable for the proposed method. Finally, in the study of the analyzed water supply networks the concept of reliability in introduced. This concept is essential to understand the validity of the obtained results. The second part, starting with values optimized for the probability of crossing and mutation, the influence of the population size is analyzed in the final solutions on the network of Hanoi, widely studied in the bibliography. The aim is to find the most suitable configuration of the problem, so that good solutions are obtained in the less time.
Taheri, Mahboobeh; Mohebbi, Ali
2008-08-30
In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.
2014-01-01
Background To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. Results This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Conclusions Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel
Energy Technology Data Exchange (ETDEWEB)
Magnier, Laurent; Haghighat, Fariborz [Department of Building, Civil and Environmental Engineering, Concordia University, 1455 de Maisonneuve Blvd. W., BE-351, Montreal, Quebec H3G 1M8 (Canada)
2010-03-15
Building optimization involving multiple objectives is generally an extremely time-consuming process. The GAINN approach presented in this study first uses a simulation-based Artificial Neural Network (ANN) to characterize building behaviour, and then combines this ANN with a multiobjective Genetic Algorithm (NSGA-II) for optimization. The methodology has been used in the current study for the optimization of thermal comfort and energy consumption in a residential house. Results of ANN training and validation are first discussed. Two optimizations were then conducted taking variables from HVAC system settings, thermostat programming, and passive solar design. By integrating ANN into optimization the total simulation time was considerably reduced compared to classical optimization methodology. Results of the optimizations showed significant reduction in terms of energy consumption as well as improvement in thermal comfort. Finally, thanks to the multiobjective approach, dozens of potential designs were revealed, with a wide range of trade-offs between thermal comfort and energy consumption. (author)
Gossip algorithms in quantum networks
Siomau, Michael
2017-01-01
Gossip algorithms is a common term to describe protocols for unreliable information dissemination in natural networks, which are not optimally designed for efficient communication between network entities. We consider application of gossip algorithms to quantum networks and show that any quantum network can be updated to optimal configuration with local operations and classical communication. This allows to speed-up - in the best case exponentially - the quantum information dissemination. Irrespective of the initial configuration of the quantum network, the update requiters at most polynomial number of local operations and classical communication.
Design and Analysis of an Efficient Energy Algorithm in Wireless Social Sensor Networks
Zhang, Longzhen; Zhang, Wei; Vasilakos, Athanasios V.; Imran, Muhammad
2017-01-01
Because mobile ad hoc networks have characteristics such as lack of center nodes, multi-hop routing and changeable topology, the existing checkpoint technologies for normal mobile networks cannot be applied well to mobile ad hoc networks. Considering the multi-frequency hierarchy structure of ad hoc networks, this paper proposes a hybrid checkpointing strategy which combines the techniques of synchronous checkpointing with asynchronous checkpointing, namely the checkpoints of mobile terminals in the same cluster remain synchronous, and the checkpoints in different clusters remain asynchronous. This strategy could not only avoid cascading rollback among the processes in the same cluster, but also avoid too many message transmissions among the processes in different clusters. What is more, it can reduce the communication delay. In order to assure the consistency of the global states, this paper discusses the correctness criteria of hybrid checkpointing, which includes the criteria of checkpoint taking, rollback recovery and indelibility. Based on the designed Intra-Cluster Checkpoint Dependence Graph and Inter-Cluster Checkpoint Dependence Graph, the elimination rules for different kinds of checkpoints are discussed, and the algorithms for the same cluster checkpoints, different cluster checkpoints, and rollback recovery are also given. Experimental results demonstrate the proposed hybrid checkpointing strategy is a preferable trade-off method, which not only synthetically takes all kinds of resource constraints of Ad hoc networks into account, but also outperforms the existing schemes in terms of the dependence to cluster heads, the recovery time compared to the pure synchronous, and the pure asynchronous checkpoint advantage. PMID:28934171
Design and Analysis of an Efficient Energy Algorithm in Wireless Social Sensor Networks
Directory of Open Access Journals (Sweden)
Naixue Xiong
2017-09-01
Full Text Available Because mobile ad hoc networks have characteristics such as lack of center nodes, multi-hop routing and changeable topology, the existing checkpoint technologies for normal mobile networks cannot be applied well to mobile ad hoc networks. Considering the multi-frequency hierarchy structure of ad hoc networks, this paper proposes a hybrid checkpointing strategy which combines the techniques of synchronous checkpointing with asynchronous checkpointing, namely the checkpoints of mobile terminals in the same cluster remain synchronous, and the checkpoints in different clusters remain asynchronous. This strategy could not only avoid cascading rollback among the processes in the same cluster, but also avoid too many message transmissions among the processes in different clusters. What is more, it can reduce the communication delay. In order to assure the consistency of the global states, this paper discusses the correctness criteria of hybrid checkpointing, which includes the criteria of checkpoint taking, rollback recovery and indelibility. Based on the designed Intra-Cluster Checkpoint Dependence Graph and Inter-Cluster Checkpoint Dependence Graph, the elimination rules for different kinds of checkpoints are discussed, and the algorithms for the same cluster checkpoints, different cluster checkpoints, and rollback recovery are also given. Experimental results demonstrate the proposed hybrid checkpointing strategy is a preferable trade-off method, which not only synthetically takes all kinds of resource constraints of Ad hoc networks into account, but also outperforms the existing schemes in terms of the dependence to cluster heads, the recovery time compared to the pure synchronous, and the pure asynchronous checkpoint advantage.
Gossip algorithms in quantum networks
Energy Technology Data Exchange (ETDEWEB)
Siomau, Michael, E-mail: siomau@nld.ds.mpg.de [Physics Department, Jazan University, P.O. Box 114, 45142 Jazan (Saudi Arabia); Network Dynamics, Max Planck Institute for Dynamics and Self-Organization (MPIDS), 37077 Göttingen (Germany)
2017-01-23
Gossip algorithms is a common term to describe protocols for unreliable information dissemination in natural networks, which are not optimally designed for efficient communication between network entities. We consider application of gossip algorithms to quantum networks and show that any quantum network can be updated to optimal configuration with local operations and classical communication. This allows to speed-up – in the best case exponentially – the quantum information dissemination. Irrespective of the initial configuration of the quantum network, the update requiters at most polynomial number of local operations and classical communication. - Highlights: • We analyze the performance of gossip algorithms in quantum networks. • Local operations and classical communication (LOCC) can speed the performance up. • The speed-up is exponential in the best case; the number of LOCC is polynomial.
Joon-Woo Lee; Won Kim
2015-01-01
This paper reports the design of a randomly deployed heterogeneous wireless sensor network (HWSN) with two types of nodes: a powerful node and an ordinary node. Powerful nodes, such as Cluster Heads (CHs), communicate directly to the data sink of the network, and ordinary nodes sense the desired information and transmit the processed data to powerful nodes. The heterogeneity of HWSNs improves the networks lifetime and coverage. This paper focuses on the design of a random network among HWSNs....
A memetic algorithm for bi-objective integrated forward/reverse logistics network design
Pishvaee, Mir Saman; Farahani, Reza Zanjirani; Dullaert, Wout
Logistics network design is a major strategic issue due to its impact on the efficiency and responsiveness of the supply chain. This paper proposes a model for integrated logistics network design to avoid the sub-optimality caused by a separate, sequential design of forward and reverse logistics
An MPCC Formulation and Its Smooth Solution Algorithm for Continuous Network Design Problem
Directory of Open Access Journals (Sweden)
Guangmin Wang
2017-12-01
Full Text Available Continuous network design problem (CNDP is searching for a transportation network configuration to minimize the sum of the total system travel time and the investment cost of link capacity expansions by considering that the travellers follow a traditional Wardrop user equilibrium (UE to choose their routes. In this paper, the CNDP model can be formulated as mathematical programs with complementarity constraints (MPCC by describing UE as a non-linear complementarity problem (NCP. To address the difficulty resulting from complementarity constraints in MPCC, they are substituted by the Fischer-Burmeister (FB function, which can be smoothed by the introduction of the smoothing parameter. Therefore, the MPCC can be transformed into a well-behaved non-linear program (NLP by replacing the complementarity constraints with a smooth equation. Consequently, the solver such as LINDOGLOBAL in GAMS can be used to solve the smooth approximate NLP to obtain the solution to MPCC for modelling CNDP. The numerical experiments on the example from the literature demonstrate that the proposed algorithm is feasible.
Single Allocation Hub-and-spoke Networks Design Based on Ant Colony Optimization Algorithm
Directory of Open Access Journals (Sweden)
Yang Pingle
2014-10-01
Full Text Available Capacitated single allocation hub-and-spoke networks can be abstracted as a mixed integer linear programming model equation with three variables. Introducing an improved ant colony algorithm, which has six local search operators. Meanwhile, introducing the "Solution Pair" concept to decompose and optimize the composition of the problem, the problem can become more specific and effectively meet the premise and advantages of using ant colony algorithm. Finally, location simulation experiment is made according to Australia Post data to demonstrate this algorithm has good efficiency and stability for solving this problem.
Skiena, Steven S
2008-01-01
Explaining designing algorithms, and analyzing their efficacy and efficiency, this book covers combinatorial algorithms technology, stressing design over analysis. It presents instruction on methods for designing and analyzing computer algorithms. It contains the catalog of algorithmic resources, implementations and a bibliography
Two neural network algorithms for designing optimal terminal controllers with open final time
Plumer, Edward S.
1992-01-01
Multilayer neural networks, trained by the backpropagation through time algorithm (BPTT), have been used successfully as state-feedback controllers for nonlinear terminal control problems. Current BPTT techniques, however, are not able to deal systematically with open final-time situations such as minimum-time problems. Two approaches which extend BPTT to open final-time problems are presented. In the first, a neural network learns a mapping from initial-state to time-to-go. In the second, the optimal number of steps for each trial run is found using a line-search. Both methods are derived using Lagrange multiplier techniques. This theoretical framework is used to demonstrate that the derived algorithms are direct extensions of forward/backward sweep methods used in N-stage optimal control. The two algorithms are tested on a Zermelo problem and the resulting trajectories compare favorably to optimal control results.
Jacomy, Mathieu; Venturini, Tommaso; Heymann, Sebastien; Bastian, Mathieu
2014-01-01
Gephi is a network visualization software used in various disciplines (social network analysis, biology, genomics...). One of its key features is the ability to display the spatialization process, aiming at transforming the network into a map, and ForceAtlas2 is its default layout algorithm. The latter is developed by the Gephi team as an all-around solution to Gephi users' typical networks (scale-free, 10 to 10,000 nodes). We present here for the first time its functioning and settings. ForceAtlas2 is a force-directed layout close to other algorithms used for network spatialization. We do not claim a theoretical advance but an attempt to integrate different techniques such as the Barnes Hut simulation, degree-dependent repulsive force, and local and global adaptive temperatures. It is designed for the Gephi user experience (it is a continuous algorithm), and we explain which constraints it implies. The algorithm benefits from much feedback and is developed in order to provide many possibilities through its settings. We lay out its complete functioning for the users who need a precise understanding of its behaviour, from the formulas to graphic illustration of the result. We propose a benchmark for our compromise between performance and quality. We also explain why we integrated its various features and discuss our design choices.
Brands, Ties; van Berkum, Eric C.
2014-01-01
The optimization of infrastructure planning in a multimodal network is defined as a multi-objective network design problem, with accessibility, use of urban space by parking, operating deficit and climate impact as objectives. Decision variables are the location of park and ride facilities, train
Energy Technology Data Exchange (ETDEWEB)
Ozkan, Semra; Dincer, Salih [Department of Chemical Engineering, Chemical-Metallurgical Faculty, Yildiz Technical University, Davutpasa Kampusu, No 127, 34210 Esenler, Istanbul (Turkey)
2001-12-01
In this work, the methods used in pinch design were applied to a heat exchanger network with the aid of an improved problem algorithm table. This table enables one to compose composite and grand composite curves in a simplified way. A user friendly computer code entitled DarboTEK, compiled by using Visual Basic 3.0, was developed for the design of integrated heat exchanger networks and estimation of related capital costs. Based on the data obtained from the TUPRAS petroleum refinery at Izmit, a retrofit design of heat exchanger networks was accomplished using DarboTEK. An investment of 3,576,627 dollars is needed which will be paid back in 1.69 years simply by energy conservation due to heat integration. (Author)
A Branch and Cut algorithm for the container shipping network design problem
DEFF Research Database (Denmark)
Reinhardt, Line Blander; Pisinger, David
2012-01-01
programming model minimizing the overall cost. To better reflect the real-life situation we take into account the cost of transhipment, a heterogeneous fleet, route dependant capacities, and butterfly routes. To the best of our knowledge it is the first time an exact solution method to the problem considers......The network design problem in liner shipping is of increasing importance in a strongly competitive market where potential cost reductions can influence market share and profits significantly. In this paper the network design and fleet assignment problems are combined into a mixed integer linear...
A Branch and Cut algorithm for the container shipping network design problem
DEFF Research Database (Denmark)
Reinhardt, Line Blander; Kallehauge, Brian; Pisinger, David
The network design problem in liner shipping is of increasing importance in a strongly competitive market where potential cost reductions can influence market share and profits significantly. In this paper the network design and fleet assignment problems are combined into a mixed integer linear...... programming model minimizing the overall cost. To better reflect the real-life situation we take into account the cost of transhipment, a heterogeneous fleet, route dependant capacities, and butterfly routes. To the best of our knowledge it is the first time an exact solution method to the problem considers...
Evolutionary algorithms for mobile ad hoc networks
Dorronsoro, Bernabé; Danoy, Grégoire; Pigné, Yoann; Bouvry, Pascal
2014-01-01
Describes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networking. Mobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks—each of these require a designer’s keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking. This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process—allowing designers to put some “intelligence” or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms topology management, and mobility models to address challenges in the ...
Algorithms for protein design.
Gainza, Pablo; Nisonoff, Hunter M; Donald, Bruce R
2016-08-01
Computational structure-based protein design programs are becoming an increasingly important tool in molecular biology. These programs compute protein sequences that are predicted to fold to a target structure and perform a desired function. The success of a program's predictions largely relies on two components: first, the input biophysical model, and second, the algorithm that computes the best sequence(s) and structure(s) according to the biophysical model. Improving both the model and the algorithm in tandem is essential to improving the success rate of current programs, and here we review recent developments in algorithms for protein design, emphasizing how novel algorithms enable the use of more accurate biophysical models. We conclude with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins and protein assemblies. Copyright © 2016 Elsevier Ltd. All rights reserved.
8. Algorithm Design Techniques
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 2; Issue 8. Algorithms - Algorithm Design Techniques. R K Shyamasundar. Series Article Volume 2 Issue 8 August 1997 pp 6-17. Fulltext. Click here to view fulltext PDF. Permanent link: http://www.ias.ac.in/article/fulltext/reso/002/08/0006-0017 ...
Peacock, Christopher
2012-01-01
The purpose of this research effort was to develop a model that provides repeatable Location Management (LM) testing using a network simulation tool, QualNet version 5.1 (2011). The model will provide current and future protocol developers a framework to simulate stable protocol environments for development. This study used the Design Science…
Complex networks an algorithmic perspective
Erciyes, Kayhan
2014-01-01
Network science is a rapidly emerging field of study that encompasses mathematics, computer science, physics, and engineering. A key issue in the study of complex networks is to understand the collective behavior of the various elements of these networks.Although the results from graph theory have proven to be powerful in investigating the structures of complex networks, few books focus on the algorithmic aspects of complex network analysis. Filling this need, Complex Networks: An Algorithmic Perspective supplies the basic theoretical algorithmic and graph theoretic knowledge needed by every r
Teaching Structured Design of Network Algorithms in Enhanced Versions of SQL
de Brock, Bert
2004-01-01
From time to time developers of (database) applications will encounter, explicitly or implicitly, structures such as trees, graphs, and networks. Such applications can, for instance, relate to bills of material, organization charts, networks of (rail)roads, networks of conduit pipes (e.g., plumbing, electricity), telecom networks, and data…
Energy Technology Data Exchange (ETDEWEB)
Matias Sanchez, A.
1999-08-01
A proposal is put forward for calculating the economic dimension of water distribution networks using the heuristic technique of genetic algorithms. Genetic algorithms are based on the binary modification of potential solutions and their operation by means of selection, crossing and mutation so that the most economically viable individuals survive and the most expensive disappear. This method obviates the need to start off with a solution that satisfies the hydraulic requirements. If the network is not suitable, its costs will rise as a result of economic penalties and this will prevent it from being selected. Use of this approach allows the study of both discrete and continuous decision variables and it can be applied equally to new networks, the expansion of existing networks or piping renovation. An example is given to show the algorithm`s search capacity as well as its potential in regard to more complex networks. (Author) 11 refs.
Directory of Open Access Journals (Sweden)
Juan Reca
2017-11-01
Full Text Available This work presents a new approach to increase the efficiency of the heuristics methods applied to the optimal design of water distribution systems. The approach is based on reducing the search space by bounding the diameters that can be used for every network pipe. To reduce the search space, two opposite extreme flow distribution scenarios are analyzed and velocity restrictions to the pipe flow are then applied. The first scenario produces the most uniform flow distribution in the network. The opposite scenario is represented by the network with the maximum flow accumulation. Both extreme flow distributions are calculated by solving a quadratic programming problem, which is a very robust and efficient procedure. This approach has been coupled to a Genetic Algorithm (GA. The GA has an integer coding scheme and variable number of alleles depending on the number of diameters comprised within the velocity restrictions. The methodology has been applied to several benchmark networks and its performance has been compared to a classic GA formulation with a non-bounded search space. It considerably reduced the search space and provided a much faster and more accurate convergence than the GA formulation. This approach can also be coupled to other metaheuristics.
Algorithmic PON/P2P FTTH Access Network Design for CAPEX Minimization
DEFF Research Database (Denmark)
Papaefthimiou, Kostantinos; Tefera, Yonas; Mihylov, Dimitar
2013-01-01
Due to the emergence of high bandwidth-requiring services, telecommunication operators (telcos) are called to upgrade their fixed access network. In order to keep up with the competition, they must consider different optical access network solutions with Fiber To The Home (FTTH) as the prevailing...
Directory of Open Access Journals (Sweden)
Yongyut Amaritsakul
2013-01-01
Full Text Available Short-segment instrumentation for spine fractures is threatened by relatively high failure rates. Failure of the spinal pedicle screws including breakage and loosening may jeopardize the fixation integrity and lead to treatment failure. Two important design objectives, bending strength and pullout strength, may conflict with each other and warrant a multiobjective optimization study. In the present study using the three-dimensional finite element (FE analytical results based on an L25 orthogonal array, bending and pullout objective functions were developed by an artificial neural network (ANN algorithm, and the trade-off solutions known as Pareto optima were explored by a genetic algorithm (GA. The results showed that the knee solutions of the Pareto fronts with both high bending and pullout strength ranged from 92% to 94% of their maxima, respectively. In mechanical validation, the results of mathematical analyses were closely related to those of experimental tests with a correlation coefficient of −0.91 for bending and 0.93 for pullout (P<0.01 for both. The optimal design had significantly higher fatigue life (P<0.01 and comparable pullout strength as compared with commercial screws. Multiobjective optimization study of spinal pedicle screws using the hybrid of ANN and GA could achieve an ideal with high bending and pullout performances simultaneously.
Amaritsakul, Yongyut; Chao, Ching-Kong; Lin, Jinn
2013-01-01
Short-segment instrumentation for spine fractures is threatened by relatively high failure rates. Failure of the spinal pedicle screws including breakage and loosening may jeopardize the fixation integrity and lead to treatment failure. Two important design objectives, bending strength and pullout strength, may conflict with each other and warrant a multiobjective optimization study. In the present study using the three-dimensional finite element (FE) analytical results based on an L25 orthogonal array, bending and pullout objective functions were developed by an artificial neural network (ANN) algorithm, and the trade-off solutions known as Pareto optima were explored by a genetic algorithm (GA). The results showed that the knee solutions of the Pareto fronts with both high bending and pullout strength ranged from 92% to 94% of their maxima, respectively. In mechanical validation, the results of mathematical analyses were closely related to those of experimental tests with a correlation coefficient of -0.91 for bending and 0.93 for pullout (P < 0.01 for both). The optimal design had significantly higher fatigue life (P < 0.01) and comparable pullout strength as compared with commercial screws. Multiobjective optimization study of spinal pedicle screws using the hybrid of ANN and GA could achieve an ideal with high bending and pullout performances simultaneously.
Energy Technology Data Exchange (ETDEWEB)
Wang, Jiangfeng; Sun, Zhixin; Dai, Yiping [Institute of Turbomachinery, Xi' an Jiaotong University, No.28 Xianning West Road, Xi' an 710049 (China); Ma, Shaolin [Dongfang Steam Turbine Works, Deyang 618201 (China)
2010-04-15
Supercritical CO{sub 2} power cycle shows a high potential to recover low-grade waste heat due to its better temperature glide matching between heat source and working fluid in the heat recovery vapor generator (HRVG). Parametric analysis and exergy analysis are conducted to examine the effects of thermodynamic parameters on the cycle performance and exergy destruction in each component. The thermodynamic parameters of the supercritical CO{sub 2} power cycle is optimized with exergy efficiency as an objective function by means of genetic algorithm (GA) under the given waste heat condition. An artificial neural network (ANN) with the multi-layer feed-forward network type and back-propagation training is used to achieve parametric optimization design rapidly. It is shown that the key thermodynamic parameters, such as turbine inlet pressure, turbine inlet temperature and environment temperature have significant effects on the performance of the supercritical CO{sub 2} power cycle and exergy destruction in each component. It is also shown that the optimum thermodynamic parameters of supercritical CO{sub 2} power cycle can be predicted with good accuracy using artificial neural network under variable waste heat conditions. (author)
Fernandez, Michael; Caballero, Julio; Fernandez, Leyden; Sarai, Akinori
2011-02-01
Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.
Inter-Vehicular Ad Hoc Networks: From the Ground Truth to Algorithm Design and Testbed Architecture
Giordano, Eugenio
2011-01-01
Many of the devices we interact with on a daily basis are currently equipped with wireless connectivity. Soon this will be extended to the vehicles we drive/ride every day. Wirelessly connected vehicles will form a new kind of network that will enable a wide set of innovative applications ranging from enhanced safety to entertainment. To…
Izadi, Arman; Kimiagari, Ali Mohammad
2014-05-01
Distribution network design as a strategic decision has long-term effect on tactical and operational supply chain management. In this research, the location-allocation problem is studied under demand uncertainty. The purposes of this study were to specify the optimal number and location of distribution centers and to determine the allocation of customer demands to distribution centers. The main feature of this research is solving the model with unknown demand function which is suitable with the real-world problems. To consider the uncertainty, a set of possible scenarios for customer demands is created based on the Monte Carlo simulation. The coefficient of variation of costs is mentioned as a measure of risk and the most stable structure for firm's distribution network is defined based on the concept of robust optimization. The best structure is identified using genetic algorithms and 14 % reduction in total supply chain costs is the outcome. Moreover, it imposes the least cost variation created by fluctuation in customer demands (such as epidemic diseases outbreak in some areas of the country) to the logistical system. It is noteworthy that this research is done in one of the largest pharmaceutical distribution firms in Iran.
Design of the smart home system based on the optimal routing algorithm and ZigBee network
Xie, Xiaoxia
2017-01-01
To improve the traditional smart home system, its electric wiring, networking technology, information transmission and facility control are studied. In this paper, we study the electric wiring, networking technology, information transmission and facility control to improve the traditional smart home system. First, ZigBee is used to replace the traditional electric wiring. Second, a network is built to connect lots of wireless sensors and facilities, thanks to the capability of ZigBee self-organized network and Genetic Algorithm-Particle Swarm Optimization Algorithm (GA-PSOA) to search for the optimal route. Finally, when the smart home system is connected to the internet based on the remote server technology, home environment and facilities could be remote real-time controlled. The experiments show that the GA-PSOA reduce the system delay and decrease the energy consumption of the wireless system. PMID:29131868
Design of the smart home system based on the optimal routing algorithm and ZigBee network.
Jiang, Dengying; Yu, Ling; Wang, Fei; Xie, Xiaoxia; Yu, Yongsheng
2017-01-01
To improve the traditional smart home system, its electric wiring, networking technology, information transmission and facility control are studied. In this paper, we study the electric wiring, networking technology, information transmission and facility control to improve the traditional smart home system. First, ZigBee is used to replace the traditional electric wiring. Second, a network is built to connect lots of wireless sensors and facilities, thanks to the capability of ZigBee self-organized network and Genetic Algorithm-Particle Swarm Optimization Algorithm (GA-PSOA) to search for the optimal route. Finally, when the smart home system is connected to the internet based on the remote server technology, home environment and facilities could be remote real-time controlled. The experiments show that the GA-PSOA reduce the system delay and decrease the energy consumption of the wireless system.
Nezhadali, Azizollah; Motlagh, Maryam Omidvar; Sadeghzadeh, Samira
2018-02-05
A selective method based on molecularly imprinted polymer (MIP) solid-phase extraction (SPE) using UV-Vis spectrophotometry as a detection technique was developed for the determination of fluoxetine (FLU) in pharmaceutical and human serum samples. The MIPs were synthesized using pyrrole as a functional monomer in the presence of FLU as a template molecule. The factors that affecting the preparation and extraction ability of MIP such as amount of sorbent, initiator concentration, the amount of monomer to template ratio, uptake shaking rate, uptake time, washing buffer pH, take shaking rate, Taking time and polymerization time were considered for optimization. First a Plackett-Burman design (PBD) consists of 12 randomized runs were applied to determine the influence of each factor. The other optimization processes were performed using central composite design (CCD), artificial neural network (ANN) and genetic algorithm (GA). At optimal condition the calibration curve showed linearity over a concentration range of 10-7-10-8M with a correlation coefficient (R2) of 0.9970. The limit of detection (LOD) for FLU was obtained 6.56×10-9M. The repeatability of the method was obtained 1.61%. The synthesized MIP sorbent showed a good selectivity and sensitivity toward FLU. The MIP/SPE method was used for the determination of FLU in pharmaceutical, serum and plasma samples, successfully. Copyright © 2017 Elsevier B.V. All rights reserved.
Nezhadali, Azizollah; Motlagh, Maryam Omidvar; Sadeghzadeh, Samira
2018-02-01
A selective method based on molecularly imprinted polymer (MIP) solid-phase extraction (SPE) using UV-Vis spectrophotometry as a detection technique was developed for the determination of fluoxetine (FLU) in pharmaceutical and human serum samples. The MIPs were synthesized using pyrrole as a functional monomer in the presence of FLU as a template molecule. The factors that affecting the preparation and extraction ability of MIP such as amount of sorbent, initiator concentration, the amount of monomer to template ratio, uptake shaking rate, uptake time, washing buffer pH, take shaking rate, Taking time and polymerization time were considered for optimization. First a Plackett-Burman design (PBD) consists of 12 randomized runs were applied to determine the influence of each factor. The other optimization processes were performed using central composite design (CCD), artificial neural network (ANN) and genetic algorithm (GA). At optimal condition the calibration curve showed linearity over a concentration range of 10- 7-10- 8 M with a correlation coefficient (R2) of 0.9970. The limit of detection (LOD) for FLU was obtained 6.56 × 10- 9 M. The repeatability of the method was obtained 1.61%. The synthesized MIP sorbent showed a good selectivity and sensitivity toward FLU. The MIP/SPE method was used for the determination of FLU in pharmaceutical, serum and plasma samples, successfully.
Bicriteria network design problems
Energy Technology Data Exchange (ETDEWEB)
Marathe, M.V.; Ravi, R.; Sundaram, R.; Ravi, S.S.; Rosenkrantz, D.J.; Hunt, H.B. III
1994-12-31
We study several bicriteria network design problems phrased as follows: given an undirected graph and two minimization objectives with a budget specified on one objective, find a subgraph satisfying certain connectivity requirements that minimizes the second objective subject to the budget on the first. Define an ({alpha}, {beta})-approximation algorithm as a polynomial-time algorithm that produces a solution in which the first objective value is at most {alpha} times the budget, and the second objective value is at most {alpha} times the minimum cost of a network obeying the budget oil the first objective. We, present the first approximation algorithms for bicriteria problems obtained by combining classical minimization objectives such as the total edge cost of the network, the diameter of the network and a weighted generalization of the maximum degree of any node in the network. We first develop some formalism related to bicriteria problems that leads to a clean way to state bicriteria approximation results. Secondly, when the two objectives are similar but only differ based on the cost function under which they are computed we present a general parametric search technique that yields approximation algorithms by reducing the problem to one of minimizing a single objective of the same type. Thirdly, we present an O(log n, log n)-approximation algorithm for finding a diameter-constrained minimum cost spanning tree of an undirected graph on n nodes generalizing the notion of shallow, light trees and light approximate shortest-path trees that have been studied before. Finally, for the class of treewidth-bounded graphs, we provide pseudopolynomial-time algorithms for a number of bicriteria problems using dynamic programming. These pseudopolynomial-time algorithms can be converted to fully polynomial-time approximation schemes using a scaling technique.
DEFF Research Database (Denmark)
Soleimani, Hamed; Kannan, Govindan
2015-01-01
, customer awareness, and the economical motivations of the organizations. However, designing and planning a closed-loop supply chain is an NP-hard problem, which makes it difficult to achieve acceptable results in a reasonable time. In this paper, we try to cope with this problem by proposing a new......-heuristic algorithms are considered to develop a new elevated hybrid algorithm: the genetic algorithm (GA) and particle swarm optimization (PSO). Analyzing the above-mentioned algorithms' strengths and weaknesses leads us to attempt to improve the GA using some aspects of PSO. Therefore, a new hybrid algorithm...
Algorithms in combinatorial design theory
Colbourn, CJ
1985-01-01
The scope of the volume includes all algorithmic and computational aspects of research on combinatorial designs. Algorithmic aspects include generation, isomorphism and analysis techniques - both heuristic methods used in practice, and the computational complexity of these operations. The scope within design theory includes all aspects of block designs, Latin squares and their variants, pairwise balanced designs and projective planes and related geometries.
PARALLEL ALGORITHM FOR BAYESIAN NETWORK STRUCTURE LEARNING
Directory of Open Access Journals (Sweden)
S. A. Arustamov
2013-03-01
Full Text Available The article deals with implementation of a scalable parallel algorithm for structure learning of Bayesian network. Comparative analysis of sequential and parallel algorithms is done.
Routing algorithms in networks-on-chip
Daneshtalab, Masoud
2014-01-01
This book provides a single-source reference to routing algorithms for Networks-on-Chip (NoCs), as well as in-depth discussions of advanced solutions applied to current and next generation, many core NoC-based Systems-on-Chip (SoCs). After a basic introduction to the NoC design paradigm and architectures, routing algorithms for NoC architectures are presented and discussed at all abstraction levels, from the algorithmic level to actual implementation. Coverage emphasizes the role played by the routing algorithm and is organized around key problems affecting current and next generation, many-core SoCs. A selection of routing algorithms is included, specifically designed to address key issues faced by designers in the ultra-deep sub-micron (UDSM) era, including performance improvement, power, energy, and thermal issues, fault tolerance and reliability. · Provides a comprehensive overview of routing algorithms for Networks-on-Chip and NoC-based, manycore systems; · Describe...
Chakraborty, Tamal; Saha Misra, Iti
2016-03-01
Secondary Users (SUs) in a Cognitive Radio Network (CRN) face unpredictable interruptions in transmission due to the random arrival of Primary Users (PUs), leading to spectrum handoff or dropping instances. An efficient spectrum handoff algorithm, thus, becomes one of the indispensable components in CRN, especially for real-time communication like Voice over IP (VoIP). In this regard, this paper investigates the effects of spectrum handoff on the Quality of Service (QoS) for VoIP traffic in CRN, and proposes a real-time spectrum handoff algorithm in two phases. The first phase (VAST-VoIP based Adaptive Sensing and Transmission) adaptively varies the channel sensing and transmission durations to perform intelligent dropping decisions. The second phase (ProReact-Proactive and Reactive Handoff) deploys efficient channel selection mechanisms during spectrum handoff for resuming communication. Extensive performance analysis in analytical and simulation models confirms a decrease in spectrum handoff delay for VoIP SUs by more than 40% and 60%, compared to existing proactive and reactive algorithms, respectively and ensures a minimum 10% reduction in call-dropping probability with respect to the previous works in this domain. The effective SU transmission duration is also maximized under the proposed algorithm, thereby making it suitable for successful VoIP communication.
OPTIMAL NETWORK TOPOLOGY DESIGN
Yuen, J. H.
1994-01-01
This program was developed as part of a research study on the topology design and performance analysis for the Space Station Information System (SSIS) network. It uses an efficient algorithm to generate candidate network designs (consisting of subsets of the set of all network components) in increasing order of their total costs, and checks each design to see if it forms an acceptable network. This technique gives the true cost-optimal network, and is particularly useful when the network has many constraints and not too many components. It is intended that this new design technique consider all important performance measures explicitly and take into account the constraints due to various technical feasibilities. In the current program, technical constraints are taken care of by the user properly forming the starting set of candidate components (e.g. nonfeasible links are not included). As subsets are generated, they are tested to see if they form an acceptable network by checking that all requirements are satisfied. Thus the first acceptable subset encountered gives the cost-optimal topology satisfying all given constraints. The user must sort the set of "feasible" link elements in increasing order of their costs. The program prompts the user for the following information for each link: 1) cost, 2) connectivity (number of stations connected by the link), and 3) the stations connected by that link. Unless instructed to stop, the program generates all possible acceptable networks in increasing order of their total costs. The program is written only to generate topologies that are simply connected. Tests on reliability, delay, and other performance measures are discussed in the documentation, but have not been incorporated into the program. This program is written in PASCAL for interactive execution and has been implemented on an IBM PC series computer operating under PC DOS. The disk contains source code only. This program was developed in 1985.
Shirley, C.
2003-12-01
A genetic algorithm (GA) is used to select the operational parameters of artificial neural networks (ANN) which are trained to predict fluid velocity. Populations of three-layer, feedforward backpropagation ANN's with varying numbers of hidden nodes, types and slopes of activation functions, alpha and beta learning rates and initial distributions of weights for both the input and hidden layers are created by the GA. The GA- defined ANN's are trained with inputs-output pairs of hydraulic conductivity neighborhoods and resulting fluid velocities at certain points in the simulation domain. The hydraulic conductivity fields are highly heterogeneous with an ensemble log conductivity variance of 1.0. Results of the GA are defined and selected ANN velocity predictions are presented.
Optical network design and planning
Simmons, Jane M
2014-01-01
This book takes a pragmatic approach to designing state-of-the-art optical networks for backbone, regional, and metro-core networks. Algorithms and methodologies related to routing, regeneration, wavelength assignment, subrate-traffic grooming, and protection are presented, with an emphasis on optical-bypass-enabled (or all-optical) networks. There are numerous case studies throughout the text to illustrate the concepts, using realistic networks and traffic sets. A full chapter of economic studies offers guidelines as to when and how optical-bypass technology should be deployed. There is also extensive coverage of recent research to provide insight into how optical networks are likely to evolve. The second edition includes new chapters on dynamic optical networking and flexible/elastic optical networks. There is expanded coverage of new physical-layer technology and its impact on network design, along with enhanced coverage of ROADM architectures, including the colorless, directionless, contentionless, a...
Algorithms Design Techniques and Analysis
Alsuwaiyel, M H
1999-01-01
Problem solving is an essential part of every scientific discipline. It has two components: (1) problem identification and formulation, and (2) solution of the formulated problem. One can solve a problem on its own using ad hoc techniques or follow those techniques that have produced efficient solutions to similar problems. This requires the understanding of various algorithm design techniques, how and when to use them to formulate solutions and the context appropriate for each of them. This book advocates the study of algorithm design techniques by presenting most of the useful algorithm desi
Jia, F.; Lichti, D.
2017-09-01
The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn't guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.
Novel quantum inspired binary neural network algorithm
Indian Academy of Sciences (India)
In this paper, a quantum based binary neural network algorithm is proposed, named as novel quantum binary neural network algorithm (NQ-BNN). It forms a neural network structure by deciding weights and separability parameter in quantum based manner. Quantum computing concept represents solution probabilistically ...
General lossless planar coupler design algorithms.
Vance, Rod
2015-08-01
This paper reviews and extends two classes of algorithms for the design of planar couplers with any unitary transfer matrix as design goals. Such couplers find use in optical sensing for fading free interferometry, coherent optical network demodulation, and also for quantum state preparation in quantum optical experiments and technology. The two classes are (1) "atomic coupler algorithms" decomposing a unitary transfer matrix into a planar network of 2×2 couplers, and (2) "Lie theoretic algorithms" concatenating unit cell devices with variable phase delay sets that form canonical coordinates for neighborhoods in the Lie group U(N), so that the concatenations realize any transfer matrix in U(N). As well as review, this paper gives (1) a Lie theoretic proof existence proof showing that both classes of algorithms work and (2) direct proofs of the efficacy of the "atomic coupler" algorithms. The Lie theoretic proof strengthens former results. 5×5 couplers designed by both methods are compared by Monte Carlo analysis, which would seem to imply atomic rather than Lie theoretic methods yield designs more resilient to manufacturing imperfections.
Communication Complexity (for Algorithm Designers)
Roughgarden, Tim
2015-01-01
This document collects the lecture notes from my course "Communication Complexity (for Algorithm Designers),'' taught at Stanford in the winter quarter of 2015. The two primary goals of the course are: 1. Learn several canonical problems that have proved the most useful for proving lower bounds (Disjointness, Index, Gap-Hamming, etc.). 2. Learn how to reduce lower bounds for fundamental algorithmic problems to communication complexity lower bounds. Along the way, we'll also: 3. Get exposure t...
Directory of Open Access Journals (Sweden)
Buratti Chiara
2005-01-01
Full Text Available A new energy-efficient scheme for data transmission in a wireless sensor network (WSN is proposed, having in mind a typical application including a sink, which periodically triggers the WSN, and nodes uniformly distributed over a specified area. Routing, multiple access control (MAC, physical, energy, and propagation aspects are jointly taken into account through simulation; however, the protocol design is based on some analytical considerations reported in the appendix. Information routing is based on a clustered self-organized structure; a carrier-sensing multiple access (CSMA protocol is chosen at MAC layer. Two different scenarios are examined, characterized by different channel fading rates. Four versions of our protocol are presented, suitably oriented to the two different scenarios; two of them implement a cross-layer (CL approach, where MAC parameters influence both the network and physical layers. Performance is measured in terms of network lifetime (related to energy efficiency and packet loss rate (related to network availability. The paper discusses the rationale behind the selection of MAC protocols for WSNs and provides a complete model characterization spanning from the network layer to the propagation channel. The advantages of the CL approach, with respect to an algorithm which belongs to the well-known class of low-energy adaptive clustering hierarchy (LEACH protocols, are shown.
Scalable Virtual Network Mapping Algorithm for Internet-Scale Networks
Yang, Qiang; Wu, Chunming; Zhang, Min
The proper allocation of network resources from a common physical substrate to a set of virtual networks (VNs) is one of the key technical challenges of network virtualization. While a variety of state-of-the-art algorithms have been proposed in an attempt to address this issue from different facets, the challenge still remains in the context of large-scale networks as the existing solutions mainly perform in a centralized manner which requires maintaining the overall and up-to-date information of the underlying substrate network. This implies the restricted scalability and computational efficiency when the network scale becomes large. This paper tackles the virtual network mapping problem and proposes a novel hierarchical algorithm in conjunction with a substrate network decomposition approach. By appropriately transforming the underlying substrate network into a collection of sub-networks, the hierarchical virtual network mapping algorithm can be carried out through a global virtual network mapping algorithm (GVNMA) and a local virtual network mapping algorithm (LVNMA) operated in the network central server and within individual sub-networks respectively with their cooperation and coordination as necessary. The proposed algorithm is assessed against the centralized approaches through a set of numerical simulation experiments for a range of network scenarios. The results show that the proposed hierarchical approach can be about 5-20 times faster for VN mapping tasks than conventional centralized approaches with acceptable communication overhead between GVNCA and LVNCA for all examined networks, whilst performs almost as well as the centralized solutions.
Algorithms for radio networks with dynamic topology
Shacham, Nachum; Ogier, Richard; Rutenburg, Vladislav V.; Garcia-Luna-Aceves, Jose
1991-08-01
The objective of this project was the development of advanced algorithms and protocols that efficiently use network resources to provide optimal or nearly optimal performance in future communication networks with highly dynamic topologies and subject to frequent link failures. As reflected by this report, we have achieved our objective and have significantly advanced the state-of-the-art in this area. The research topics of the papers summarized include the following: efficient distributed algorithms for computing shortest pairs of disjoint paths; minimum-expected-delay alternate routing algorithms for highly dynamic unreliable networks; algorithms for loop-free routing; multipoint communication by hierarchically encoded data; efficient algorithms for extracting the maximum information from event-driven topology updates; methods for the neural network solution of link scheduling and other difficult problems arising in communication networks; and methods for robust routing in networks subject to sophisticated attacks.
Competitive Liner Shipping Network Design
DEFF Research Database (Denmark)
Karsten, Christian Vad; Brouer, Berit Dangaard; Pisinger, David
We present a solution method for the liner shipping network design problem which is a core strategic planning problem faced by container carriers. We propose the first practical algorithm which explicitly handles transshipment time limits for all demands. Individual sailing speeds at each service...... leg are used to balance sailings speed against operational costs, hence ensuring that the found network is competitive on both transit time and cost. We present a matheuristic for the problem where a MIP is used to select which ports should be inserted or removed on a route. Computational results...... are presented showing very promising results for realistic global liner shipping networks. Due to a number of algorithmic enhancements, the obtained solutions can be found within the same time frame as used by previous algorithms not handling time constraints. Furthermore we present a sensitivity analysis...
Molecular beacon sequence design algorithm.
Monroe, W Todd; Haselton, Frederick R
2003-01-01
A method based on Web-based tools is presented to design optimally functioning molecular beacons. Molecular beacons, fluorogenic hybridization probes, are a powerful tool for the rapid and specific detection of a particular nucleic acid sequence. However, their synthesis costs can be considerable. Since molecular beacon performance is based on its sequence, it is imperative to rationally design an optimal sequence before synthesis. The algorithm presented here uses simple Microsoft Excel formulas and macros to rank candidate sequences. This analysis is carried out using mfold structural predictions along with other free Web-based tools. For smaller laboratories where molecular beacons are not the focus of research, the public domain algorithm described here may be usefully employed to aid in molecular beacon design.
Automatic design of decision-tree algorithms with evolutionary algorithms.
Barros, Rodrigo C; Basgalupp, Márcio P; de Carvalho, André C P L F; Freitas, Alex A
2013-01-01
This study reports the empirical analysis of a hyper-heuristic evolutionary algorithm that is capable of automatically designing top-down decision-tree induction algorithms. Top-down decision-tree algorithms are of great importance, considering their ability to provide an intuitive and accurate knowledge representation for classification problems. The automatic design of these algorithms seems timely, given the large literature accumulated over more than 40 years of research in the manual design of decision-tree induction algorithms. The proposed hyper-heuristic evolutionary algorithm, HEAD-DT, is extensively tested using 20 public UCI datasets and 10 microarray gene expression datasets. The algorithms automatically designed by HEAD-DT are compared with traditional decision-tree induction algorithms, such as C4.5 and CART. Experimental results show that HEAD-DT is capable of generating algorithms which are significantly more accurate than C4.5 and CART.
Algorithmic Design Tools in Design Process
Directory of Open Access Journals (Sweden)
Daryanto Daryanto
2011-06-01
Full Text Available This article explores algorithmic design methods in a design process that uses natural phenomena as the basis of its architectural morphology. It implements digital morphogenesis in reaction to ecology and the influential forces of the building environment. This paper is divided into two equally important sections: the process description and the project implementation. The description of the process demonstrates the methods used and the idea involved in incorporating nature’s influential elements as part of the creative task. Meanwhile, the project implementation showed practical case of the outcome of that process. Tools for visualizing and simulating nature’s environment are showed using algorithmic design method. The tools create transformations in NURBS-based surfaces through the translation of their respective control point matrices. The tools generate several different alternatives to be tested and analyzed.
The Vital Network: An Algorithmic Milieu of Communication and Control
Directory of Open Access Journals (Sweden)
Sandra Robinson
2016-09-01
Full Text Available The biological turn in computing has influenced the development of algorithmic control and what I call the vital network: a dynamic, relational, and generative assemblage that is self-organizing in response to the heterogeneity of contemporary network processes, connections, and communication. I discuss this biological turn in computation and control for communication alongside historically significant developments in cybernetics that set out the foundation for the development of self-regulating computer systems. Control is shifting away from models that historically relied on the human-animal model of cognition to govern communication and control, as in early cybernetics and computer science, to a decentred, nonhuman model of control by algorithm for communication and networks. To illustrate the rise of contemporary algorithmic control, I outline a particular example, that of the biologically-inspired routing algorithm known as a ‘quorum sensing’ algorithm. The increasing expansion of algorithms as a sense-making apparatus is important in the context of social media, but also in the subsystems that coordinate networked flows of information. In that domain, algorithms are not inferring categories of identity, sociality, and practice associated with Internet consumers, rather, these algorithms are designed to act on information flows as they are transmitted along the network. The development of autonomous control realized through the power of the algorithm to monitor, sort, organize, determine, and transmit communication is the form of control emerging as a postscript to Gilles Deleuze’s ‘postscript on societies of control.’
Nouiri, Issam
2017-11-01
This paper presents the development of multi-objective Genetic Algorithms to optimize chlorination design and management in drinking water networks (DWN). Three objectives have been considered: the improvement of the chlorination uniformity (healthy objective), the minimization of chlorine booster stations number, and the injected chlorine mass (economic objectives). The problem has been dissociated in medium and short terms ones. The proposed methodology was tested on hypothetical and real DWN. Results proved the ability of the developed optimization tool to identify relationships between the healthy and economic objectives as Pareto fronts. The proposed approach was efficient in computing solutions ensuring better chlorination uniformity while requiring the weakest injected chlorine mass when compared to other approaches. For the real DWN studied, chlorination optimization has been crowned by great improvement of free-chlorine-dosing uniformity and by a meaningful chlorine mass reduction, in comparison with the conventional chlorination.
Nouiri, Issam
2017-09-01
This paper presents the development of multi-objective Genetic Algorithms to optimize chlorination design and management in drinking water networks (DWN). Three objectives have been considered: the improvement of the chlorination uniformity (healthy objective), the minimization of chlorine booster stations number, and the injected chlorine mass (economic objectives). The problem has been dissociated in medium and short terms ones. The proposed methodology was tested on hypothetical and real DWN. Results proved the ability of the developed optimization tool to identify relationships between the healthy and economic objectives as Pareto fronts. The proposed approach was efficient in computing solutions ensuring better chlorination uniformity while requiring the weakest injected chlorine mass when compared to other approaches. For the real DWN studied, chlorination optimization has been crowned by great improvement of free-chlorine-dosing uniformity and by a meaningful chlorine mass reduction, in comparison with the conventional chlorination.
Energy Aware Clustering Algorithms for Wireless Sensor Networks
Rakhshan, Noushin; Rafsanjani, Marjan Kuchaki; Liu, Chenglian
2011-09-01
The sensor nodes deployed in wireless sensor networks (WSNs) are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. In wireless sensor networks, hierarchical network structures have the advantage of providing scalable and energy efficient solutions. In this paper, we investigate different clustering algorithms for WSNs and also compare these clustering algorithms based on metrics such as clustering distribution, cluster's load balancing, Cluster Head's (CH) selection strategy, CH's role rotation, node mobility, clusters overlapping, intra-cluster communications, reliability, security and location awareness.
Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng
2014-01-01
A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping.
Directory of Open Access Journals (Sweden)
Jiali Wang
2014-01-01
Full Text Available A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping.
Algorithms and networking for computer games
Smed, Jouni
2006-01-01
Algorithms and Networking for Computer Games is an essential guide to solving the algorithmic and networking problems of modern commercial computer games, written from the perspective of a computer scientist. Combining algorithmic knowledge and game-related problems, the authors discuss all the common difficulties encountered in game programming. The first part of the book tackles algorithmic problems by presenting how they can be solved practically. As well as ""classical"" topics such as random numbers, tournaments and game trees, the authors focus on how to find a path in, create the terrai
Directory of Open Access Journals (Sweden)
V. E. Marley
2015-01-01
Full Text Available Summary. The concept of algorithmic models appeared from the algorithmic approach in which the simulated object, the phenomenon appears in the form of process, subject to strict rules of the algorithm, which placed the process of operation of the facility. Under the algorithmic model is the formalized description of the scenario subject specialist for the simulated process, the structure of which is comparable with the structure of the causal and temporal relationships between events of the process being modeled, together with all information necessary for its software implementation. To represent the structure of algorithmic models used algorithmic network. Normally, they were defined as loaded finite directed graph, the vertices which are mapped to operators and arcs are variables, bound by operators. The language of algorithmic networks has great features, the algorithms that it can display indifference the class of all random algorithms. In existing systems, automation modeling based on algorithmic nets, mainly used by operators working with real numbers. Although this reduces their ability, but enough for modeling a wide class of problems related to economy, environment, transport, technical processes. The task of modeling the execution of schedules and network diagrams is relevant and useful. There are many counting systems, network graphs, however, the monitoring process based analysis of gaps and terms of graphs, no analysis of prediction execution schedule or schedules. The library is designed to build similar predictive models. Specifying source data to obtain a set of projections from which to choose one and take it for a new plan.
Projection learning algorithm for threshold - controlled neural networks
Energy Technology Data Exchange (ETDEWEB)
Reznik, A.M.
1995-03-01
The projection learning algorithm proposed in [1, 2] and further developed in [3] substantially improves the efficiency of memorizing information and accelerates the learning process in neural networks. This algorithm is compatible with the completely connected neural network architecture (the Hopfield network [4]), but its application to other networks involves a number of difficulties. The main difficulties include constraints on interconnection structure and the need to eliminate the state uncertainty of latent neurons if such are present in the network. Despite the encouraging preliminary results of [3], further extension of the applications of the projection algorithm therefore remains problematic. In this paper, which is a continuation of the work begun in [3], we consider threshold-controlled neural networks. Networks of this type are quite common. They represent the receptor neuron layers in some neurocomputer designs. A similar structure is observed in the lower divisions of biological sensory systems [5]. In multilayer projection neural networks with lateral interconnections, the neuron layers or parts of these layers may also have the structure of a threshold-controlled completely connected network. Here the thresholds are the potentials delivered through the projection connections from other parts of the network. The extension of the projection algorithm to the class of threshold-controlled networks may accordingly prove to be useful both for extending its technical applications and for better understanding of the operation of the nervous system in living organisms.
Genetic Algorithm Optimized Neural Networks Ensemble as ...
African Journals Online (AJOL)
NJD
Genetic Algorithm Optimized Neural Networks Ensemble as. Calibration Model for Simultaneous Spectrophotometric. Estimation of Atenolol and Losartan Potassium in Tablets. Dondeti Satyanarayana*, Kamarajan Kannan and Rajappan Manavalan. Department of Pharmacy, Annamalai University, Annamalainagar, Tamil ...
Directory of Open Access Journals (Sweden)
Shou-feng Ji
2017-06-01
Full Text Available The increasing concern on global warming is prompting transportation sector to take into account more sustainable operation strategies. Among them, intermodal transportation (IT has already been regarded as one of the most effective measures on carbon reductions. This paper focuses on the model and algorithm for a certain kind of IT, namely multi-objective multi-sourcing intermodal transportation network design problem (MO_MITNDP, in which carbon emission factors are specially considered. The MO_MITNDP is concerned with determining optimal transportation routes and modes for a series of freight provided by multiple sourcing places to find good balance between the total costs and time efficiencies. First, we establish a multi-objective integer programming model to formulate the MO_MITNDP with total cost (TTC and maximum flow time (MFT criteria. Specifically, carbon emission costs distinguished by the different transportation mode and route are included in the cost function. Second, to solve the MO_MITNDP, a hybrid estimation of distribution algorithm (HEDA combined with a heterogeneous marginal distribution and a multi-objective local search is proposed, in which the from the Pareto dominance scenario. Finally, based on randomly generated data and a real-life case study of Jilin Petrochemical Company (JPC, China, simulation experiments and comparisons are carried out to demonstrate the effectiveness and application value of the proposed HEDA.
Flow enforcement algorithms for ATM networks
DEFF Research Database (Denmark)
Dittmann, Lars; Jacobsen, Søren B.; Moth, Klaus
1991-01-01
Four measurement algorithms for flow enforcement in asynchronous transfer mode (ATM) networks are presented. The algorithms are the leaky bucket, the rectangular sliding window, the triangular sliding window, and the exponentially weighted moving average. A comparison, based partly on teletraffic....... Implementations are proposed on the block diagram level, and dimensioning examples are carried out when flow enforcing a renewal-type connection using the four algorithms. The corresponding hardware demands are estimated aid compared......Four measurement algorithms for flow enforcement in asynchronous transfer mode (ATM) networks are presented. The algorithms are the leaky bucket, the rectangular sliding window, the triangular sliding window, and the exponentially weighted moving average. A comparison, based partly on teletraffic...... theory and partly on signal processing theory, is carried out. It is seen that the time constant involved increases with the increasing burstiness of the connection. It is suggested that the RMS measurement bandwidth be used to dimension linear algorithms for equal flow enforcement characteristics...
Principal component analysis networks and algorithms
Kong, Xiangyu; Duan, Zhansheng
2017-01-01
This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.
Improved Degree Search Algorithms in Unstructured P2P Networks
Directory of Open Access Journals (Sweden)
Guole Liu
2012-01-01
Full Text Available Searching and retrieving the demanded correct information is one important problem in networks; especially, designing an efficient search algorithm is a key challenge in unstructured peer-to-peer (P2P networks. Breadth-first search (BFS and depth-first search (DFS are the current two typical search methods. BFS-based algorithms show the perfect performance in the aspect of search success rate of network resources, while bringing the huge search messages. On the contrary, DFS-based algorithms reduce the search message quantity and also cause the dropping of search success ratio. To address the problem that only one of performances is excellent, we propose two memory function degree search algorithms: memory function maximum degree algorithm (MD and memory function preference degree algorithm (PD. We study their performance including the search success rate and the search message quantity in different networks, which are scale-free networks, random graph networks, and small-world networks. Simulations show that the two performances are both excellent at the same time, and the performances are improved at least 10 times.
DEFF Research Database (Denmark)
Thomadsen, Tommy
2005-01-01
design. The papers have all been submitted for journals, and except for two papers, are awaiting review. The papers are mostly concerned with optimal methods and, in a few cases, heuristics for designing hierarchical and ring networks. All papers develop bounds which are used in the optimal methods...... danne grundlag for et studie af design af hierarkiske netværk. Afhandlings vigtigste bidrag best ar af syv artikler, der er inkluderet i appendiks. Artiklerne handler om design af hierarkisk netværk og ring netværk. Artiklerne er alle indsendt til videnskablige journaler og afventer bedømmelse, bortset......Communication networks are immensely important today, since both companies and individuals use numerous services that rely on them. This thesis considers the design of hierarchical (communication) networks. Hierarchical networks consist of layers of networks and are well-suited for coping...
Nasiri, G. Reza; Davoudpour, Hamid; Movahedi, Yaser
Distribution decisions play an important role in the strategic planning of supply chain management. In order to use the most proper strategic decisions in a supply chain, decision makers should focus on the identification and management of the sources of uncertainties in the supply chain process. In this paper these conditions in a multi-period problem with demands changed over the planning horizon is considered. We develop a non-linear mixed-integer model and propose an efficient heuristic genetic based algorithm which finds the optimal facility locations/allocation, relocation times and the total cost, for the whole supply chain. To explore the viability and efficiency of the proposed model and the solution approach, various computational experiments are performed based on the real size case problems.
DEFF Research Database (Denmark)
Brouer, Berit Dangaard
This thesis concerns design of liner shipping networks using operations research to optimize liner shipping networks at the strategic, tactical and operational level. Liner shipping networks are often compared to public transit networks as they consist of a set of scheduled sailings connecting....... The research field of liner shipping network design is relatively young and many open research questions exists. Among others, a unified and rich mathematical model formulating the main characteristics of the business domain has not been clearly described and exact methods for such mathematical models...... are still not able to solve significant instances of this complex optimization problem. In this thesis two research directions are explored within the field: The first research direction contributes to basic research on the liner shipping network design problem by describing the domain seen from...
A Multidomain Survivable Virtual Network Mapping Algorithm
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Xiancui Xiao
2017-01-01
Full Text Available Although the existing networks are more often deployed in the multidomain environment, most of existing researches focus on single-domain networks and there are no appropriate solutions for the multidomain virtual network mapping problem. In fact, most studies assume that the underlying network can operate without any interruption. However, physical networks cannot ensure the normal provision of network services for external reasons and traditional single-domain networks have difficulties to meet user needs, especially for the high security requirements of the network transmission. In order to solve the above problems, this paper proposes a survivable virtual network mapping algorithm (IntD-GRC-SVNE that implements multidomain mapping in network virtualization. IntD-GRC-SVNE maps the virtual communication networks onto different domain networks and provides backup resources for virtual links which improve the survivability of the special networks. Simulation results show that IntD-GRC-SVNE can not only improve the survivability of multidomain communications network but also render the network load more balanced and greatly improve the network acceptance rate due to employment of GRC (global resource capacity.
Genetic algorithm for neural networks optimization
Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta
2004-11-01
This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.
Vectorized algorithms for spiking neural network simulation.
Brette, Romain; Goodman, Dan F M
2011-06-01
High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usually associated with compiled languages.
Model for Microcirculation Transportation Network Design
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Qun Chen
2012-01-01
Full Text Available The idea of microcirculation transportation was proposed to shunt heavy traffic on arterial roads through branch roads. The optimization model for designing micro-circulation transportation network was developed to pick out branch roads as traffic-shunting channels and determine their required capacity, trying to minimize the total reconstruction expense and land occupancy subject to saturation and reconstruction space constraints, while accounting for the route choice behaviour of network users. Since micro-circulation transportation network design problem includes both discrete and continuous variables, a discretization method was developed to convert two groups of variables (discrete variables and continuous variables into one group of new discrete variables, transforming the mixed network design problem into a new kind of discrete network design problem with multiple values. The genetic algorithm was proposed to solve the new discrete network design problem. Finally a numerical example demonstrated the efficiency of the model and algorithm.
Sathiya, P.; Panneerselvam, K.; Soundararajan, R.
2012-09-01
Laser welding input parameters play a very significant role in determining the quality of a weld joint. The joint quality can be defined in terms of properties such as weld bead geometry, mechanical properties and distortion. Therefore, mechanical properties should be controlled to obtain good welded joints. In this study, the weld bead geometry such as depth of penetration (DP), bead width (BW) and tensile strength (TS) of the laser welded butt joints made of AISI 904L super austenitic stainless steel were investigated. Full factorial design was used to carry out the experimental design. Artificial Neural networks (ANN) program was developed in MatLab software to establish the relationships between the laser welding input parameters like beam power, travel speed and focal position and the three responses DP, BW and TS in three different shielding gases (Argon, Helium and Nitrogen). The established models were used for optimizing the process parameters using Genetic Algorithm (GA). Optimum solutions for the three different gases and their respective responses were obtained. Confirmation experiment has also been conducted to validate the optimized parameters obtained from GA.
Stoer, Mechthild
1992-01-01
The problem of designing a cost-efficient network that survives the failure of one or more nodes or edges of the network is critical to modern telecommunications engineering. The method developed in this book is designed to solve such problems to optimality. In particular, a cutting plane approach is described, based on polyhedral combinatorics, that is ableto solve real-world problems of this type in short computation time. These results are of interest for practitioners in the area of communication network design. The book is addressed especially to the combinatorial optimization community, but also to those who want to learn polyhedral methods. In addition, interesting new research problemsare formulated.
Possel, B.; Wismans, Luc Johannes Josephus; van Berkum, Eric C.; Bliemer, M.C.J.
2016-01-01
Incorporation of externalities in the Multi-Objective Network Design Problem (MO NDP) as objectives is an important step in designing sustainable networks. In this research the problem is defined as a bi-level optimization problem in which minimizing externalities are the objectives and link types
Optimizing of Passive Optical Network Deployment Using Algorithm with Metrics
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Tomas Pehnelt
2017-01-01
Full Text Available Various approaches and methods are used for designing of optimum deployment of Passive Optical Networks (PON according to selected optimization criteria, such as optimal trenching distance, endpoint attenuation and overall installed fibre length. This article describes the ideas and possibilities for an algorithm with the application of graph algorithms for finding the shortest path from Optical Line Termination to Optical Network Terminal unit. This algorithm uses a combination of different methods for generating of an optimal metric, thus creating the optimized tree topology mainly focused on summary trenching distance. Furthermore, it deals with algorithms for finding an optimal placement of optical splitter with the help of K-Means clustering method and hierarchical clustering technique. The results of the proposed algorithm are compared with existing methods.
Online Algorithms for Adaptive Optimization in Heterogeneous Delay Tolerant Networks
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Wissam Chahin
2013-12-01
Full Text Available Delay Tolerant Networks (DTNs are an emerging type of networks which do not need a predefined infrastructure. In fact, data forwarding in DTNs relies on the contacts among nodes which may possess different features, radio range, battery consumption and radio interfaces. On the other hand, efficient message delivery under limited resources, e.g., battery or storage, requires to optimize forwarding policies. We tackle optimal forwarding control for a DTN composed of nodes of different types, forming a so-called heterogeneous network. Using our model, we characterize the optimal policies and provide a suitable framework to design a new class of multi-dimensional stochastic approximation algorithms working for heterogeneous DTNs. Crucially, our proposed algorithms drive online the source node to the optimal operating point without requiring explicit estimation of network parameters. A thorough analysis of the convergence properties and stability of our algorithms is presented.
Node-Dependence-Based Dynamic Incentive Algorithm in Opportunistic Networks
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Ruiyun Yu
2014-01-01
Full Text Available Opportunistic networks lack end-to-end paths between source nodes and destination nodes, so the communications are mainly carried out by the “store-carry-forward” strategy. Selfish behaviors of rejecting packet relay requests will severely worsen the network performance. Incentive is an efficient way to reduce selfish behaviors and hence improves the reliability and robustness of the networks. In this paper, we propose the node-dependence-based dynamic gaming incentive (NDI algorithm, which exploits the dynamic repeated gaming to motivate nodes relaying packets for other nodes. The NDI algorithm presents a mechanism of tolerating selfish behaviors of nodes. Reward and punishment methods are also designed based on the node dependence degree. Simulation results show that the NDI algorithm is effective in increasing the delivery ratio and decreasing average latency when there are a lot of selfish nodes in the opportunistic networks.
Real-world experimentation of distributed DSA network algorithms
DEFF Research Database (Denmark)
Tonelli, Oscar; Berardinelli, Gilberto; Tavares, Fernando Menezes Leitão
2013-01-01
of the available spectrum by nodes in a network, without centralized coordination. While proof-of-concept and statistical validation of such algorithms is typically achieved by using system level simulations, experimental activities are valuable contributions for the investigation of particular aspects......The problem of spectrum scarcity in uncoordinated and/or heterogeneous wireless networks is the key aspect driving the research in the field of flexible management of frequency resources. In particular, distributed dynamic spectrum access (DSA) algorithms enable an efficient sharing...... such as a dynamic propagation environment, human presence impact and terminals mobility. This chapter focuses on the practical aspects related to the real world-experimentation with distributed DSA network algorithms over a testbed network. Challenges and solutions are extensively discussed, from the testbed design...
Using network properties to evaluate targeted immunization algorithms
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Bita Shams
2014-09-01
Full Text Available Immunization of complex network with minimal or limited budget is a challenging issue for research community. In spite of much literature in network immunization, no comprehensive research has been conducted for evaluation and comparison of immunization algorithms. In this paper, we propose an evaluation framework for immunization algorithms regarding available amount of vaccination resources, goal of immunization program, and time complexity. The evaluation framework is designed based on network topological metrics which is extensible to all epidemic spreading model. Exploiting evaluation framework on well-known targeted immunization algorithms shows that in general, immunization based on PageRank centrality outperforms other targeting strategies in various types of networks, whereas, closeness and eigenvector centrality exhibit the worst case performance.
Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.
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Christian L Barrett
2006-05-01
Full Text Available The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments approximately 10(12 that can be performed, experiment design algorithms will be necessary for synthesizing the various computational and experimental data to maximize the efficiency of regulatory network reconstruction. This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge.
A computational study of routing algorithms for realistic transportation networks
Energy Technology Data Exchange (ETDEWEB)
Jacob, R.; Marathe, M.V.; Nagel, K.
1998-12-01
The authors carry out an experimental analysis of a number of shortest path (routing) algorithms investigated in the context of the TRANSIMS (Transportation Analysis and Simulation System) project. The main focus of the paper is to study how various heuristic and exact solutions, associated data structures affected the computational performance of the software developed especially for realistic transportation networks. For this purpose the authors have used Dallas Fort-Worth road network with very high degree of resolution. The following general results are obtained: (1) they discuss and experimentally analyze various one-one shortest path algorithms, which include classical exact algorithms studied in the literature as well as heuristic solutions that are designed to take into account the geometric structure of the input instances; (2) they describe a number of extensions to the basic shortest path algorithm. These extensions were primarily motivated by practical problems arising in TRANSIMS and ITS (Intelligent Transportation Systems) related technologies. Extensions discussed include--(i) time dependent networks, (ii) multi-modal networks, (iii) networks with public transportation and associated schedules. Computational results are provided to empirically compare the efficiency of various algorithms. The studies indicate that a modified Dijkstra`s algorithm is computationally fast and an excellent candidate for use in various transportation planning applications as well as ITS related technologies.
Fast search algorithms for computational protein design.
Traoré, Seydou; Roberts, Kyle E; Allouche, David; Donald, Bruce R; André, Isabelle; Schiex, Thomas; Barbe, Sophie
2016-05-05
One of the main challenges in computational protein design (CPD) is the huge size of the protein sequence and conformational space that has to be computationally explored. Recently, we showed that state-of-the-art combinatorial optimization technologies based on Cost Function Network (CFN) processing allow speeding up provable rigid backbone protein design methods by several orders of magnitudes. Building up on this, we improved and injected CFN technology into the well-established CPD package Osprey to allow all Osprey CPD algorithms to benefit from associated speedups. Because Osprey fundamentally relies on the ability of A* to produce conformations in increasing order of energy, we defined new A* strategies combining CFN lower bounds, with new side-chain positioning-based branching scheme. Beyond the speedups obtained in the new A*-CFN combination, this novel branching scheme enables a much faster enumeration of suboptimal sequences, far beyond what is reachable without it. Together with the immediate and important speedups provided by CFN technology, these developments directly benefit to all the algorithms that previously relied on the DEE/ A* combination inside Osprey* and make it possible to solve larger CPD problems with provable algorithms. © 2016 Wiley Periodicals, Inc.
Odd-graceful labeling algorithm and its implementation of generalized ring core network
Xie, Jianmin; Hong, Wenmei; Zhao, Tinggang; Yao, Bing
2017-08-01
The computer implementation of some labeling algorithms of special networks has practical guiding significance to computer communication network system design of functional, reliability, low communication cost. Generalized ring core network is a very important hybrid network topology structure and it is the basis of generalized ring network. In this paper, based on the requirements of research of generalized ring network addressing, the author has designed the odd-graceful labeling algorithm of generalized ring core network when n1, n2,…nm ≡ 0(mod 4), proved odd-graceful of the structure, worked out the corresponding software, and shown the practical effectiveness of this algorithm with our experimental data.
Genetic Algorithms in Wireless Networking: Techniques, Applications, and Issues
Mehboob, Usama; Qadir, Junaid; Ali, Salman; Vasilakos, Athanasios
2014-01-01
In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorit...
Designing liner shipping networks
J. Mulder (Judith); R. Dekker (Rommert)
2012-01-01
textabstractIn this paper the combined fleet-design, ship-scheduling and cargo-routing problem with limited availability of ships in liner shipping is considered. A genetic algorithm based solution method is proposed in which the ports are first aggregated into port cluster to reduce the problem
Mitigate Cascading Failures on Networks using a Memetic Algorithm.
Tang, Xianglong; Liu, Jing; Hao, Xingxing
2016-12-09
Research concerning cascading failures in complex networks has become a hot topic. However, most of the existing studies have focused on modelling the cascading phenomenon on networks and analysing network robustness from a theoretical point of view, which considers only the damage incurred by the failure of one or several nodes. However, such a theoretical approach may not be useful in practical situation. Thus, we first design a much more practical measure to evaluate the robustness of networks against cascading failures, termed Rcf. Then, adopting Rcf as the objective function, we propose a new memetic algorithm (MA) named MA-Rcf to enhance network the robustness against cascading failures. Moreover, we design a new local search operator that considers the characteristics of cascading failures and operates by connecting nodes with a high probability of having similar loads. In experiments, both synthetic scale-free networks and real-world networks are used to test the efficiency and effectiveness of the MA-Rcf. We systematically investigate the effects of parameters on the performance of the MA-Rcf and validate the performance of the newly designed local search operator. The results show that the local search operator is effective, that MA-Rcf can enhance network robustness against cascading failures efficiently, and that it outperforms existing algorithms.
Designing for Networked Communications
DEFF Research Database (Denmark)
Designing for Networked Communications: Strategies and Development explains how to plan, use, and understand the products and the dynamic social processes and tasks some of the most vital innovations in the knowledge society depend upon social as well as technological. Focusing on various forms...... of design, implementation and integration of computer mediated communication, this book bridges the academic fields of computer science and communication studies. Designing for Networked Communications: Strategies and Development uses an interdisciplinary approach, and presents results from recent...... and important research in a variety of forms for networked communications. A constructive and critical view of the interplay between the new electronic and the more conventional modes of communication are utilized, while studies of organizational work practices demonstrate that the use of new technologies...
Designing for Networked Communications
DEFF Research Database (Denmark)
of design, implementation and integration of computer mediated communication, this book bridges the academic fields of computer science and communication studies. Designing for Networked Communications: Strategies and Development uses an interdisciplinary approach, and presents results from recent......Designing for Networked Communications: Strategies and Development explains how to plan, use, and understand the products and the dynamic social processes and tasks some of the most vital innovations in the knowledge society depend upon social as well as technological. Focusing on various forms...... and important research in a variety of forms for networked communications. A constructive and critical view of the interplay between the new electronic and the more conventional modes of communication are utilized, while studies of organizational work practices demonstrate that the use of new technologies...
Computational network design from functional specifications
Peng, Chi Han
2016-07-11
Connectivity and layout of underlying networks largely determine agent behavior and usage in many environments. For example, transportation networks determine the flow of traffic in a neighborhood, whereas building floorplans determine the flow of people in a workspace. Designing such networks from scratch is challenging as even local network changes can have large global effects. We investigate how to computationally create networks starting from only high-level functional specifications. Such specifications can be in the form of network density, travel time versus network length, traffic type, destination location, etc. We propose an integer programming-based approach that guarantees that the resultant networks are valid by fulfilling all the specified hard constraints and that they score favorably in terms of the objective function. We evaluate our algorithm in two different design settings, street layout and floorplans to demonstrate that diverse networks can emerge purely from high-level functional specifications.
Optimization of Process Design Problems Using Differential Evolution Algorithm
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A.M. Gujarathia
2016-06-01
Full Text Available A modified differential evolution algorithm (MDE has been used for solving different process related design problems (namely calculation of the NRTL and Two-Suffix Margules activity coefficient models parameters in 20 ternary extraction systems including different ionic liquids and reactor network design problem. The obtained results, in terms of root mean square deviations (rmsd for these models are satisfactory, with the overall values of 0.0023 and 0.0170 for 169 tie-lines for NRTL and Two-Suffix Margules models, respectively. The results showed that the MDE algorithm results in better solutions compared to the previous work based on genetic algorithm (GA for correlating liquid-liquid equilibrium (LLE data in these systems. MDE also outperformed DE algorithm when tested on reactor network design problem with respect to convergence and speed.
Protein complexes predictions within protein interaction networks using genetic algorithms.
Ramadan, Emad; Naef, Ahmed; Ahmed, Moataz
2016-07-25
Protein-protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein-protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein-protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .
Designing algorithms using CAD technologies
Directory of Open Access Journals (Sweden)
Alin IORDACHE
2008-01-01
Full Text Available A representative example of eLearning-platform modular application, Ã¢Â€Â˜Logical diagramsÃ¢Â€Â™, is intended to be a useful learning and testing tool for the beginner programmer, but also for the more experienced one. The problem this application is trying to solve concerns young programmers who forget about the fundamentals of this domain, algorithmic. Logical diagrams are a graphic representation of an algorithm, which uses different geometrical figures (parallelograms, rectangles, rhombuses, circles with particular meaning that are called blocks and connected between them to reveal the flow of the algorithm. The role of this application is to help the user build the diagram for the algorithm and then automatically generate the C code and test it.
Designing Networks for Innovation
Laskowski, Paul Luke
2009-01-01
The last decades have seen tremendous growth and transformation in the Internet's commercial landscape. Underneath this success, however, the underlying network architecture has shown a marked resistance to change; it is now described as stagnant and ossified. Numerous design proposals have been developed by researchers, implemented in code, and…
Access Network Selection Based on Fuzzy Logic and Genetic Algorithms
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Mohammed Alkhawlani
2008-01-01
Full Text Available In the next generation of heterogeneous wireless networks (HWNs, a large number of different radio access technologies (RATs will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS provisioning. This paper proposes a general scheme to solve the access network selection (ANS problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL and genetic algorithms (GAs have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection.
Quantum-based algorithm for optimizing artificial neural networks.
Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang
2013-08-01
This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.
Evaluation of Topology-Aware Broadcast Algorithms for Dragonfly Networks
Energy Technology Data Exchange (ETDEWEB)
Dorier, Matthieu; Mubarak, Misbah; Ross, Rob; Li, Jianping Kelvin; Carothers, Christopher D.; Ma, Kwan-Liu
2016-09-12
Two-tiered direct network topologies such as Dragonflies have been proposed for future post-petascale and exascale machines, since they provide a high-radix, low-diameter, fast interconnection network. Such topologies call for redesigning MPI collective communication algorithms in order to attain the best performance. Yet as increasingly more applications share a machine, it is not clear how these topology-aware algorithms will react to interference with concurrent jobs accessing the same network. In this paper, we study three topology-aware broadcast algorithms, including one designed by ourselves. We evaluate their performance through event-driven simulation for small- and large-sized broadcasts (in terms of both data size and number of processes). We study the effect of different routing mechanisms on the topology-aware collective algorithms, as well as their sensitivity to network contention with other jobs. Our results show that while topology-aware algorithms dramatically reduce link utilization, their advantage in terms of latency is more limited.
a Strategy of Designing Routing Algorithms Based on Ideal Routings
Shinjo, K.; Shimogawa, S.; Yamada, J.; Oida, K.
This paper proposes a strategy of designing routing algorithms for connectionless packet-switched networks. This strategy consists of three design elements as follows: [A] the notion of ideal routings is introduced to provide the upper performance limits attained by improving routing algorithm and it serves as a standard to measure the performance of other algorithms; [B] a method of constructing simple algorithms is presented under implementation conditions from ideal routings; [C] a method is described to enhance the performance limits of [A]. By using these elements, simple algorithms with a maximum degree of performance attainment are realized. By "degree of performance attainment", we mean that we can see how much room is left for the improvement of algorithms. We develop [A] and [B] with the performance measures of throughput and average packet delay and the M/M/1 queuing network. We decide ideal static routings and their performance limits from [A]. We obtain a new simple algorithm from [B] based on the notion of the ideal routings in implementation conditions. The designed algorithm improves the throughput and the average delay, which are comparable to those from ideal static routings. This improvement is contrasted to the adaptive and distributed OSPF (Open Shortest Path First), a standard Internet routing protocol.
An Optimal Routing Algorithm in Service Customized 5G Networks
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Haipeng Yao
2016-01-01
Full Text Available With the widespread use of Internet, the scale of mobile data traffic grows explosively, which makes 5G networks in cellular networks become a growing concern. Recently, the ideas related to future network, for example, Software Defined Networking (SDN, Content-Centric Networking (CCN, and Big Data, have drawn more and more attention. In this paper, we propose a service-customized 5G network architecture by introducing the ideas of separation between control plane and data plane, in-network caching, and Big Data processing and analysis to resolve the problems traditional cellular radio networks face. Moreover, we design an optimal routing algorithm for this architecture, which can minimize average response hops in the network. Simulation results reveal that, by introducing the cache, the network performance can be obviously improved in different network conditions compared to the scenario without a cache. In addition, we explore the change of cache hit rate and average response hops under different cache replacement policies, cache sizes, content popularity, and network topologies, respectively.
Algorithmic Mechanism Design of Evolutionary Computation.
Pei, Yan
2015-01-01
We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm.
Algorithms for structural natural-frequency design
Levy, R.
1991-01-01
The algorithms used in the Jet Propulsion Laboratory (JPL) Iterative Design of Antenna Structures (IDEAS) program are furnished. The algorithms are based upon the operational research method of optimality criteria and the structural analysis method of virtual work. Examples of the natural frequency constrained design of an antenna tripod structure are included.
Network-based recommendation algorithms: A review
Yu, Fei; Zeng, An; Gillard, Sébastien; Medo, Matúš
2016-06-01
Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use-such as the possible influence of recommendation on the evolution of systems that use it-and finally discuss open research directions and challenges.
Information Dynamics in Networks: Models and Algorithms
2016-09-13
ICDCS). 29-JUN-15, Columbus, OH, USA. : , . Value-Based Network Externalities and Optimal Auction Design, Conference on Web and Internet Economics...NAME Total Number: NAME Total Number: PERCENT_SUPPORTEDNAME FTE Equivalent: Total Number: ...... ...... Inventions (DD882) Scientific Progress In...Value-based network externalities and optimal auction design. In Web and Internet Economics - 10th International Conference, WINE 2014, Beijing, China, December 14-17, pages 147–160, 2014. 6
A Differentiated Anonymity Algorithm for Social Network Privacy Preservation
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Yuqin Xie
2016-12-01
Full Text Available Devising methods to publish social network data in a form that affords utility without compromising privacy remains a longstanding challenge, while many existing methods based on k-anonymity algorithms on social networks may result in nontrivial utility loss without analyzing the social network topological structure and without considering the attributes of sparse distribution. Toward this objective, we explore the impact of the attributes of sparse distribution on data utility. Firstly, we propose a new utility metric that emphasizes network structure distortion and attribute value loss. Furthermore, we design and implement a differentiated k-anonymity l-diversity social network anonymity algorithm, which seeks to protect users’ privacy in social networks and increase the usability of the published anonymized data. Its key idea is that it divides a node into two child nodes and only anonymizes sensitive values to satisfy anonymity requirements. The evaluation results show that our method can effectively improve the data utility as compared to generalized anonymizing algorithms.
A new mutually reinforcing network node and link ranking algorithm.
Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E
2015-10-23
This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity.
A new mutually reinforcing network node and link ranking algorithm
Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E.
2015-10-01
This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity.
Algorithm For A Self-Growing Neural Network
Cios, Krzysztof J.
1996-01-01
CID3 algorithm simulates self-growing neural network. Constructs decision trees equivalent to hidden layers of neural network. Based on ID3 algorithm, which dynamically generates decision tree while minimizing entropy of information. CID3 algorithm generates feedforward neural network by use of either crisp or fuzzy measure of entropy.
Toward human-centered algorithm design
Directory of Open Access Journals (Sweden)
Eric PS Baumer
2017-07-01
Full Text Available As algorithms pervade numerous facets of daily life, they are incorporated into systems for increasingly diverse purposes. These systems’ results are often interpreted differently by the designers who created them than by the lay persons who interact with them. This paper offers a proposal for human-centered algorithm design, which incorporates human and social interpretations into the design process for algorithmically based systems. It articulates three specific strategies for doing so: theoretical, participatory, and speculative. Drawing on the author’s work designing and deploying multiple related systems, the paper provides a detailed example of using a theoretical approach. It also discusses findings pertinent to participatory and speculative design approaches. The paper addresses both strengths and challenges for each strategy in helping to center the process of designing algorithmically based systems around humans.
Solving Hub Network Problem Using Genetic Algorithm
Directory of Open Access Journals (Sweden)
Mursyid Hasan Basri
2012-01-01
Full Text Available This paper addresses a network problem that described as follows. There are n ports that interact, and p of those will be designated as hubs. All hubs are fully interconnected. Each spoke will be allocated to only one of available hubs. Direct connection between two spokes is allowed only if they are allocated to the same hub. The latter is a distinct characteristic that differs it from pure hub-and-spoke system. In case of pure hub-and-spoke system, direct connection between two spokes is not allowed. The problem is where to locate hub ports and to which hub a spoke should be allocated so that total transportation cost is minimum. In the first model, there are some additional aspects are taken into consideration in order to achieve a better representation of the problem. The first, weekly service should be accomplished. Secondly, various vessel types should be considered. The last, a concept of inter-hub discount factor is introduced. Regarding the last aspect, it represents cost reduction factor at hub ports due to economies of scale. In practice, it is common that the cost rate for inter-hub movement is less than the cost rate for movement between hub and origin/destination. In this first model, inter-hub discount factor is assumed independent with amount of flows on inter-hub links (denoted as flow-independent discount policy. The results indicated that the patterns of enlargement of container ship size, to some degree, are similar with those in Kurokawa study. However, with regard to hub locations, the results have not represented the real practice. In the proposed model, unsatisfactory result on hub locations is addressed. One aspect that could possibly be improved to find better hub locations is inter-hub discount factor. Then inter-hub discount factor is assumed to depend on amount of inter-hub flows (denoted as flow-dependent discount policy. There are two discount functions examined in this paper. Both functions are characterized by
Resistive Network Optimal Power Flow: Uniqueness and Algorithms
Energy Technology Data Exchange (ETDEWEB)
Tan, CW; Cai, DWH; Lou, X
2015-01-01
The optimal power flow (OPF) problem minimizes the power loss in an electrical network by optimizing the voltage and power delivered at the network buses, and is a nonconvex problem that is generally hard to solve. By leveraging a recent development on the zero duality gap of OPF, we propose a second-order cone programming convex relaxation of the resistive network OPF, and study the uniqueness of the optimal solution using differential topology, especially the Poincare-Hopf Index Theorem. We characterize the global uniqueness for different network topologies, e.g., line, radial, and mesh networks. This serves as a starting point to design distributed local algorithms with global behaviors that have low complexity, are computationally fast, and can run under synchronous and asynchronous settings in practical power grids.
Chou, Yu-Cheng; Lin, Po Ting
2015-10-01
The network of delivering commodities has been an important design problem in our daily lives and many transportation applications. The reliability of delivering commodities from a source node to a sink node in the network is maximised to find the optimal routing. However, the design problem is not simple due to randomly distributed attributes in each path, multiple commodities with variable path capacities and the allowable time constraints for delivery. This paper presents the design optimisation of the multi-state flow network (MSFN) for multiple commodities. We propose an efficient and robust approach to evaluate the system reliability in the MSFN with respect to randomly distributed path attributes and to find the optimal routing subject to the allowable time constraints. The delivery rates of the path segments are evaluated and the minimal-speed arcs are eliminated to reduce the complexity of the MSFN. Accordingly, the correct optimal routing is found and the worst-case reliability is evaluated. The reliability of the optimal routing is at least higher than worst-case measure. Three benchmark examples are utilised to demonstrate the proposed method. The comparisons between the original and the reduced networks show that the proposed method is very efficient.
Public traffic transfer algorithm design
Yin, Kui-Ying; Jin, Lin; Qian, Bo; Fang, Kai
The route relationship matrix M is employed in this paper to indicate relationship between routes. The travel solutions for bus stations are obtained by matrix operation. Through the data mining to the history record of transportation information, the road transportation status at specific time interval can be predicted. Based on the predication, the best travel solutions can be found. The algorithm fully uses history transportation information, optimizes the travel solutions according to the time consumed. It can satisfy the time requirement of traveler and is very useful.
A Decomposition Algorithm for Parametric Design
Jauregui Becker, Juan Manuel; Schotborgh, W.O.; van Houten, Frederikus J.A.M.; Culley, T.C.; Hicks, B.J.; McAloone, T.C.; Howard, T.J.; Dong, A.
2011-01-01
This paper presents a recursive division algorithm to decompose an under constraint parametric design problem. The algorithm defines the separation of the problem at the hand of two complexity measures that are calculated for each parameter in the problem, namely, the effort E and the influence Inf.
Localization Algorithms of Underwater Wireless Sensor Networks: A Survey
Directory of Open Access Journals (Sweden)
Yongjun Xu
2012-02-01
Full Text Available In Underwater Wireless Sensor Networks (UWSNs, localization is one of most important technologies since it plays a critical role in many applications. Motivated by widespread adoption of localization, in this paper, we present a comprehensive survey of localization algorithms. First, we classify localization algorithms into three categories based on sensor nodes’ mobility: stationary localization algorithms, mobile localization algorithms and hybrid localization algorithms. Moreover, we compare the localization algorithms in detail and analyze future research directions of localization algorithms in UWSNs.
Localization algorithms of Underwater Wireless Sensor Networks: a survey.
Han, Guangjie; Jiang, Jinfang; Shu, Lei; Xu, Yongjun; Wang, Feng
2012-01-01
In Underwater Wireless Sensor Networks (UWSNs), localization is one of most important technologies since it plays a critical role in many applications. Motivated by widespread adoption of localization, in this paper, we present a comprehensive survey of localization algorithms. First, we classify localization algorithms into three categories based on sensor nodes' mobility: stationary localization algorithms, mobile localization algorithms and hybrid localization algorithms. Moreover, we compare the localization algorithms in detail and analyze future research directions of localization algorithms in UWSNs.
Computing of network tenacity based on modified binary particle swarm optimization algorithm
Shen, Maoxing; Sun, Chengyu
2017-05-01
For rapid calculation of network node tenacity, which can depict the invulnerability performance of network, this paper designs a computational method based on modified binary particle swarm optimization (BPSO) algorithm. Firstly, to improve the astringency of the BPSO algorithm, the algorithm adopted an improved bit transfer probability function and location updating formula. Secondly, algorithm for fitness function value of BPSO based on the breadth-first search is designed. Thirdly, the computing method for network tenacity based on the modified BPSO algorithm is presented. Results of experiment conducted in the Advanced Research Project Agency (ARPA) network and Tactical Support Communication (TCS) network illustrate that the computing method is impactful and high-performance to calculate network tenacity.
An Improved Harmony Search Algorithm for Power Distribution Network Planning
Directory of Open Access Journals (Sweden)
Wei Sun
2015-01-01
Full Text Available Distribution network planning because of involving many variables and constraints is a multiobjective, discrete, nonlinear, and large-scale optimization problem. Harmony search (HS algorithm is a metaheuristic algorithm inspired by the improvisation process of music players. HS algorithm has several impressive advantages, such as easy implementation, less adjustable parameters, and quick convergence. But HS algorithm still has some defects such as premature convergence and slow convergence speed. According to the defects of the standard algorithm and characteristics of distribution network planning, an improved harmony search (IHS algorithm is proposed in this paper. We set up a mathematical model of distribution network structure planning, whose optimal objective function is to get the minimum annual cost and constraint conditions are overload and radial network. IHS algorithm is applied to solve the complex optimization mathematical model. The empirical results strongly indicate that IHS algorithm can effectively provide better results for solving the distribution network planning problem compared to other optimization algorithms.
Evolutionary Algorithms For Neural Networks Binary And Real Data Classification
Directory of Open Access Journals (Sweden)
Dr. Hanan A.R. Akkar
2015-08-01
Full Text Available Artificial neural networks are complex networks emulating the way human rational neurons process data. They have been widely used generally in prediction clustering classification and association. The training algorithms that used to determine the network weights are almost the most important factor that influence the neural networks performance. Recently many meta-heuristic and Evolutionary algorithms are employed to optimize neural networks weights to achieve better neural performance. This paper aims to use recently proposed algorithms for optimizing neural networks weights comparing these algorithms performance with other classical meta-heuristic algorithms used for the same purpose. However to evaluate the performance of such algorithms for training neural networks we examine such algorithms to classify four opposite binary XOR clusters and classification of continuous real data sets such as Iris and Ecoli.
Harmony search algorithms for structural design optimization
National Research Council Canada - National Science Library
Geem, Zong Woo
2009-01-01
... of these techniques is harmony search, an algorithm developed from musical improvisation that has been applied to various structural design problems and has demonstrated cost-savings. This book gathers all ...
Luo, Yaqi; Zeng, Bi
2017-08-01
This paper researches the drainage routing problem in drainage pipe network, and propose an intelligent scheduling method. The method relates to the design of improved particle swarm optimization algorithm, the establishment of the corresponding model from the pipe network, and the process by using the algorithm based on improved particle swarm optimization to find the optimum drainage route in the current environment.
A Decomposition Algorithm for Learning Bayesian Network Structures from Data
DEFF Research Database (Denmark)
Zeng, Yifeng; Cordero Hernandez, Jorge
2008-01-01
It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....
Neural-network-based voice-tracking algorithm
Baker, Mary; Stevens, Charise; Chaparro, Brennen; Paschall, Dwayne
2002-11-01
A voice-tracking algorithm was developed and tested for the purposes of electronically separating the voice signals of simultaneous talkers. Many individuals suffer from hearing disorders that often inhibit their ability to focus on a single speaker in a multiple speaker environment (the cocktail party effect). Digital hearing aid technology makes it possible to implement complex algorithms for speech processing in both the time and frequency domains. In this work, an average magnitude difference function (AMDF) was performed on mixed voice signals in order to determine the fundamental frequencies present in the signals. A time prediction neural network was trained to recognize normal human voice inflection patterns, including rising, falling, rising-falling, and falling-rising patterns. The neural network was designed to track the fundamental frequency of a single talker based on the training procedure. The output of the neural network can be used to design an active filter for speaker segregation. Tests were done using audio mixing of two to three speakers uttering short phrases. The AMDF function accurately identified the fundamental frequencies present in the signal. The neural network was tested using a single speaker uttering a short sentence. The network accurately tracked the fundamental frequency of the speaker.
SA-SOM algorithm for detecting communities in complex networks
Chen, Luogeng; Wang, Yanran; Huang, Xiaoming; Hu, Mengyu; Hu, Fang
2017-10-01
Currently, community detection is a hot topic. This paper, based on the self-organizing map (SOM) algorithm, introduced the idea of self-adaptation (SA) that the number of communities can be identified automatically, a novel algorithm SA-SOM of detecting communities in complex networks is proposed. Several representative real-world networks and a set of computer-generated networks by LFR-benchmark are utilized to verify the accuracy and the efficiency of this algorithm. The experimental findings demonstrate that this algorithm can identify the communities automatically, accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity, NMI and density than the SOM algorithm does.
Evolving neural networks using a genetic algorithm for heartbeat classification.
Sekkal, Mansouria; Chikh, Mohamed Amine; Settouti, Nesma
2011-07-01
This study investigates the effectiveness of a genetic algorithm (GA) evolved neural network (NN) classifier and its application to the classification of premature ventricular contraction (PVC) beats. As there is no standard procedure to determine the network structure for complicated cases, generally the design of the NN would be dependent on the user's experience. To prevent this problem, we propose a neural classifier that uses a GA for the determination of optimal connections between neurons for better recognition. The MIT-BIH arrhythmia database is employed to evaluate its accuracy. First, the topology of the NN was determined using the trial and error method. Second, the genetic operators were carefully designed to optimize the neural network structure. Performance and accuracy of the two techniques are presented and compared. Copyright © 2011 Informa UK, Ltd.
Optimized Design of Microresonators Using Genetic Algorithm
Directory of Open Access Journals (Sweden)
G.Uma
2006-10-01
Full Text Available This paper represents the optimization of micro resonator design using Genetic Algorithm. Optimized physical layout parameters are generated using genetic algorithm. Optimization evaluates parameter by minimizing active device area, electrostatic drive voltage or a weighted combination of area and drive voltage or by maximizing displacement at resonance. Desired resonant frequency and mode frequency separations are governed by the objective function. Layouts are generated for optimized design parameters using Coventorware. Modal analysis is performed and it is compared with the designed resonant frequency.
Algorithm validation and hardware design interactive approach
Lazarescu, Mihai Teodor; M. Sartori
1996-01-01
In this paper we will describe a modality to speed up the design of the VLSI digital (mainly DSP) circuits and to reduce the design errors by increasing the interaction between the ad-hoc software program developed to validate the algorithm and the VHDL description and simulation. A real case of a digital power analyzer will be used for exemplification
A simple and efficient algorithm for modeling modular complex networks
Kowalczyk, Mateusz; Fronczak, Piotr; Fronczak, Agata
2017-09-01
In this paper we introduce a new algorithm to generate networks in which node degrees and community sizes can follow any arbitrary distribution. We compare the quality and efficiency of the proposed algorithm and the well-known algorithm by Lancichinetti et al. In contrast to the later one, the new algorithm, at the cost of accuracy, allows to generate two orders of magnitude larger networks in a reasonable time and it can be easily described analytically.
Recurrent neural networks training with stable bounding ellipsoid algorithm.
Yu, Wen; de Jesús Rubio, José
2009-06-01
Bounding ellipsoid (BE) algorithms offer an attractive alternative to traditional training algorithms for neural networks, for example, backpropagation and least squares methods. The benefits include high computational efficiency and fast convergence speed. In this paper, we propose an ellipsoid propagation algorithm to train the weights of recurrent neural networks for nonlinear systems identification. Both hidden layers and output layers can be updated. The stability of the BE algorithm is proven.
Training product unit neural networks with genetic algorithms
Janson, D. J.; Frenzel, J. F.; Thelen, D. C.
1991-01-01
The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.
A Comparative Analysis of Community Detection Algorithms on Artificial Networks.
Yang, Zhao; Algesheimer, René; Tessone, Claudio J
2016-08-01
Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms' computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm's predicting power and the effective computing time.
Robustness of the ATLAS pixel clustering neural network algorithm
AUTHOR|(INSPIRE)INSPIRE-00407780; The ATLAS collaboration
2016-01-01
Proton-proton collisions at the energy frontier puts strong constraints on track reconstruction algorithms. In the ATLAS track reconstruction algorithm, an artificial neural network is utilised to identify and split clusters of neighbouring read-out elements in the ATLAS pixel detector created by multiple charged particles. The robustness of the neural network algorithm is presented, probing its sensitivity to uncertainties in the detector conditions. The robustness is studied by evaluating the stability of the algorithm's performance under a range of variations in the inputs to the neural networks. Within reasonable variation magnitudes, the neural networks prove to be robust to most variation types.
Secure Multicast Routing Algorithm for Wireless Mesh Networks
Directory of Open Access Journals (Sweden)
Rakesh Matam
2016-01-01
Full Text Available Multicast is an indispensable communication technique in wireless mesh network (WMN. Many applications in WMN including multicast TV, audio and video conferencing, and multiplayer social gaming use multicast transmission. On the other hand, security in multicast transmissions is crucial, without which the network services are significantly disrupted. Existing secure routing protocols that address different active attacks are still vulnerable due to subtle nature of flaws in protocol design. Moreover, existing secure routing protocols assume that adversarial nodes cannot share an out-of-band communication channel which rules out the possibility of wormhole attack. In this paper, we propose SEMRAW (SEcure Multicast Routing Algorithm for Wireless mesh network that is resistant against all known active threats including wormhole attack. SEMRAW employs digital signatures to prevent a malicious node from gaining illegitimate access to the message contents. Security of SEMRAW is evaluated using the simulation paradigm approach.
An Efficient Hierarchy Algorithm for Community Detection in Complex Networks
Directory of Open Access Journals (Sweden)
Lili Zhang
2014-01-01
Full Text Available Community structure is one of the most fundamental and important topology characteristics of complex networks. The research on community structure has wide applications and is very important for analyzing the topology structure, understanding the functions, finding the hidden properties, and forecasting the time-varying of the networks. This paper analyzes some related algorithms and proposes a new algorithm—CN agglomerative algorithm based on graph theory and the local connectedness of network to find communities in network. We show this algorithm is distributed and polynomial; meanwhile the simulations show it is accurate and fine-grained. Furthermore, we modify this algorithm to get one modified CN algorithm and apply it to dynamic complex networks, and the simulations also verify that the modified CN algorithm has high accuracy too.
Application of Different Algorithms to Optimal Design of Canal Sections
Directory of Open Access Journals (Sweden)
A. Kentli
2014-08-01
Full Text Available Today the increased world population and therefore the growth demand has forced the researchers to investigate better water canal networks distributing much more water while at least keeping its quality. Canal design formulas are explicitly obtained for different cross-sections considering minimum area but optimal design of canal sections considering seepage and evaporation losses are still an open area to study. In this study, two different algorithms are applied to this problem and results are compared with the one in literature. Genetic algorithm and sequential quadratic programming technique are used in optimization. Triangular, rectangular and trapezoidal cross-sections are optimized. It is seen that both algorithms are giving more accurate results than in literature.
Algorithms for energy efficiency in wireless sensor networks
Energy Technology Data Exchange (ETDEWEB)
Busse, M.
2007-01-21
The recent advances in microsensor and semiconductor technology have opened a new field within computer science: the networking of small-sized sensors which are capable of sensing, processing, and communicating. Such wireless sensor networks offer new applications in the areas of habitat and environment monitoring, disaster control and operation, military and intelligence control, object tracking, video surveillance, traffic control, as well as in health care and home automation. It is likely that the deployed sensors will be battery-powered, which will limit the energy capacity significantly. Thus, energy efficiency becomes one of the main challenges that need to be taken into account, and the design of energy-efficient algorithms is a major contribution of this thesis. As the wireless communication in the network is one of the main energy consumers, we first consider in detail the characteristics of wireless communication. By using the embedded sensor board (ESB) platform recently developed by the Free University of Berlin, we analyze the means of forward error correction and propose an appropriate resync mechanism, which improves the communication between two ESB nodes substantially. Afterwards, we focus on the forwarding of data packets through the network. We present the algorithms energy-efficient forwarding (EEF), lifetime-efficient forwarding (LEF), and energy-efficient aggregation forwarding (EEAF). While EEF is designed to maximize the number of data bytes delivered per energy unit, LEF additionally takes into account the residual energy of forwarding nodes. In so doing, LEF further prolongs the lifetime of the network. Energy savings due to data aggregation and in-network processing are exploited by EEAF. Besides single-link forwarding, in which data packets are sent to only one forwarding node, we also study the impact of multi-link forwarding, which exploits the broadcast characteristics of the wireless medium by sending packets to several (potential
Study of Vivaldi Algorithm in Energy Constraint Networks
Directory of Open Access Journals (Sweden)
Tomas Handl
2011-01-01
Full Text Available The presented paper discusses a viability of Vivaldi localization algorithm and synthetic coordinate system in general to be used for localization purposes in energy constraint networks. Synthetic coordinate systems achieve good results in IP based networks and thus, it could be a perspective way of node localization in other types of networks. However, transfer of Vivaldi algorithm into a different kind of network is a difficult task because the different basic characteristic of the network and network nodes. In this paper we focus on the different aspects of IP based networks and networks of wireless sensors which suffer from strict energy limitation. During our work we proposed a modified version of two dimensional Vivaldi localization algorithm with height system and developed a simulator tool for initial investigation of its function in ad-hoc energy constraint networks.
A Location-Aware Vertical Handoff Algorithm for Hybrid Networks
Mehbodniya, Abolfazl
2010-07-01
One of the main objectives of wireless networking is to provide mobile users with a robust connection to different networks so that they can move freely between heterogeneous networks while running their computing applications with no interruption. Horizontal handoff, or generally speaking handoff, is a process which maintains a mobile user\\'s active connection as it moves within a wireless network, whereas vertical handoff (VHO) refers to handover between different types of networks or different network layers. Optimizing VHO process is an important issue, required to reduce network signalling and mobile device power consumption as well as to improve network quality of service (QoS) and grade of service (GoS). In this paper, a VHO algorithm in multitier (overlay) networks is proposed. This algorithm uses pattern recognition to estimate user\\'s position, and decides on the handoff based on this information. For the pattern recognition algorithm structure, the probabilistic neural network (PNN) which has considerable simplicity and efficiency over existing pattern classifiers is used. Further optimization is proposed to improve the performance of the PNN algorithm. Performance analysis and comparisons with the existing VHO algorithm are provided and demonstrate a significant improvement with the proposed algorithm. Furthermore, incorporating the proposed algorithm, a structure is proposed for VHO from the medium access control (MAC) layer point of view. © 2010 ACADEMY PUBLISHER.
Network Analysis, Architecture, and Design
McCabe, James D
2007-01-01
Traditionally, networking has had little or no basis in analysis or architectural development, with designers relying on technologies they are most familiar with or being influenced by vendors or consultants. However, the landscape of networking has changed so that network services have now become one of the most important factors to the success of many third generation networks. It has become an important feature of the designer's job to define the problems that exist in his network, choose and analyze several optimization parameters during the analysis process, and then prioritize and evalua
Understanding and designing computer networks
King, Graham
1995-01-01
Understanding and Designing Computer Networks considers the ubiquitous nature of data networks, with particular reference to internetworking and the efficient management of all aspects of networked integrated data systems. In addition it looks at the next phase of networking developments; efficiency and security are covered in the sections dealing with data compression and data encryption; and future examples of network operations, such as network parallelism, are introduced.A comprehensive case study is used throughout the text to apply and illustrate new techniques and concepts as th
2013-09-01
end if 8: end forreturn BroadcastCosti In algorithm 1, we first create a graph using the Dijkstra algorithm [9], with the candidate switch as the root...me to successfully navigate through this program and complete my studies. Words cannot describe my gratitude to both my wife and son for everything...expertise and sound guidance helped me navigate and successfully complete the thesis process. Your technical insight and encouragement helped make this
BOUNDARY DETECTION ALGORITHMS IN WIRELESS SENSOR NETWORKS: A SURVEY
Directory of Open Access Journals (Sweden)
Lanny Sitanayah
2009-01-01
Full Text Available Wireless sensor networks (WSNs comprise a large number of sensor nodes, which are spread out within a region and communicate using wireless links. In some WSN applications, recognizing boundary nodes is important for topology discovery, geographic routing and tracking. In this paper, we study the problem of recognizing the boundary nodes of a WSN. We firstly identify the factors that influence the design of algorithms for boundary detection. Then, we classify the existing work in boundary detection, which is vital for target tracking to detect when the targets enter or leave the sensor field.
Competitive Liner Shipping Network Design
DEFF Research Database (Denmark)
Karsten, Christian Vad; Brouer, Berit Dangaard; Pisinger, David
2017-01-01
leg are used to balance sailing speed against operational costs, hence ensuring that the found network is competitive on both transit time and cost. We present a matheuristic for the problem where a MIP is used to select which ports should be inserted or removed on a route. Computational results...... on fluctuations in bunker price which confirms the applicability of the algorithm....
Enhanced Handover Decision Algorithm in Heterogeneous Wireless Network.
Abdullah, Radhwan Mohamed; Zukarnain, Zuriati Ahmad
2017-07-14
Transferring a huge amount of data between different network locations over the network links depends on the network's traffic capacity and data rate. Traditionally, a mobile device may be moved to achieve the operations of vertical handover, considering only one criterion, that is the Received Signal Strength (RSS). The use of a single criterion may cause service interruption, an unbalanced network load and an inefficient vertical handover. In this paper, we propose an enhanced vertical handover decision algorithm based on multiple criteria in the heterogeneous wireless network. The algorithm consists of three technology interfaces: Long-Term Evolution (LTE), Worldwide interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN). It also employs three types of vertical handover decision algorithms: equal priority, mobile priority and network priority. The simulation results illustrate that the three types of decision algorithms outperform the traditional network decision algorithm in terms of handover number probability and the handover failure probability. In addition, it is noticed that the network priority handover decision algorithm produces better results compared to the equal priority and the mobile priority handover decision algorithm. Finally, the simulation results are validated by the analytical model.
An Energy Efficient Multipath Routing Algorithm for Wireless Sensor Networks
Dulman, S.O.; Wu Jian, W.J.; Havinga, Paul J.M.
In this paper we introduce a new routing algorithm for wireless sensor networks. The aim of this algorithm is to provide on-demand multiple disjoint paths between a data source and a destination. Our Multipath On-Demand Routing Algorithm (MDR) improves the reliability of data routing in a wireless
Spectrum Assignment Algorithm for Cognitive Machine-to-Machine Networks
Directory of Open Access Journals (Sweden)
Soheil Rostami
2016-01-01
Full Text Available A novel aggregation-based spectrum assignment algorithm for Cognitive Machine-To-Machine (CM2M networks is proposed. The introduced algorithm takes practical constraints including interference to the Licensed Users (LUs, co-channel interference (CCI among CM2M devices, and Maximum Aggregation Span (MAS into consideration. Simulation results show clearly that the proposed algorithm outperforms State-Of-The-Art (SOTA algorithms in terms of spectrum utilisation and network capacity. Furthermore, the convergence analysis of the proposed algorithm verifies its high convergence rate.
Adaptive clustering algorithm for community detection in complex networks
Ye, Zhenqing; Hu, Songnian; Yu, Jun
2008-10-01
Community structure is common in various real-world networks; methods or algorithms for detecting such communities in complex networks have attracted great attention in recent years. We introduced a different adaptive clustering algorithm capable of extracting modules from complex networks with considerable accuracy and robustness. In this approach, each node in a network acts as an autonomous agent demonstrating flocking behavior where vertices always travel toward their preferable neighboring groups. An optimal modular structure can emerge from a collection of these active nodes during a self-organization process where vertices constantly regroup. In addition, we show that our algorithm appears advantageous over other competing methods (e.g., the Newman-fast algorithm) through intensive evaluation. The applications in three real-world networks demonstrate the superiority of our algorithm to find communities that are parallel with the appropriate organization in reality.
A generic algorithm for layout of biological networks.
Schreiber, Falk; Dwyer, Tim; Marriott, Kim; Wybrow, Michael
2009-11-12
Biological networks are widely used to represent processes in biological systems and to capture interactions and dependencies between biological entities. Their size and complexity is steadily increasing due to the ongoing growth of knowledge in the life sciences. To aid understanding of biological networks several algorithms for laying out and graphically representing networks and network analysis results have been developed. However, current algorithms are specialized to particular layout styles and therefore different algorithms are required for each kind of network and/or style of layout. This increases implementation effort and means that new algorithms must be developed for new layout styles. Furthermore, additional effort is necessary to compose different layout conventions in the same diagram. Also the user cannot usually customize the placement of nodes to tailor the layout to their particular need or task and there is little support for interactive network exploration. We present a novel algorithm to visualize different biological networks and network analysis results in meaningful ways depending on network types and analysis outcome. Our method is based on constrained graph layout and we demonstrate how it can handle the drawing conventions used in biological networks. The presented algorithm offers the ability to produce many of the fundamental popular drawing styles while allowing the exibility of constraints to further tailor these layouts.
A generic algorithm for layout of biological networks
Directory of Open Access Journals (Sweden)
Dwyer Tim
2009-11-01
Full Text Available Abstract Background Biological networks are widely used to represent processes in biological systems and to capture interactions and dependencies between biological entities. Their size and complexity is steadily increasing due to the ongoing growth of knowledge in the life sciences. To aid understanding of biological networks several algorithms for laying out and graphically representing networks and network analysis results have been developed. However, current algorithms are specialized to particular layout styles and therefore different algorithms are required for each kind of network and/or style of layout. This increases implementation effort and means that new algorithms must be developed for new layout styles. Furthermore, additional effort is necessary to compose different layout conventions in the same diagram. Also the user cannot usually customize the placement of nodes to tailor the layout to their particular need or task and there is little support for interactive network exploration. Results We present a novel algorithm to visualize different biological networks and network analysis results in meaningful ways depending on network types and analysis outcome. Our method is based on constrained graph layout and we demonstrate how it can handle the drawing conventions used in biological networks. Conclusion The presented algorithm offers the ability to produce many of the fundamental popular drawing styles while allowing the exibility of constraints to further tailor these layouts.
Directory of Open Access Journals (Sweden)
Maryam Shokouhi
2017-06-01
Full Text Available Distribution networks are designed as a ring and operated as a radial form. Therefore, the reconfiguration is a simple and cost-effective way to use existing facilities without the need for any new equipment in distribution networks to achieve various objectives such as: power loss reduction, feeder overload reduction, load balancing, voltage profile improvement, reducing the number of switching considering constraints that ultimately result in the power loss reduction. In this paper, a new method based on the Ant Lion algorithm (a modern meta-heuristic algorithm is provided for the reconfiguration of distribution networks. Considering the extension of the distribution networks and complexity of their communications networks, and the various parameters, using smart techniques is inevitable. The proposed approach is tested on the IEEE 33 & 69-bus radial standard distribution networks. The Evaluation of results in MATLAB software shows the effectiveness of the Ant Lion algorithm in the distribution network reconfiguration.
The Generalized Fixed-Charge Network Design Problem
DEFF Research Database (Denmark)
Thomadsen, Tommy; Stidsen, Thomas K.
2007-01-01
In this paper we present the generalized fixed-charge network design (GFCND) problem. The GFCND problem is an instance of the so-called generalized network design problems. In such problems, clusters instead of nodes have to be interconnected by a network. The network interconnecting the clusters...... is a fixed-charge network, and thus the GFCND problem generalizes the fixed-charge network design problem. The GFCND problem is related to the more general problem of designing hierarchical telecommunication networks. A mixed integer programming model is described and a branch-cut-and-price algorithm...... is implemented. Violated constraints and variables with negative reduced costs are found using enumeration. The algorithm is capable of obtaining optimal solutions for problems with up to 30 clusters and up to 300 nodes. This is possible, since the linear programming relaxation bound is very tight...
Learning algorithms for feedforward networks based on finite samples
Energy Technology Data Exchange (ETDEWEB)
Rao, N.S.V.; Protopopescu, V.; Mann, R.C.; Oblow, E.M.; Iyengar, S.S.
1994-09-01
Two classes of convergent algorithms for learning continuous functions (and also regression functions) that are represented by feedforward networks, are discussed. The first class of algorithms, applicable to networks with unknown weights located only in the output layer, is obtained by utilizing the potential function methods of Aizerman et al. The second class, applicable to general feedforward networks, is obtained by utilizing the classical Robbins-Monro style stochastic approximation methods. Conditions relating the sample sizes to the error bounds are derived for both classes of algorithms using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can be directly adapted to concept learning problems.
An artificial immune system algorithm approach for reconfiguring distribution network
Syahputra, Ramadoni; Soesanti, Indah
2017-08-01
This paper proposes an artificial immune system (AIS) algorithm approach for reconfiguring distribution network with the presence distributed generators (DG). The distribution network with high-performance is a network that has a low power loss, better voltage profile, and loading balance among feeders. The task for improving the performance of the distribution network is optimization of network configuration. The optimization has become a necessary study with the presence of DG in entire networks. In this work, optimization of network configuration is based on an AIS algorithm. The methodology has been tested in a model of 33 bus IEEE radial distribution networks with and without DG integration. The results have been showed that the optimal configuration of the distribution network is able to reduce power loss and to improve the voltage profile of the distribution network significantly.
FAST ZEROX ALGORITHM FOR ROUTING IN OPTICAL MULTISTAGE INTERCONNECTION NETWORKS
Directory of Open Access Journals (Sweden)
T. D. Shahida
2010-05-01
Full Text Available Based on the ZeroX algorithm, a fast and efficient crosstalk-free time- domain algorithm called the Fast ZeroX or shortly FastZ_X algorithm is proposed for solving optical crosstalk problem in optical Omega multistage interconnection networks. A new pre-routing technique called the inverse Conflict Matrix (iCM is also introduced to map all possible conflicts identified between each node in the network as another representation of the standard conflict matrix commonly used in previous Zero-based algorithms. It is shown that using the new iCM, the original ZeroX algorithm is simplified, thus improved the algorithm by reducing the time to complete routing process. Through simulation modeling, the new approach yields the best performance in terms of minimal routing time in comparison to the original ZeroX algorithm as well as previous algorithms tested for comparison in this paper.
Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network.
Hao, Chuangbo; Song, Ping; Yang, Cheng; Liu, Xiongjun
2017-03-08
Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links.
Power control algorithms for mobile ad hoc networks
Directory of Open Access Journals (Sweden)
Nuraj L. Pradhan
2011-07-01
We will also focus on an adaptive distributed power management (DISPOW algorithm as an example of the multi-parameter optimization approach which manages the transmit power of nodes in a wireless ad hoc network to preserve network connectivity and cooperatively reduce interference. We will show that the algorithm in a distributed manner builds a unique stable network topology tailored to its surrounding node density and propagation environment over random topologies in a dynamic mobile wireless channel.
RNEDE: Resilient Network Design Environment
Energy Technology Data Exchange (ETDEWEB)
Venkat Venkatasubramanian, Tanu Malik, Arun Giridh; Craig Rieger; Keith Daum; Miles McQueen
2010-08-01
Modern living is more and more dependent on the intricate web of critical infrastructure systems. The failure or damage of such systems can cause huge disruptions. Traditional design of this web of critical infrastructure systems was based on the principles of functionality and reliability. However, it is increasingly being realized that such design objectives are not sufficient. Threats, disruptions and faults often compromise the network, taking away the benefits of an efficient and reliable design. Thus, traditional network design parameters must be combined with self-healing mechanisms to obtain a resilient design of the network. In this paper, we present RNEDEa resilient network design environment that that not only optimizes the network for performance but tolerates fluctuations in its structure that result from external threats and disruptions. The environment evaluates a set of remedial actions to bring a compromised network to an optimal level of functionality. The environment includes a visualizer that enables the network administrator to be aware of the current state of the network and the suggested remedial actions at all times.
A Network Selection Algorithm Considering Power Consumption in Hybrid Wireless Networks
Joe, Inwhee; Kim, Won-Tae; Hong, Seokjoon
In this paper, we propose a novel network selection algorithm considering power consumption in hybrid wireless networks for vertical handover. CDMA, WiBro, WLAN networks are candidate networks for this selection algorithm. This algorithm is composed of the power consumption prediction algorithm and the final network selection algorithm. The power consumption prediction algorithm estimates the expected lifetime of the mobile station based on the current battery level, traffic class and power consumption for each network interface card of the mobile station. If the expected lifetime of the mobile station in a certain network is not long enough compared the handover delay, this particular network will be removed from the candidate network list, thereby preventing unnecessary handovers in the preprocessing procedure. On the other hand, the final network selection algorithm consists of AHP (Analytic Hierarchical Process) and GRA (Grey Relational Analysis). The global factors of the network selection structure are QoS, cost and lifetime. If user preference is lifetime, our selection algorithm selects the network that offers longest service duration due to low power consumption. Also, we conduct some simulations using the OPNET simulation tool. The simulation results show that the proposed algorithm provides longer lifetime in the hybrid wireless network environment.
Networking systems design and development
Chao, Lee
2009-01-01
Effectively integrating theory and hands-on practice, Networking Systems Design and Development provides students and IT professionals with the knowledge and skills needed to design, implement, and manage fully functioning network systems using readily available Linux networking tools. Recognizing that most students are beginners in the field of networking, the text provides step-by-step instruction for setting up a virtual lab environment at home. Grounded in real-world applications, this book provides the ideal blend of conceptual instruction and lab work to give students and IT professional
Feed Forward Neural Network Algorithm for Frequent Patterns Mining
Dr. K.R.Pardasani; Sanjay Sharma; Amit Bhagat
2010-01-01
Association rule mining is used to find relationships among items in large data sets. Frequent patterns mining is an important aspect in association rule mining. In this paper, an efficient algorithm named Apriori-Feed Forward(AFF) based on Apriori algorithm and the Feed Forward Neural Network is presented to mine frequent patterns. Apriori algorithm scans database many times to generate frequent itemsets whereas Apriori-Feed Forward(AFF) algorithm scans database Only Once. Computational resu...
Multidisciplinary design optimization using genetic algorithms
Unal, Resit
1994-01-01
Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared
Redundant Design in Interdependent Networks.
Directory of Open Access Journals (Sweden)
Lijun Liu
Full Text Available Modern infrastructure networks are often coupled together and thus could be modeled as interdependent networks. Overload and interdependent effect make interdependent networks more fragile when suffering from attacks. Existing research has primarily concentrated on the cascading failure process of interdependent networks without load, or the robustness of isolated network with load. Only limited research has been done on the cascading failure process caused by overload in interdependent networks. Redundant design is a primary approach to enhance the reliability and robustness of the system. In this paper, we propose two redundant methods, node back-up and dependency redundancy, and the experiment results indicate that two measures are effective and costless. Two detailed models about redundant design are introduced based on the non-linear load-capacity model. Based on the attributes and historical failure distribution of nodes, we introduce three static selecting strategies-Random-based, Degree-based, Initial load-based and a dynamic strategy-HFD (historical failure distribution to identify which nodes could have a back-up with priority. In addition, we consider the cost and efficiency of different redundant proportions to determine the best proportion with maximal enhancement and minimal cost. Experiments on interdependent networks demonstrate that the combination of HFD and dependency redundancy is an effective and preferred measure to implement redundant design on interdependent networks. The results suggest that the redundant design proposed in this paper can permit construction of highly robust interactive networked systems.
Hybrid fuzzy charged system search algorithm based state estimation in distribution networks
Directory of Open Access Journals (Sweden)
Sachidananda Prasad
2017-06-01
Full Text Available This paper proposes a new hybrid charged system search (CSS algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS algorithm and particle swarm optimization (PSO for feasibility of the algorithm.
Congested Link Inference Algorithms in Dynamic Routing IP Network
Directory of Open Access Journals (Sweden)
Yu Chen
2017-01-01
Full Text Available The performance descending of current congested link inference algorithms is obviously in dynamic routing IP network, such as the most classical algorithm CLINK. To overcome this problem, based on the assumptions of Markov property and time homogeneity, we build a kind of Variable Structure Discrete Dynamic Bayesian (VSDDB network simplified model of dynamic routing IP network. Under the simplified VSDDB model, based on the Bayesian Maximum A Posteriori (BMAP and Rest Bayesian Network Model (RBNM, we proposed an Improved CLINK (ICLINK algorithm. Considering the concurrent phenomenon of multiple link congestion usually happens, we also proposed algorithm CLILRS (Congested Link Inference algorithm based on Lagrangian Relaxation Subgradient to infer the set of congested links. We validated our results by the experiments of analogy, simulation, and actual Internet.
Analysis of Community Detection Algorithms for Large Scale Cyber Networks
Energy Technology Data Exchange (ETDEWEB)
Mane, Prachita; Shanbhag, Sunanda; Kamath, Tanmayee; Mackey, Patrick S.; Springer, John
2016-09-30
The aim of this project is to use existing community detection algorithms on an IP network dataset to create supernodes within the network. This study compares the performance of different algorithms on the network in terms of running time. The paper begins with an introduction to the concept of clustering and community detection followed by the research question that the team aimed to address. Further the paper describes the graph metrics that were considered in order to shortlist algorithms followed by a brief explanation of each algorithm with respect to the graph metric on which it is based. The next section in the paper describes the methodology used by the team in order to run the algorithms and determine which algorithm is most efficient with respect to running time. Finally, the last section of the paper includes the results obtained by the team and a conclusion based on those results as well as future work.
Bayesian network structure learning using chaos hybrid genetic algorithm
Shen, Jiajie; Lin, Feng; Sun, Wei; Chang, KC
2012-06-01
A new Bayesian network (BN) learning method using a hybrid algorithm and chaos theory is proposed. The principles of mutation and crossover in genetic algorithm and the cloud-based adaptive inertia weight were incorporated into the proposed simple particle swarm optimization (sPSO) algorithm to achieve better diversity, and improve the convergence speed. By means of ergodicity and randomicity of chaos algorithm, the initial network structure population is generated by using chaotic mapping with uniform search under structure constraints. When the algorithm converges to a local minimal, a chaotic searching is started to skip the local minima and to identify a potentially better network structure. The experiment results show that this algorithm can be effectively used for BN structure learning.
Multidimensional Scaling Localization Algorithm in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Zhang Dongyang
2014-02-01
Full Text Available Due to the localization algorithm in large-scale wireless sensor network exists shortcomings both in positioning accuracy and time complexity compared to traditional localization algorithm, this paper presents a fast multidimensional scaling location algorithm. By positioning algorithm for fast multidimensional scaling, fast mapping initialization, fast mapping and coordinate transform can get schematic coordinates of node, coordinates Initialize of MDS algorithm, an accurate estimate of the node coordinates and using the PRORUSTES to analysis alignment of the coordinate and final position coordinates of nodes etc. There are four steps, and the thesis gives specific implementation steps of the algorithm. Finally, compared with stochastic algorithms and classical MDS algorithm experiment, the thesis takes application of specific examples. Experimental results show that: the proposed localization algorithm has fast multidimensional scaling positioning accuracy in ensuring certain circumstances, but also greatly improves the speed of operation.
Automated Antenna Design with Evolutionary Algorithms
Hornby, Gregory S.; Globus, Al; Linden, Derek S.; Lohn, Jason D.
2006-01-01
Current methods of designing and optimizing antennas by hand are time and labor intensive, and limit complexity. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions. In recent years, evolutionary algorithms have shown great promise in finding practical solutions in large, poorly understood design spaces. In particular, spacecraft antenna design has proven tractable to evolutionary design techniques. Researchers have been investigating evolutionary antenna design and optimization since the early 1990s, and the field has grown in recent years as computer speed has increased and electromagnetic simulators have improved. Two requirements-compliant antennas, one for ST5 and another for TDRS-C, have been automatically designed by evolutionary algorithms. The ST5 antenna is slated to fly this year, and a TDRS-C phased array element has been fabricated and tested. Such automated evolutionary design is enabled by medium-to-high quality simulators and fast modern computers to evaluate computer-generated designs. Evolutionary algorithms automate cut-and-try engineering, substituting automated search though millions of potential designs for intelligent search by engineers through a much smaller number of designs. For evolutionary design, the engineer chooses the evolutionary technique, parameters and the basic form of the antenna, e.g., single wire for ST5 and crossed-element Yagi for TDRS-C. Evolutionary algorithms then search for optimal configurations in the space defined by the engineer. NASA's Space Technology 5 (ST5) mission will launch three small spacecraft to test innovative concepts and technologies. Advanced evolutionary algorithms were used to automatically design antennas for ST5. The combination of wide beamwidth for a circularly-polarized wave and wide impedance bandwidth made for a challenging antenna design problem. From past experience in designing wire antennas, we chose to
Predicting resistance mutations using protein design algorithms.
Frey, Kathleen M; Georgiev, Ivelin; Donald, Bruce R; Anderson, Amy C
2010-08-03
Drug resistance resulting from mutations to the target is an unfortunate common phenomenon that limits the lifetime of many of the most successful drugs. In contrast to the investigation of mutations after clinical exposure, it would be powerful to be able to incorporate strategies early in the development process to predict and overcome the effects of possible resistance mutations. Here we present a unique prospective application of an ensemble-based protein design algorithm, K*, to predict potential resistance mutations in dihydrofolate reductase from Staphylococcus aureus using positive design to maintain catalytic function and negative design to interfere with binding of a lead inhibitor. Enzyme inhibition assays show that three of the four highly-ranked predicted mutants are active yet display lower affinity (18-, 9-, and 13-fold) for the inhibitor. A crystal structure of the top-ranked mutant enzyme validates the predicted conformations of the mutated residues and the structural basis of the loss of potency. The use of protein design algorithms to predict resistance mutations could be incorporated in a lead design strategy against any target that is susceptible to mutational resistance.
An improved localization algorithm based on genetic algorithm in wireless sensor networks.
Peng, Bo; Li, Lei
2015-04-01
Wireless sensor network (WSN) are widely used in many applications. A WSN is a wireless decentralized structure network comprised of nodes, which autonomously set up a network. The node localization that is to be aware of position of the node in the network is an essential part of many sensor network operations and applications. The existing localization algorithms can be classified into two categories: range-based and range-free. The range-based localization algorithm has requirements on hardware, thus is expensive to be implemented in practice. The range-free localization algorithm reduces the hardware cost. Because of the hardware limitations of WSN devices, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is a typical range-free localization algorithm utilizing hop-distance estimation. In this paper, we propose an improved DV-Hop algorithm based on genetic algorithm. Simulation results show that our proposed algorithm improves the localization accuracy compared with previous algorithms.
Uhr, Leonard
1984-01-01
Computer Science and Applied Mathematics: Algorithm-Structured Computer Arrays and Networks: Architectures and Processes for Images, Percepts, Models, Information examines the parallel-array, pipeline, and other network multi-computers.This book describes and explores arrays and networks, those built, being designed, or proposed. The problems of developing higher-level languages for systems and designing algorithm, program, data flow, and computer structure are also discussed. This text likewise describes several sequences of successively more general attempts to combine the power of arrays wi
Hierarchical Ring Network Design Using Branch-and-Price
DEFF Research Database (Denmark)
Thomadsen, Tommy; Stidsen, Thomas K.
2005-01-01
link establishment costs and the link capacity costs into account. Hierarchical ring network design problems combines the following optimization problems: Clustering, hub selection, metro ring design, federal ring design and routing problems. In this paper a branch-and-price algorithm is presented...... for jointly solving the clustering problem, the metro ring design problem and the routing problem. Computational results are given for networks with up to 36 nodes....
EGNAS: an exhaustive DNA sequence design algorithm
Directory of Open Access Journals (Sweden)
Kick Alfred
2012-06-01
Full Text Available Abstract Background The molecular recognition based on the complementary base pairing of deoxyribonucleic acid (DNA is the fundamental principle in the fields of genetics, DNA nanotechnology and DNA computing. We present an exhaustive DNA sequence design algorithm that allows to generate sets containing a maximum number of sequences with defined properties. EGNAS (Exhaustive Generation of Nucleic Acid Sequences offers the possibility of controlling both interstrand and intrastrand properties. The guanine-cytosine content can be adjusted. Sequences can be forced to start and end with guanine or cytosine. This option reduces the risk of “fraying” of DNA strands. It is possible to limit cross hybridizations of a defined length, and to adjust the uniqueness of sequences. Self-complementarity and hairpin structures of certain length can be avoided. Sequences and subsequences can optionally be forbidden. Furthermore, sequences can be designed to have minimum interactions with predefined strands and neighboring sequences. Results The algorithm is realized in a C++ program. TAG sequences can be generated and combined with primers for single-base extension reactions, which were described for multiplexed genotyping of single nucleotide polymorphisms. Thereby, possible foldback through intrastrand interaction of TAG-primer pairs can be limited. The design of sequences for specific attachment of molecular constructs to DNA origami is presented. Conclusions We developed a new software tool called EGNAS for the design of unique nucleic acid sequences. The presented exhaustive algorithm allows to generate greater sets of sequences than with previous software and equal constraints. EGNAS is freely available for noncommercial use at http://www.chm.tu-dresden.de/pc6/EGNAS.
Design of all Optical Packet Switching Networks
Directory of Open Access Journals (Sweden)
Hussein T. Mouftah
2002-06-01
Full Text Available Optical switches and wavelength converters are recognized as two of the most important DWDM system components in future all-optical networks. Optical switches perform the key functions of flexible routing, reconfigurable optical cross-connect (OXC, network protection and restoration, etc. in optical networks. Wavelength Converters are used to shift one incoming wavelength to another outgoing wavelength when this needs to be done. Always residing in optical switches, they can effectively alleviate the blocking probability and help solve contention happening at the output port of switches. The deployment of wavelength converters within optical switches provides robust routing, switching and network management in optical layer, which is critical to the emerging all-optical Internet. However, the high cost of wavelength converters at current stage of manufacturing technology has to be taken into consideration when we design node architectures for an optical network. Our research explores the efficiency of wavelength converters in a long-haul optical network at different degrees of traffic load by running a simulation. Then, we propose a new cost-effective way to optimally design wavelength-convertible switch so as to achieve higher network performance while still keeping the total network cost down. Meanwhile, the routing and wavelength assignment (RWA algorithm used in the research is designed to be a generic one for both large-scale and small-scale traffic. Removing the constraint on the traffic load makes the RWA more adaptive and robust. When this new RWA works in conjunction with a newly introduced concept of wavelength-convertible switches, we shall explore the impact of large-scale traffic on the role of wavelength converter so as to determine the method towards optimal use of wavelength convertible switches for all-optical networks.
Optimal design of network distribution systems
Directory of Open Access Journals (Sweden)
U. Passy
2003-12-01
Full Text Available The problem of finding the optimal distribution of pressure drop over a network is solved via an unconstrained gradient type algorithm. The developed algorithm is computationally attractive. Problems with several hundred variables and constraints were solved.
A New Optimized GA-RBF Neural Network Algorithm
Directory of Open Access Journals (Sweden)
Weikuan Jia
2014-01-01
Full Text Available When confronting the complex problems, radial basis function (RBF neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm, which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer’s neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.
A new optimized GA-RBF neural network algorithm.
Jia, Weikuan; Zhao, Dean; Shen, Tian; Su, Chunyang; Hu, Chanli; Zhao, Yuyan
2014-01-01
When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid.
Hybrid Wireless Sensor Network Coverage Holes Restoring Algorithm
Directory of Open Access Journals (Sweden)
Liu Zhouzhou
2016-01-01
Full Text Available Aiming at the perception hole caused by the necessary movement or failure of nodes in the wireless sensor actuator network, this paper proposed a kind of coverage restoring scheme based on hybrid particle swarm optimization algorithm. The scheme first introduced network coverage based on grids, transformed the coverage restoring problem into unconstrained optimization problem taking the network coverage as the optimization target, and then solved the optimization problem in the use of the hybrid particle swarm optimization algorithm with the idea of simulated annealing. Simulation results show that the probabilistic jumping property of simulated annealing algorithm could make up for the defect that particle swarm optimization algorithm is easy to fall into premature convergence, and the hybrid algorithm can effectively solve the coverage restoring problem.
Algorithms for Finding Small Attractors in Boolean Networks
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Hayashida Morihiro
2007-01-01
Full Text Available A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.
Hybrid neural network model for the design of beam subjected to ...
Indian Academy of Sciences (India)
This paper demonstrates the applicability of Artiﬁcial Neural Networks (ANN) and Genetic Algorithms (GA) for the design of beams subjected to moment and shear. A hybrid neural network model which combines the features of feed forward neural networks and genetic algorithms has been developed for the design of beam ...
Singh, Puja; Prakash, Shashi
2017-07-01
Hybrid wireless-optical broadband access network (WOBAN) or Fiber-Wireless (FiWi) is the integration of wireless access network and optical network. This hybrid multi-domain network adopts the advantages of wireless and optical domains and serves the demand of technology savvy users. FiWi exhibits the properties of cost effectiveness, robustness, flexibility, high capacity, reliability and is self organized. Optical Network Unit (ONU) placement problem in FiWi contributes in simplifying the network design and enhances the performance in terms of cost efficiency and increased throughput. Several individual-based algorithms, such as Simulated Annealing (SA), Tabu Search, etc. have been suggested for ONU placement, but these algorithms suffer from premature convergence (trapping in a local optima). The present research work undertakes the deployment of FiWi and proposes a novel nature-inspired heuristic paradigm called Moth-Flame optimization (MFO) algorithm for multiple optical network units' placement. MFO is a population based algorithm. Population-based algorithms are better in handling local optima avoidance. The simulation results are compared with the existing Greedy and Simulated Annealing algorithms to optimize the position of ONUs. To the best of our knowledge, MFO algorithm has been used for the first time in this domain, moreover it has been able to provide very promising and competitive results. The performance of MFO algorithm has been analyzed by varying the 'b' parameter. MFO algorithm results in faster convergence than the existing strategies of Greedy and SA and returns a lower value of overall cost function. The results exhibit the dependence of the objective function on the distribution of wireless users also.
Neural Network Algorithm for Prediction of Secondary Protein Structure
National Research Council Canada - National Science Library
Zikrija Avdagic; Elvir Purisevic; Emir Buza; Zlatan Coralic
2009-01-01
.... In this paper we describe the method and results of using CB513 as a dataset suitable for development of artificial neural network algorithms for prediction of secondary protein structure with MATLAB...
Location-Based Self-Adaptive Routing Algorithm for Wireless Sensor Networks in Home Automation
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Hong SeungHo
2011-01-01
Full Text Available The use of wireless sensor networks in home automation (WSNHA is attractive due to their characteristics of self-organization, high sensing fidelity, low cost, and potential for rapid deployment. Although the AODVjr routing algorithm in IEEE 802.15.4/ZigBee and other routing algorithms have been designed for wireless sensor networks, not all are suitable for WSNHA. In this paper, we propose a location-based self-adaptive routing algorithm for WSNHA called WSNHA-LBAR. It confines route discovery flooding to a cylindrical request zone, which reduces the routing overhead and decreases broadcast storm problems in the MAC layer. It also automatically adjusts the size of the request zone using a self-adaptive algorithm based on Bayes' theorem. This makes WSNHA-LBAR more adaptable to the changes of the network state and easier to implement. Simulation results show improved network reliability as well as reduced routing overhead.
Regular Network Class Features Enhancement Using an Evolutionary Synthesis Algorithm
Directory of Open Access Journals (Sweden)
O. G. Monahov
2014-01-01
Full Text Available This paper investigates a solution of the optimization problem concerning the construction of diameter-optimal regular networks (graphs. Regular networks are of practical interest as the graph-theoretical models of reliable communication networks of parallel supercomputer systems, as a basis of the structure in a model of small world in optical and neural networks. It presents a new class of parametrically described regular networks - hypercirculant networks (graphs. An approach that uses evolutionary algorithms for the automatic generation of parametric descriptions of optimal hypercirculant networks is developed. Synthesis of optimal hypercirculant networks is based on the optimal circulant networks with smaller degree of nodes. To construct optimal hypercirculant networks is used a template of circulant network from the known optimal families of circulant networks with desired number of nodes and with smaller degree of nodes. Thus, a generating set of the circulant network is used as a generating subset of the hypercirculant network, and the missing generators are synthesized by means of the evolutionary algorithm, which is carrying out minimization of diameter (average diameter of networks. A comparative analysis of the structural characteristics of hypercirculant, toroidal, and circulant networks is conducted. The advantage hypercirculant networks under such structural characteristics, as diameter, average diameter, and the width of bisection, with comparable costs of the number of nodes and the number of connections is demonstrated. It should be noted the advantage of hypercirculant networks of dimension three over four higher-dimensional tori. Thus, the optimization of hypercirculant networks of dimension three is more efficient than the introduction of an additional dimension for the corresponding toroidal structures. The paper also notes the best structural parameters of hypercirculant networks in comparison with iBT-networks previously
An algorithm for link restoration in wavwlength translating networks
DEFF Research Database (Denmark)
Limal, Emmanuel; Gliese, Ulrik Bo
1999-01-01
We propose the BONRA, a new and innovative algorithm for dynamic allocation of working and spare channel capacity for single link restoration in wavelength translating optical networks. The BONRA has very low calculation complexity yet gives high capacity utilisation.......We propose the BONRA, a new and innovative algorithm for dynamic allocation of working and spare channel capacity for single link restoration in wavelength translating optical networks. The BONRA has very low calculation complexity yet gives high capacity utilisation....
Engineering Algorithms for Route Planning in Multimodal Transportation Networks
Dibbelt, Julian Matthias
2016-01-01
Practical algorithms for route planning in transportation networks are a showpiece of successful Algorithm Engineering. This has produced many speedup techniques, varying in preprocessing time, space, query performance, simplicity, and ease of implementation. This thesis explores solutions to more realistic scenarios, taking into account, e.g., traffic, user preferences, public transit schedules, and the options offered by the many modalities of modern transportation networks.
An algorithm for link restoration of wavelength routing optical networks
DEFF Research Database (Denmark)
Limal, Emmanuel; Stubkjær, Kristian
1999-01-01
We present an algorithm for restoration of single link failure in wavelength routing multihop optical networks. The algorithm is based on an innovative study of networks using graph theory. It has the following original features: it (i) assigns working and spare channels simultaneously, (ii) prev...... low complexity is studied in detail and compared to the complexity of a classical path assignment algorithm. Finally, we explain how to use the algorithm to control the restoration path lengths.......We present an algorithm for restoration of single link failure in wavelength routing multihop optical networks. The algorithm is based on an innovative study of networks using graph theory. It has the following original features: it (i) assigns working and spare channels simultaneously, (ii......) prevents the search for unacceptable routing paths by pointing out channels required for restoration, (iii) offers a high utilization of the capacity resources and (iv) allows a trivial search for the restoration paths. The algorithm is for link restoration of networks without wavelength translation. Its...
Integrated network design and scheduling problems :
Energy Technology Data Exchange (ETDEWEB)
Nurre, Sarah G.; Carlson, Jeffrey J.
2014-01-01
We consider the class of integrated network design and scheduling problems. These problems focus on selecting and scheduling operations that will change the characteristics of a network, while being speci cally concerned with the performance of the network over time. Motivating applications of INDS problems include infrastructure restoration after extreme events and building humanitarian distribution supply chains. While similar models have been proposed, no one has performed an extensive review of INDS problems from their complexity, network and scheduling characteristics, information, and solution methods. We examine INDS problems under a parallel identical machine scheduling environment where the performance of the network is evaluated by solving classic network optimization problems. We classify that all considered INDS problems as NP-Hard and propose a novel heuristic dispatching rule algorithm that selects and schedules sets of arcs based on their interactions in the network. We present computational analysis based on realistic data sets representing the infrastructures of coastal New Hanover County, North Carolina, lower Manhattan, New York, and a realistic arti cial community CLARC County. These tests demonstrate the importance of a dispatching rule to arrive at near-optimal solutions during real-time decision making activities. We extend INDS problems to incorporate release dates which represent the earliest an operation can be performed and exible release dates through the introduction of specialized machine(s) that can perform work to move the release date earlier in time. An online optimization setting is explored where the release date of a component is not known.
Recommending Learning Activities in Social Network Using Data Mining Algorithms
Mahnane, Lamia
In this paper, we show how data mining algorithms (e.g. Apriori Algorithm (AP) and Collaborative Filtering (CF)) is useful in New Social Network (NSN-AP-CF). "NSN-AP-CF" processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the users through mining the frequent episodes by the…
Recommending Learning Activities in Social Network Using Data Mining Algorithms
Mahnane, Lamia
2017-01-01
In this paper, we show how data mining algorithms (e.g. Apriori Algorithm (AP) and Collaborative Filtering (CF)) is useful in New Social Network (NSN-AP-CF). "NSN-AP-CF" processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the users through mining the frequent episodes by the…
A generalized clustering algorithm for dynamic wireless sensor networks
Marin Perianu, Raluca; Hurink, Johann L.; Hartel, Pieter H.
We propose a general clustering algorithm for dynamic sensor networks, that makes localized decisions (1-hop neighbourhood) and produces disjoint clusters. The purpose is to extract and emphasise the essential clustering mechanisms common for a set of state-of-the-art algorithms, which allows for a
A Generalized Clustering Algorithm for Dynamic Wireless Sensor Networks
Marin Perianu, Raluca; Hurink, Johann L.; Hartel, Pieter H.
2008-01-01
We propose a general clustering algorithm for dynamic sensor networks, that makes localized decisions (1-hop neighbourhood) and produces disjoint clusters. The purpose is to extract and emphasise the essential clustering mechanisms common for a set of state-of-the-art algorithms, which allows for a
Practical Algorithms for Subgroup Detection in Covert Networks
DEFF Research Database (Denmark)
Memon, Nasrullah; Wiil, Uffe Kock; Qureshi, Pir Abdul Rasool
2010-01-01
In this paper, we present algorithms for subgroup detection and demonstrated them with a real-time case study of USS Cole bombing terrorist network. The algorithms are demonstrated in an application by a prototype system. The system finds associations between terrorist and terrorist organisations...
Social-Stratification Probabilistic Routing Algorithm in Delay-Tolerant Network
Alnajjar, Fuad; Saadawi, Tarek
Routing in mobile ad hoc networks (MANET) is complicated due to the fact that the network graph is episodically connected. In MANET, topology is changing rapidly because of weather, terrain and jamming. A key challenge is to create a mechanism that can provide good delivery performance and low end-to-end delay in an intermittent network graph where nodes may move freely. Delay-Tolerant Networking (DTN) architecture is designed to provide communication in intermittently connected networks, by moving messages towards destination via ”store, carry and forward” technique that supports multi-routing algorithms to acquire best path towards destination. In this paper, we propose the use of probabilistic routing in DTN architecture using the concept of social-stratification network. We use the Opportunistic Network Environment (ONE) simulator as a simulation tool to compare the proposed Social- stratification Probabilistic Routing Algorithm (SPRA) with the common DTN-based protocols. Our results show that SPRA outperforms the other protocols.
A Distributed Algorithm for Economic Dispatch Over Time-Varying Directed Networks With Delays
Energy Technology Data Exchange (ETDEWEB)
Yang, Tao; Lu, Jie; Wu, Di; Wu, Junfeng; Shi, Guodong; Meng, Ziyang; Johansson, Karl Henrik
2017-06-01
In power system operation, economic dispatch problem (EDP) is designed to minimize the total generation cost while meeting the demand and satisfying generator capacity limits. This paper proposes an algorithm based on the gradient-push method to solve the EDP in a distributed manner over communication networks potentially with time-varying topologies and communication delays. It has been shown that the proposed method is guaranteed to solve the EDP if the time-varying directed communication network is uniformly jointly strongly connected. Moreover, the proposed algorithm is also able to handle arbitrarily large but bounded time delays on communication links. Numerical simulations are used to illustrate and validate the proposed algorithm.
Directory of Open Access Journals (Sweden)
Afaz Uddin Ahmed
2014-01-01
Full Text Available An artificial neural network (ANN and affinity propagation (AP algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.
UWB Tracking System Design with TDOA Algorithm
Ni, Jianjun; Arndt, Dickey; Ngo, Phong; Phan, Chau; Gross, Julia; Dusl, John; Schwing, Alan
2006-01-01
This presentation discusses an ultra-wideband (UWB) tracking system design effort using a tracking algorithm TDOA (Time Difference of Arrival). UWB technology is exploited to implement the tracking system due to its properties, such as high data rate, fine time resolution, and low power spectral density. A system design using commercially available UWB products is proposed. A two-stage weighted least square method is chosen to solve the TDOA non-linear equations. Matlab simulations in both two-dimensional space and three-dimensional space show that the tracking algorithm can achieve fine tracking resolution with low noise TDOA data. The error analysis reveals various ways to improve the tracking resolution. Lab experiments demonstrate the UWBTDOA tracking capability with fine resolution. This research effort is motivated by a prototype development project Mini-AERCam (Autonomous Extra-vehicular Robotic Camera), a free-flying video camera system under development at NASA Johnson Space Center for aid in surveillance around the International Space Station (ISS).
Cai, Guanjing; Zheng, Wei; Yang, Xujun; Zhang, Bangzhou; Zheng, Tianling
2014-05-24
Controlling harmful algae blooms (HABs) using microbial algicides is cheap, efficient and environmental-friendly. However, obtaining high yield of algicidal microbes to meet the need of field test is still a big challenge since qualitative and quantitative analysis of algicidal compounds is difficult. In this study, we developed a protocol to increase the yield of both biomass and algicidal compound present in a novel algicidal actinomycete Streptomyces alboflavus RPS, which kills Phaeocystis globosa. To overcome the problem in algicidal compound quantification, we chose algicidal ratio as the index and used artificial neural network to fit the data, which was appropriate for this nonlinear situation. In this protocol, we firstly determined five main influencing factors through single factor experiments and generated the multifactorial experimental groups with a U15(155) uniform-design-table. Then, we used the traditional quadratic polynomial stepwise regression model and an accurate, fully optimized BP-neural network to simulate the fermentation. Optimized with genetic algorithm and verified using experiments, we successfully increased the algicidal ratio of the fermentation broth by 16.90% and the dry mycelial weight by 69.27%. These results suggested that this newly developed approach is a viable and easy way to optimize the fermentation conditions for algicidal microorganisms.
Insertion algorithms for network model database management systems
Mamadolimov, Abdurashid; Khikmat, Saburov
2017-12-01
The network model is a database model conceived as a flexible way of representing objects and their relationships. Its distinguishing feature is that the schema, viewed as a graph in which object types are nodes and relationship types are arcs, forms partial order. When a database is large and a query comparison is expensive then the efficiency requirement of managing algorithms is minimizing the number of query comparisons. We consider updating operation for network model database management systems. We develop a new sequantial algorithm for updating operation. Also we suggest a distributed version of the algorithm.
New Heuristic Algorithm for Dynamic Traffic in WDM Optical Networks
Directory of Open Access Journals (Sweden)
Arturo Benito Rodríguez Garcia
2015-12-01
Full Text Available The results and comparison of the simulation of a new heuristic algorithm called Snake One are presented. The comparison is made with three heuristic algorithms, Genetic Algorithms, Simulated Annealing, and Tabu Search, using blocking probability and network utilization as standard indicators. The simulation was made on the WDM NSFNET under dynamic traffic conditions. The results show a substantial decrease of blocking, but this causes a relative growth of network utilization. There are also load intervals at which its performance improves, decreasing the number of blocked requests.
Genetic Algorithm Optimized Neural Networks Ensemble as ...
African Journals Online (AJOL)
Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous spectrophotometric multicomponent analysis are suggested, with a study on the estimation of the components of an antihypertensive combination, namely, atenolol and losartan potassium.
Wireless Sensor Networks : Structure and Algorithms
van Dijk, T.C.|info:eu-repo/dai/nl/304841293
2014-01-01
In this thesis we look at various problems in wireless networking. First we consider two problems in physical-model networks. We introduce a new model for localisation. The model is based on a range-free model of radio transmissions. The first scheme is randomised and we analyse its expected
Network Design Tool for EHF Satellite Communications Networks
1983-06-01
constraints. A useful algorithm for this is the Esau -Williams algorithm. An exact solution can be obtained using the branch-and-bound method. Descriptions of...Three of these algorithms are the Esau -Williams algo- rithm, the Prim algorithm and the Kruskal algorithm. These are described in Chapter 9 of...requirements. In the constrained multipoint design problem they produce somewhat different designs as well. Experience has shown that the Esau - Williams
Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling
Abrahart, R. J.
2004-05-01
This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and
A Practical Algorithm for Reconstructing Level-1 Phylogenetic Networks
K.T. Huber; L.J.J. van Iersel (Leo); S.M. Kelk (Steven); R. Suchecki
2010-01-01
htmlabstractRecently much attention has been devoted to the construction of phylogenetic networks which generalize phylogenetic trees in order to accommodate complex evolutionary processes. Here we present an efficient, practical algorithm for reconstructing level-1 phylogenetic networks - a type of
Design Tools for Large Networks
Panikashvili, E; 15th IEEE Real Time Conference 2007
2007-01-01
The Atlas TDAQ network consists of four separate networks which together total over 4000 ports at 1Gb/s, 40 ports at 10Gbps, 200 Ethernet switches at the edge of the network, 6 multiblade chassis switches at the core and over 20 km of copper and fiber cabling. The NetDesign suite was developed to provide a comprehensive set of design tools that permits the generation of a navigable circuit design, automates a number of routine tasks and provides the user with a powerful and flexible reporting system. Netdesign is an addon extension to the basic functionality of the Microsoft Visio CAD tool. It features: automatic labeling of cables, media / port connection validation, navigation along a cable, schematic hierarchical, cross-page and vertical navigation, overall verification of network diagram and hyperlinks to other equipment databases. The internal network structure can be exported to a 3rd party database from which a user-friendly meta-language is used to process a large variety of reports on the network des...
Slow update stochastic simulation algorithms for modeling complex biochemical networks.
Ghosh, Debraj; De, Rajat K
2017-10-30
The stochastic simulation algorithm (SSA) based modeling is a well recognized approach to predict the stochastic behavior of biological networks. The stochastic simulation of large complex biochemical networks is a challenge as it takes a large amount of time for simulation due to high update cost. In order to reduce the propensity update cost, we proposed two algorithms: slow update exact stochastic simulation algorithm (SUESSA) and slow update exact sorting stochastic simulation algorithm (SUESSSA). We applied cache-based linear search (CBLS) in these two algorithms for improving the search operation for finding reactions to be executed. Data structure used for incorporating CBLS is very simple and the cost of maintaining this during propensity update operation is very low. Hence, time taken during propensity updates, for simulating strongly coupled networks, is very fast; which leads to reduction of total simulation time. SUESSA and SUESSSA are not only restricted to elementary reactions, they support higher order reactions too. We used linear chain model and colloidal aggregation model to perform a comparative analysis of the performances of our methods with the existing algorithms. We also compared the performances of our methods with the existing ones, for large biochemical networks including B cell receptor and FcϵRI signaling networks. Copyright © 2017 Elsevier B.V. All rights reserved.
A clustering algorithm for determining community structure in complex networks
Jin, Hong; Yu, Wei; Li, ShiJun
2018-02-01
Clustering algorithms are attractive for the task of community detection in complex networks. DENCLUE is a representative density based clustering algorithm which has a firm mathematical basis and good clustering properties allowing for arbitrarily shaped clusters in high dimensional datasets. However, this method cannot be directly applied to community discovering due to its inability to deal with network data. Moreover, it requires a careful selection of the density parameter and the noise threshold. To solve these issues, a new community detection method is proposed in this paper. First, we use a spectral analysis technique to map the network data into a low dimensional Euclidean Space which can preserve node structural characteristics. Then, DENCLUE is applied to detect the communities in the network. A mathematical method named Sheather-Jones plug-in is chosen to select the density parameter which can describe the intrinsic clustering structure accurately. Moreover, every node on the network is meaningful so there were no noise nodes as a result the noise threshold can be ignored. We test our algorithm on both benchmark and real-life networks, and the results demonstrate the effectiveness of our algorithm over other popularity density based clustering algorithms adopted to community detection.
ANOMALY DETECTION IN NETWORKING USING HYBRID ARTIFICIAL IMMUNE ALGORITHM
Directory of Open Access Journals (Sweden)
D. Amutha Guka
2012-01-01
Full Text Available Especially in today’s network scenario, when computers are interconnected through internet, security of an information system is very important issue. Because no system can be absolutely secure, the timely and accurate detection of anomalies is necessary. The main aim of this research paper is to improve the anomaly detection by using Hybrid Artificial Immune Algorithm (HAIA which is based on Artificial Immune Systems (AIS and Genetic Algorithm (GA. In this research work, HAIA approach is used to develop Network Anomaly Detection System (NADS. The detector set is generated by using GA and the anomalies are identified using Negative Selection Algorithm (NSA which is based on AIS. The HAIA algorithm is tested with KDD Cup 99 benchmark dataset. The detection rate is used to measure the effectiveness of the NADS. The results and consistency of the HAIA are compared with earlier approaches and the results are presented. The proposed algorithm gives best results when compared to the earlier approaches.
A Distributed Algorithm for Energy Optimization in Hydraulic Networks
DEFF Research Database (Denmark)
Kallesøe, Carsten; Wisniewski, Rafal; Jensen, Tom Nørgaard
2014-01-01
An industrial case study in the form of a large-scale hydraulic network underlying a district heating system is considered. A distributed control is developed that minimizes the aggregated electrical energy consumption of the pumps in the network without violating the control demands. The algorithm...... a Plug & Play control system as most commissioning can be done during the manufacture of the pumps. Only information on the graph-structure of the hydraulic network is needed during installation....
Aggregation algorithm towards large-scale Boolean network analysis
Zhao, Y.; Kim, J.; Filippone, M.
2013-01-01
The analysis of large-scale Boolean network dynamics is of great importance in understanding complex phenomena where systems are characterized by a large number of components. The computational cost to reveal the number of attractors and the period of each attractor increases exponentially as the number of nodes in the networks increases. This paper presents an efficient algorithm to find attractors for medium to large-scale networks. This is achieved by analyzing subnetworks within the netwo...
Community Clustering Algorithm in Complex Networks Based on Microcommunity Fusion
Directory of Open Access Journals (Sweden)
Jin Qi
2015-01-01
Full Text Available With the further research on physical meaning and digital features of the community structure in complex networks in recent years, the improvement of effectiveness and efficiency of the community mining algorithms in complex networks has become an important subject in this area. This paper puts forward a concept of the microcommunity and gets final mining results of communities through fusing different microcommunities. This paper starts with the basic definition of the network community and applies Expansion to the microcommunity clustering which provides prerequisites for the microcommunity fusion. The proposed algorithm is more efficient and has higher solution quality compared with other similar algorithms through the analysis of test results based on network data set.
Improved mine blast algorithm for optimal cost design of water distribution systems
Sadollah, Ali; Guen Yoo, Do; Kim, Joong Hoon
2015-12-01
The design of water distribution systems is a large class of combinatorial, nonlinear optimization problems with complex constraints such as conservation of mass and energy equations. Since feasible solutions are often extremely complex, traditional optimization techniques are insufficient. Recently, metaheuristic algorithms have been applied to this class of problems because they are highly efficient. In this article, a recently developed optimizer called the mine blast algorithm (MBA) is considered. The MBA is improved and coupled with the hydraulic simulator EPANET to find the optimal cost design for water distribution systems. The performance of the improved mine blast algorithm (IMBA) is demonstrated using the well-known Hanoi, New York tunnels and Balerma benchmark networks. Optimization results obtained using IMBA are compared to those using MBA and other optimizers in terms of their minimum construction costs and convergence rates. For the complex Balerma network, IMBA offers the cheapest network design compared to other optimization algorithms.
Properties of healthcare teaming networks as a function of network construction algorithms.
Zand, Martin S; Trayhan, Melissa; Farooq, Samir A; Fucile, Christopher; Ghoshal, Gourab; White, Robert J; Quill, Caroline M; Rosenberg, Alexander; Barbosa, Hugo Serrano; Bush, Kristen; Chafi, Hassan; Boudreau, Timothy
2017-01-01
Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other, and to map how patients traverse the network of providers. Most healthcare service network models have been constructed from patient claims data, using billing claims to link a patient with a specific provider in time. The data sets can be quite large (106-108 individual claims per year), making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks, which as we demonstrate, can be dramatically different. To address this issue, we compared the properties of healthcare networks constructed using different algorithms from 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We find that each algorithm produced networks with substantially different topological properties, as reflected by numbers of edges, network density, assortativity, clustering coefficients and other structural measures. Provider networks adhered to a power law, while organization networks were best fit by a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and network density, and greatly altered measures of vertex prominence such as the betweenness centrality. Data analysis identified patterns in the distance patients travel between network providers, and a striking set of teaming relationships between providers in the Northeast United States and
AN ALGORITHM FOR AN ALGORITHM FOR THE DESIGN THE ...
African Journals Online (AJOL)
eobe
thermodynamic and aerodynamic correlations. This base design will make up for about 60 – 70% of the finished design [2]. In this first stage of designing a new compressor, designs that would not work or have poor efficiency can be improved, therefore, creating a base design for an existing compressor, such as in GT-. II.
Fast Parallel Algorithms for Graphs and Networks
1987-12-01
loosing the nth game of badminton to him. Valerie King and .Joel Friedman showed me the wonders of cross-country skiing in Yosemite. Steven Rudich was...2), both W(u) and L(v) have no more than 7s/8 vertices. Let x be some ver- tex. We can describe the history of x throughout the algorithm by a zero
Incremental Centrality Algorithms for Dynamic Network Analysis
2013-08-01
Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware...run-time of O(m + nlogn) can be achieved by implementing the priority queue using a Fibonacci heap [127]. When Dijsktra’s algorithm is invoked
Directory of Open Access Journals (Sweden)
Jiang Ting
2010-01-01
Full Text Available We optimize the cluster structure to solve problems such as the uneven energy consumption of the radar sensor nodes and random cluster head selection in the traditional clustering routing algorithm. According to the defined cost function for clusters, we present the clustering algorithm which is based on radio-free space path loss. In addition, we propose the energy and distance pheromones based on the residual energy and aggregation of the radar sensor nodes. According to bionic heuristic algorithm, a new ant colony-based clustering algorithm for radar sensor networks is also proposed. Simulation results show that this algorithm can get a better balance of the energy consumption and then remarkably prolong the lifetime of the radar sensor network.
The guitar chord-generating algorithm based on complex network
Ren, Tao; Wang, Yi-fan; Du, Dan; Liu, Miao-miao; Siddiqi, Awais
2016-02-01
This paper aims to generate chords for popular songs automatically based on complex network. Firstly, according to the characteristics of guitar tablature, six chord networks of popular songs by six pop singers are constructed and the properties of all networks are concluded. By analyzing the diverse chord networks, the accompaniment regulations and features are shown, with which the chords can be generated automatically. Secondly, in terms of the characteristics of popular songs, a two-tiered network containing a verse network and a chorus network is constructed. With this network, the verse and chorus can be composed respectively with the random walk algorithm. Thirdly, the musical motif is considered for generating chords, with which the bad chord progressions can be revised. This method can make the accompaniments sound more melodious. Finally, a popular song is chosen for generating chords and the new generated accompaniment sounds better than those done by the composers.
Parameter-free Network Sparsification and Data Reduction by Minimal Algorithmic Information Loss
Zenil, Hector
2018-02-16
The study of large and complex datasets, or big data, organized as networks has emerged as one of the central challenges in most areas of science and technology. Cellular and molecular networks in biology is one of the prime examples. Henceforth, a number of techniques for data dimensionality reduction, especially in the context of networks, have been developed. Yet, current techniques require a predefined metric upon which to minimize the data size. Here we introduce a family of parameter-free algorithms based on (algorithmic) information theory that are designed to minimize the loss of any (enumerable computable) property contributing to the object\\'s algorithmic content and thus important to preserve in a process of data dimension reduction when forcing the algorithm to delete first the least important features. Being independent of any particular criterion, they are universal in a fundamental mathematical sense. Using suboptimal approximations of efficient (polynomial) estimations we demonstrate how to preserve network properties outperforming other (leading) algorithms for network dimension reduction. Our method preserves all graph-theoretic indices measured, ranging from degree distribution, clustering-coefficient, edge betweenness, and degree and eigenvector centralities. We conclude and demonstrate numerically that our parameter-free, Minimal Information Loss Sparsification (MILS) method is robust, has the potential to maximize the preservation of all recursively enumerable features in data and networks, and achieves equal to significantly better results than other data reduction and network sparsification methods.
Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem
Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf
2017-08-01
Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.
A Low Energy Algorithm of Wireless Sensor Networks Based on Fractal Dimension
Directory of Open Access Journals (Sweden)
Ting Dong
2014-05-01
Full Text Available For the energy limitation of nodes and imbalance energy consuming among nodes, this paper proposes an optimization algorithm --Low Energy Algorithm-- of wireless sensor networks based on fractal dimension algorithm for the purpose of reduction of the energy consumption. The nodes in WSN cannot be located evenly, and cannot move with the monitoring environment changed once be located. Considering the characteristics of WSN, the paper designs an optimized clustering method accompany with dimension by calculating the dimension of each cluster to determine the cluster which needs to be adjusted dynamically. If the cluster with high value of dimension, increasing more nodes in this cluster. If the cluster with low value of dimension, reducing more nodes in the cluster. The simulation results show that the LEA algorithm improves energy efficiency, prolongs the network lifetime, and balances energy consumption in the sensor network.
Demeyer, Sofie; Michoel, Tom; Fostier, Jan; Audenaert, Pieter; Pickavet, Mario; Demeester, Piet
2013-01-01
Subgraph matching algorithms are designed to find all instances of predefined subgraphs in a large graph or network and play an important role in the discovery and analysis of so-called network motifs, subgraph patterns which occur more often than expected by chance. We present the index-based subgraph matching algorithm (ISMA), a novel tree-based algorithm. ISMA realizes a speedup compared to existing algorithms by carefully selecting the order in which the nodes of a query subgraph are investigated. In order to achieve this, we developed a number of data structures and maximally exploited symmetry characteristics of the subgraph. We compared ISMA to a naive recursive tree-based algorithm and to a number of well-known subgraph matching algorithms. Our algorithm outperforms the other algorithms, especially on large networks and with large query subgraphs. An implementation of ISMA in Java is freely available at http://sourceforge.net/projects/isma/. PMID:23620730
Quantum Google algorithm. Construction and application to complex networks
Paparo, G. D.; Müller, M.; Comellas, F.; Martin-Delgado, M. A.
2014-07-01
We review the main findings on the ranking capabilities of the recently proposed Quantum PageRank algorithm (G.D. Paparo et al., Sci. Rep. 2, 444 (2012) and G.D. Paparo et al., Sci. Rep. 3, 2773 (2013)) applied to large complex networks. The algorithm has been shown to identify unambiguously the underlying topology of the network and to be capable of clearly highlighting the structure of secondary hubs of networks. Furthermore, it can resolve the degeneracy in importance of the low-lying part of the list of rankings. Examples of applications include real-world instances from the WWW, which typically display a scale-free network structure and models of hierarchical networks. The quantum algorithm has been shown to display an increased stability with respect to a variation of the damping parameter, present in the Google algorithm, and a more clearly pronounced power-law behaviour in the distribution of importance among the nodes, as compared to the classical algorithm.
Performance evaluation of power control algorithms in wireless cellular networks
Temaneh-Nyah, C.; Iita, V.
2014-10-01
Power control in a mobile communication network intents to control the transmission power levels in such a way that the required quality of service (QoS) for the users is guaranteed with lowest possible transmission powers. Most of the studies of power control algorithms in the literature are based on some kind of simplified assumptions which leads to compromise in the validity of the results when applied in a real environment. In this paper, a CDMA network was simulated. The real environment was accounted for by defining the analysis area and the network base stations and mobile stations are defined by their geographical coordinates, the mobility of the mobile stations is accounted for. The simulation also allowed for a number of network parameters including the network traffic, and the wireless channel models to be modified. Finally, we present the simulation results of a convergence speed based comparative analysis of three uplink power control algorithms.
Wireless sensor networks principles, design and applications
Yang, Shuang-Hua
2014-01-01
Wireless Sensor Networks presents the latest practical solutions to the design issues presented in wireless-sensor-network-based systems. Novel features of the text, distributed throughout, include workable solutions, demonstration systems and case studies of the design and application of wireless sensor networks (WSNs) based on the first-hand research and development experience of the author, and the chapters on real applications: building fire safety protection; smart home automation; and logistics resource management. Case studies and applications illustrate the practical perspectives of: · sensor node design; · embedded software design; · routing algorithms; · sink node positioning; · co-existence with other wireless systems; · data fusion; · security; · indoor location tracking; · integrating with radio-frequency identification; and · In...
Algorithmic and analytical methods in network biology
Koyutürk, Mehmet
2010-01-01
During genomic revolution, algorithmic and analytical methods for organizing, integrating, analyzing, and querying biological sequence data proved invaluable. Today, increasing availability of high-throughput data pertaining functional states of biomolecules, as well as their interactions, enables genome-scale studies of the cell from a systems perspective. The past decade witnessed significant efforts on the development of computational infrastructure for large-scale modeling and analysis of...
Algorithms for Scheduling and Network Problems
1991-09-01
Baruch Awerbuch while at MIT, and I thank him for serving on my thesis committee as well. My fellow students Cliff Stein and David Williamson have both...least one processor per operation, this can be done in NC using the edge-coloring algorithm of Lev , Pippinger, and Valiant [84]. We can extend this to...scheduling unrelated parallel machines. Mathematical Programming, 46:259-271, 1990. [84] G. F. Lev , N. Pippenger, and L. G. Valiant. A fast parallel
In-Trail Procedure (ITP) Algorithm Design
Munoz, Cesar A.; Siminiceanu, Radu I.
2007-01-01
The primary objective of this document is to provide a detailed description of the In-Trail Procedure (ITP) algorithm, which is part of the Airborne Traffic Situational Awareness In-Trail Procedure (ATSA-ITP) application. To this end, the document presents a high level description of the ITP Algorithm and a prototype implementation of this algorithm in the programming language C.
Datasets for radiation network algorithm development and testing
Energy Technology Data Exchange (ETDEWEB)
Rao, Nageswara S [ORNL; Sen, Satyabrata [ORNL; Berry, M. L.. [New Jersey Institute of Technology; Wu, Qishi [University of Memphis; Grieme, M. [New Jersey Institute of Technology; Brooks, Richard R [ORNL; Cordone, G. [Clemson University
2016-01-01
Domestic Nuclear Detection Office s (DNDO) Intelligence Radiation Sensors Systems (IRSS) program supported the development of networks of commercial-off-the-shelf (COTS) radiation counters for detecting, localizing, and identifying low-level radiation sources. Under this program, a series of indoor and outdoor tests were conducted with multiple source strengths and types, different background profiles, and various types of source and detector movements. Following the tests, network algorithms were replayed in various re-constructed scenarios using sub-networks. These measurements and algorithm traces together provide a rich collection of highly valuable datasets for testing the current and next generation radiation network algorithms, including the ones (to be) developed by broader R&D communities such as distributed detection, information fusion, and sensor networks. From this multiple TeraByte IRSS database, we distilled out and packaged the first batch of canonical datasets for public release. They include measurements from ten indoor and two outdoor tests which represent increasingly challenging baseline scenarios for robustly testing radiation network algorithms.
A New Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Complex Networks
Directory of Open Access Journals (Sweden)
Guoqiang Chen
2013-01-01
Full Text Available Community detection in dynamic networks is an important research topic and has received an enormous amount of attention in recent years. Modularity is selected as a measure to quantify the quality of the community partition in previous detection methods. But, the modularity has been exposed to resolution limits. In this paper, we propose a novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm. Modularity density which can address the limitations of modularity function is adopted to measure the snapshot cost, and normalized mutual information is selected to measure temporal cost, respectively. The characteristics knowledge of the problem is used in designing the genetic operators. Furthermore, a local search operator was designed, which can improve the effectiveness and efficiency of community detection. Experimental studies based on synthetic datasets show that the proposed algorithm can obtain better performance than the compared algorithms.
Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.
Walter, Florian; Röhrbein, Florian; Knoll, Alois
2015-12-01
The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks. Copyright © 2015 Elsevier Ltd. All rights reserved.
An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network
Directory of Open Access Journals (Sweden)
Kai Hu
2013-01-01
Full Text Available A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA to decide weights in a back propagation neural network (BPN. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.
Block Least Mean Squares Algorithm over Distributed Wireless Sensor Network
Directory of Open Access Journals (Sweden)
T. Panigrahi
2012-01-01
Full Text Available In a distributed parameter estimation problem, during each sampling instant, a typical sensor node communicates its estimate either by the diffusion algorithm or by the incremental algorithm. Both these conventional distributed algorithms involve significant communication overheads and, consequently, defeat the basic purpose of wireless sensor networks. In the present paper, we therefore propose two new distributed algorithms, namely, block diffusion least mean square (BDLMS and block incremental least mean square (BILMS by extending the concept of block adaptive filtering techniques to the distributed adaptation scenario. The performance analysis of the proposed BDLMS and BILMS algorithms has been carried out and found to have similar performances to those offered by conventional diffusion LMS and incremental LMS algorithms, respectively. The convergence analyses of the proposed algorithms obtained from the simulation study are also found to be in agreement with the theoretical analysis. The remarkable and interesting aspect of the proposed block-based algorithms is that their communication overheads per node and latencies are less than those of the conventional algorithms by a factor as high as the block size used in the algorithms.
Telecommunications network modelling, planning and design
Evans, Sharon
2003-01-01
Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.
Modeling gene regulatory networks: A network simplification algorithm
Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.
2016-12-01
Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.
In search for a robust design of environmental sensor networks.
Budi, Setia; Susanto, Ferry; de Souza, Paulo; Timms, Greg; Malhotra, Vishv; Turner, Paul
2018-03-01
This paper presents an approach to the design of environmental sensor networks (ESN) which aims at providing a robust, fit-for-purpose network with minimum redundancy. A set of near optimum ESN designs is sought using an evolutionary algorithm, which incorporates redundancy and robustness as fitness functions. This work can assist the decision-making process when determining the number of sensor nodes and how the nodes are going to be deployed in the region of interest.
Algorithmic Complexity and Reprogrammability of Chemical Structure Networks
Zenil, Hector
2018-02-16
Here we address the challenge of profiling causal properties and tracking the transformation of chemical compounds from an algorithmic perspective. We explore the potential of applying a computational interventional calculus based on the principles of algorithmic probability to chemical structure networks. We profile the sensitivity of the elements and covalent bonds in a chemical structure network algorithmically, asking whether reprogrammability affords information about thermodynamic and chemical processes involved in the transformation of different compound classes. We arrive at numerical results suggesting a correspondence between some physical, structural and functional properties. Our methods are capable of separating chemical classes that reflect functional and natural differences without considering any information about atomic and molecular properties. We conclude that these methods, with their links to chemoinformatics via algorithmic, probability hold promise for future research.
Review Of Fast Multiplication Algorithms For Embedded Systems Design
Mohammed Mosab Asad; Ibrahim Marouf; Qasem Abu Al-Haija
2017-01-01
This paper presents a review with numerical example and complexity analysis of the fast multiplication algorithms to help embedded system designers to improve the hardware performance for many applications such as cryptosystems design. The paper presented two practical multiplication algorithms Karatsuba multiplication algorithm with time complexity Onamp12310logamp123112amp82893 and SchnhageStrassen multiplication algorithm with the run-time bit complexity defined as On logamp8289n logamp8...
Competitive Liner Shipping Network Design
DEFF Research Database (Denmark)
Karsten, Christian Vad
The goal of this thesis is to develop decision support tools, which can be used to optimize container shipping networks while supporting competitive transportation services. The competitiveness of container liner shipping is to a high degree determined by transportation times and number of transs......The goal of this thesis is to develop decision support tools, which can be used to optimize container shipping networks while supporting competitive transportation services. The competitiveness of container liner shipping is to a high degree determined by transportation times and number...... of transshipments on the most important sailing routes. The proposed methods in this thesis, aimed at liner shipping network design, integrate competitiveness such that the fuel consumption per transported container is reduced without increasing the transit times.A well-designed route net is decisive for container...... shipping company earnings.The operation of the route net constitute the majority of the total costs, so it is essential to achieve a good capacity utilization in a route plan with travel times that satisfy customer requirements. Most academic articles dealing with the design of container networks neither...
Designing Indonesian Liner Shipping Network
Directory of Open Access Journals (Sweden)
Armand Omar Moeis
2017-06-01
Full Text Available As the largest archipelago nation in the world, Indonesia’s logistics system has not shown excellence according to the parameters of logistics performance index and based on logistics costs percentages from overall GDP. This is due to the imbalances of trading on the western and eastern regions in Indonesia, which impacts the transportation systems costs to and from the eastern regions. Therefore, it is imperative to improve the competitiveness of Indonesian maritime logistics through maritime logistics network design. This research will focus on three levels of decision making in logistics network design, which include type of ships in the strategic level, shipping routes in the tactical level, and container allocation in the operational level with implementing butterfly routes in Indonesia’s logistics networking problems. Furthermore, this research will analyze the impact of Pendulum Nusantara and Sea Toll routes against the company profits and percentages of containers shipped. This research will also foresee how demand uncertainties and multi-period planning should affect decision making in designing the Indonesian Liner Shipping Network.
Classifying epilepsy diseases using artificial neural networks and genetic algorithm.
Koçer, Sabri; Canal, M Rahmi
2011-08-01
In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop (QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers of hidden layer in the training process. This study shows that the artificial neural network increases the classification performance using genetic algorithm.
Algorithm for queueing networks with multi-rate traffic
DEFF Research Database (Denmark)
Iversen, Villy Bæk; Ko, King-Tim
2011-01-01
In this paper we present a new algorithm for evaluating queueing networks with multi-rate traffic. The detailed state space of a node is evaluated by explicit formulæ. We consider reversible nodes with multi-rate traffic and find the state probabilities by taking advantage of local balance. Theory...... is reversibility which implies that the arrival process and departure process are identical processes, for example state-dependent Poisson processes. This property is equivalent to reversibility. Due to product form, an open network with multi-rate traffic is easy to evaluate by convolution algorithms because...... the nodes behave as independent nodes. For closed queueing networks with multiple servers in every node and multi-rate services we may apply multidimensional convolution algorithm to aggregate the nodes so that we end up with two nodes, the aggregated node and a single node, for which we can calculate...
An Efficient Tabu Search DSA Algorithm for Heterogeneous Traffic in Cellular Networks
Kamal, Hany; Coupechoux, Marceau; Godlewski, Philippe
2010-01-01
International audience; In this paper, we propose and analyze a TS (Tabu Search) algorithm for DSA (Dynamic Spectrum Access) in cellular networks. We consider a scenario where cellular operators share a common access band, and we focus on the strategy of one operator providing packet services to the end-users. We consider a soft interference requirement for the algorithm's design that suits the packet traffic context. The operator's objective is to maximize its reward while taking into accoun...
Low-cost design of next generation SONET/SDH networks with multiple constraints
CSIR Research Space (South Africa)
Karem, TR
2007-07-01
Full Text Available on constraints programming satisfaction technology is proposed. The algorithm is tested in OPNET simulation environment using different network models derived from a hypothetical case study of an optical network design for Bellville area in Cape Town, South...
Models and algorithms for biomolecules and molecular networks
DasGupta, Bhaskar
2016-01-01
By providing expositions to modeling principles, theories, computational solutions, and open problems, this reference presents a full scope on relevant biological phenomena, modeling frameworks, technical challenges, and algorithms. * Up-to-date developments of structures of biomolecules, systems biology, advanced models, and algorithms * Sampling techniques for estimating evolutionary rates and generating molecular structures * Accurate computation of probability landscape of stochastic networks, solving discrete chemical master equations * End-of-chapter exercises
Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan
2017-01-01
The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV’s parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power
Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan
2017-05-04
The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV's parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power
Directory of Open Access Journals (Sweden)
Ning Li
2017-05-01
Full Text Available The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs project is to make autonomous underwater vehicles (AUVs, remote operated vehicles (ROVs and unmanned surface vehicles (USVs more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV’s parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the
Fast, Distributed Algorithms in Deep Networks
2016-05-11
dataset consists of images of house numbers taken from the Google Streetview car . Each data point consisted of a cropped image of a single digit which...1989. [9] Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, volume 1...Bengio. Understanding the difficult of training deep feed- forward neural networks. In International Conference on Artificial Intelligence and Statistics
A source location algorithm of lightning detection networks in China
Directory of Open Access Journals (Sweden)
Z. X. Hu
2010-10-01
Full Text Available Fast and accurate retrieval of lightning sources is crucial to the early warning and quick repairs of lightning disaster. An algorithm for computing the location and onset time of cloud-to-ground lightning using the time-of-arrival (TOA and azimuth-of-arrival (AOA data is introduced in this paper. The algorithm can iteratively calculate the least-squares solution of a lightning source on an oblate spheroidal Earth. It contains a set of unique formulas to compute the geodesic distance and azimuth and an explicit method to compute the initial position using TOA data of only three sensors. Since the method accounts for the effects of the oblateness of the Earth, it would provide a more accurate solution than algorithms based on planar or spherical surface models. Numerical simulations are presented to test this algorithm and evaluate the performance of a lightning detection network in the Hubei province of China. Since 1990s, the proposed algorithm has been used in many regional lightning detection networks installed by the electric power system in China. It is expected that the proposed algorithm be used in more lightning detection networks and other location systems.
Advanced Algorithm for Optimizing the Deployment Cost of Passive Optical Networks
Directory of Open Access Journals (Sweden)
Pavel Lafata
2013-01-01
Full Text Available The deployment of passive optical networks (PONs is slow today, especially in Europe, because completely new optical infrastructures are necessary to be installed in the last-mile segments of access networks, which is always very expensive process. One of the possibilities is to design economically effective topologies and to optimize the deployment cost. This article describes the method leading to evaluate an algorithm for designing suboptimal economic solutions and topologies for PONs by focusing on optimization of constructional length of distribution networks. While the typical PON topologies are star topologies or tree-star topologies, the first part of this article introduces new sub algorithm for estimating the minimum star topology. The next section brings the evaluation of two sub algorithms for solving minimum constructional length problems. Finally, all these parts will be merged into a complex algorithm by using clusterization technique to solve optimum topologies. However, the current version of presented algorithm is purely based on mathematical theories and was implemented in Matlab environment. Therefore, it is able to design only theoretical optimum topologies without taking external conditions and real limitations into account. These real conditions will be further implemented in the future, so the algorithm could be also used for practical applications.
Algorithm for queueing networks with multi-rate traffic
DEFF Research Database (Denmark)
Iversen, Villy Bæk; King-Tim, Ko
2011-01-01
In this paper we present a new algorithm for evaluating queueing networks with multi-rate traffic. The detailed state space of a node is evaluated by explicit formulæ. We consider reversible nodes with multi-rate traffic and find the state probabilities by taking advantage of local balance. Theory...... is reversibility which implies that the arrival process and departure process are identical processes, for example state-dependent Poisson processes. This property is equivalent to reversibility. Due to product form, an open network with multi-rate traffic is easy to evaluate by convolution algorithms because...
Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm
Directory of Open Access Journals (Sweden)
Jianyong Liu
2015-01-01
Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.
Design and Analysis of Stimulated Raman Scattering-Aware Algorithm in RWA
Sim, Wai S.; Tan, Saw C.; Yusoff, Zulfadzli
2017-05-01
A stimulated Raman scattering (SRS)-aware routing and wavelength assignment (RWA) scheme, called assign minimum interference and shortest algorithm, is proposed to minimize the effect of SRS in network. The design parameter, the number of interference of routes in the proposed algorithm is investigated to analyze its capabilities of influence the network performance. The various setting of the parameter is tested in 15-nodes Mesh and 14-nodes National Science Foundation networks for analysis in order to provide useful guidelines for designing effective SRS-aware RWA scheme. The ultimate objective is to define the necessary and sufficient rules that must be followed in the proposed algorithm by identifying the necessary RWA parameters in order to minimize the effect of SRS in optical networks. The result shows that the smaller the number of interference of routes, the lower the blocking probability in the mentioned topologies.
Report on dynamic speed harmonization and queue warning algorithm design.
2014-02-01
This report provides a detailed description of the algorithms that will be used to generate harmonized recommended speeds : and queue warning information in the proposed Intelligent Network Flow Optimization (INFLO) prototype. This document : describ...
Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm.
Wu, Haizhou; Zhou, Yongquan; Luo, Qifang; Basset, Mohamed Abdel
2016-01-01
Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs). In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared.
A Vehicle Detection Algorithm Based on Deep Belief Network
Directory of Open Access Journals (Sweden)
Hai Wang
2014-01-01
Full Text Available Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. Traditional shallow model based vehicle detection algorithm still cannot meet the requirement of accurate vehicle detection in these applications. In this work, a novel deep learning based vehicle detection algorithm with 2D deep belief network (2D-DBN is proposed. In the algorithm, the proposed 2D-DBN architecture uses second-order planes instead of first-order vector as input and uses bilinear projection for retaining discriminative information so as to determine the size of the deep architecture which enhances the success rate of vehicle detection. On-road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets.
Comparison and evaluation of network clustering algorithms applied to genetic interaction networks.
Hou, Lin; Wang, Lin; Berg, Arthur; Qian, Minping; Zhu, Yunping; Li, Fangting; Deng, Minghua
2012-01-01
The goal of network clustering algorithms detect dense clusters in a network, and provide a first step towards the understanding of large scale biological networks. With numerous recent advances in biotechnologies, large-scale genetic interactions are widely available, but there is a limited understanding of which clustering algorithms may be most effective. In order to address this problem, we conducted a systematic study to compare and evaluate six clustering algorithms in analyzing genetic interaction networks, and investigated influencing factors in choosing algorithms. The algorithms considered in this comparison include hierarchical clustering, topological overlap matrix, bi-clustering, Markov clustering, Bayesian discriminant analysis based community detection, and variational Bayes approach to modularity. Both experimentally identified and synthetically constructed networks were used in this comparison. The accuracy of the algorithms is measured by the Jaccard index in comparing predicted gene modules with benchmark gene sets. The results suggest that the choice differs according to the network topology and evaluation criteria. Hierarchical clustering showed to be best at predicting protein complexes; Bayesian discriminant analysis based community detection proved best under epistatic miniarray profile (EMAP) datasets; the variational Bayes approach to modularity was noticeably better than the other algorithms in the genome-scale networks.
Acoustic Performance of Exhaust Muffler based Genetic Algorithms and Artificial Neural Network
Directory of Open Access Journals (Sweden)
Wang Xiao Li
2013-07-01
Full Text Available The noise level was one of the important indicators as a measure of the quality and performance of the diesel engine, exhaust noise in diesel engines machine noise accounted for an important proportion of installed performance exhaust mufflerwas an effective way to control exhaust noise. This article using orthogonal test program was to the muffler structure parameters as input to the sound pressure level and diesel fuel each output artificial neural network (BP network learning sample. Matlab artificial neural network toolbox to complete the training of the network, and better noise performance and fuel consumption rate performance muffler internal structure parameters combination was obtained through genetic algorithm gifted collaborative validation of artificial neural networks and genetic algorithms to optimize application exhaust muffler design is entirely feasible
AdaBoost-based algorithm for network intrusion detection.
Hu, Weiming; Hu, Wei; Maybank, Steve
2008-04-01
Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.
Spectral algorithms for heterogeneous biological networks.
McDonald, Martin; Higham, Desmond J; Vass, J Keith
2012-11-01
Spectral methods, which use information relating to eigenvectors, singular vectors and generalized singular vectors, help us to visualize and summarize sets of pairwise interactions. In this work, we motivate and discuss the use of spectral methods by taking a matrix computation view and applying concepts from applied linear algebra. We show that this unified approach is sufficiently flexible to allow multiple sources of network information to be combined. We illustrate the methods on microarray data arising from a large population-based study in human adipose tissue, combined with related information concerning metabolic pathways.
AN INTELLIGENT VERTICAL HANDOVER DECISION ALGORITHM FOR WIRELESS HETEROGENEOUS NETWORKS
V. Anantha Narayanan; Rajeswari, A; Sureshkumar, V.
2014-01-01
The Next Generation Wireless Networks (NGWN) should be compatible with other communication technologies to offer the best connectivity to the mobile terminal which can access any IP based services at any time from any network without the knowledge of its user. It requires an intelligent vertical handover decision making algorithm to migrate between technologies that enable seamless mobility, always best connection and minimal terminal power consumption. Currently existing decision engines are...
Sensor and ad-hoc networks theoretical and algorithmic aspects
Makki, S Kami; Pissinou, Niki; Makki, Shamila; Karimi, Masoumeh
2008-01-01
This book brings together leading researchers and developers in the field of wireless sensor networks to explain the special problems and challenges of the algorithmic aspects of sensor and ad-hoc networks. The book also fosters communication not only between the different sensor and ad-hoc communities, but also between those communities and the distributed systems and information systems communities. The topics addressed pertain to the sensors and mobile environment.
Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data
2015-07-01
Bayesian networks. In IJCNN, pp. 2391– 2396. Ghahramani, Z., & Jordan, M. I. (1997). Factorial hidden markov models. Machine Learning, 29(2-3), 245–273...algorithms like EM (which require inference). 1 INTRODUCTION When learning the parameters of a Bayesian network from data with missing values, the...missing at random assumption (MAR), but also for a broad class of data that is not MAR. Their analysis is based on a graphical representation for
Applied Graph-Mining Algorithms to Study Biomolecular Interaction Networks
2014-01-01
Protein-protein interaction (PPI) networks carry vital information on the organization of molecular interactions in cellular systems. The identification of functionally relevant modules in PPI networks is one of the most important applications of biological network analysis. Computational analysis is becoming an indispensable tool to understand large-scale biomolecular interaction networks. Several types of computational methods have been developed and employed for the analysis of PPI networks. Of these computational methods, graph comparison and module detection are the two most commonly used strategies. This review summarizes current literature on graph kernel and graph alignment methods for graph comparison strategies, as well as module detection approaches including seed-and-extend, hierarchical clustering, optimization-based, probabilistic, and frequent subgraph methods. Herein, we provide a comprehensive review of the major algorithms employed under each theme, including our recently published frequent subgraph method, for detecting functional modules commonly shared across multiple cancer PPI networks. PMID:24800226
Fast grid layout algorithm for biological networks with sweep calculation.
Kojima, Kaname; Nagasaki, Masao; Miyano, Satoru
2008-06-15
Properly drawn biological networks are of great help in the comprehension of their characteristics. The quality of the layouts for retrieved biological networks is critical for pathway databases. However, since it is unrealistic to manually draw biological networks for every retrieval, automatic drawing algorithms are essential. Grid layout algorithms handle various biological properties such as aligning vertices having the same attributes and complicated positional constraints according to their subcellular localizations; thus, they succeed in providing biologically comprehensible layouts. However, existing grid layout algorithms are not suitable for real-time drawing, which is one of requisites for applications to pathway databases, due to their high-computational cost. In addition, they do not consider edge directions and their resulting layouts lack traceability for biochemical reactions and gene regulations, which are the most important features in biological networks. We devise a new calculation method termed sweep calculation and reduce the time complexity of the current grid layout algorithms through its encoding and decoding processes. We conduct practical experiments by using 95 pathway models of various sizes from TRANSPATH and show that our new grid layout algorithm is much faster than existing grid layout algorithms. For the cost function, we introduce a new component that penalizes undesirable edge directions to avoid the lack of traceability in pathways due to the differences in direction between in-edges and out-edges of each vertex. Java implementations of our layout algorithms are available in Cell Illustrator. masao@ims.u-tokyo.ac.jp Supplementary data are available at Bioinformatics online.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
2016-01-01
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.
Ripple-Spreading Network Model Optimization by Genetic Algorithm
Directory of Open Access Journals (Sweden)
Xiao-Bing Hu
2013-01-01
Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.
Optimum Water Quality Monitoring Network Design for Bidirectional River Systems.
Zhu, Xiaohui; Yue, Yong; Wong, Prudence W H; Zhang, Yixin; Tan, Jianhong
2018-01-24
Affected by regular tides, bidirectional water flows play a crucial role in surface river systems. Using optimization theory to design a water quality monitoring network can reduce the redundant monitoring nodes as well as save the costs for building and running a monitoring network. A novel algorithm is proposed to design an optimum water quality monitoring network for tidal rivers with bidirectional water flows. Two optimization objectives of minimum pollution detection time and maximum pollution detection probability are used in our optimization algorithm. We modify the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and develop new fitness functions to calculate pollution detection time and pollution detection probability in a discrete manner. In addition, the Storm Water Management Model (SWMM) is used to simulate hydraulic characteristics and pollution events based on a hypothetical river system studied in the literature. Experimental results show that our algorithm can obtain a better Pareto frontier. The influence of bidirectional water flows to the network design is also identified, which has not been studied in the literature. Besides that, we also find that the probability of bidirectional water flows has no effect on the optimum monitoring network design but slightly changes the mean pollution detection time.
Optimum Water Quality Monitoring Network Design for Bidirectional River Systems
Directory of Open Access Journals (Sweden)
Xiaohui Zhu
2018-01-01
Full Text Available Affected by regular tides, bidirectional water flows play a crucial role in surface river systems. Using optimization theory to design a water quality monitoring network can reduce the redundant monitoring nodes as well as save the costs for building and running a monitoring network. A novel algorithm is proposed to design an optimum water quality monitoring network for tidal rivers with bidirectional water flows. Two optimization objectives of minimum pollution detection time and maximum pollution detection probability are used in our optimization algorithm. We modify the Multi-Objective Particle Swarm Optimization (MOPSO algorithm and develop new fitness functions to calculate pollution detection time and pollution detection probability in a discrete manner. In addition, the Storm Water Management Model (SWMM is used to simulate hydraulic characteristics and pollution events based on a hypothetical river system studied in the literature. Experimental results show that our algorithm can obtain a better Pareto frontier. The influence of bidirectional water flows to the network design is also identified, which has not been studied in the literature. Besides that, we also find that the probability of bidirectional water flows has no effect on the optimum monitoring network design but slightly changes the mean pollution detection time.
Jafari, Mohieddin; Mirzaie, Mehdi; Sadeghi, Mehdi
2015-10-05
In the field of network science, exploring principal and crucial modules or communities is critical in the deduction of relationships and organization of complex networks. This approach expands an arena, and thus allows further study of biological functions in the field of network biology. As the clustering algorithms that are currently employed in finding modules have innate uncertainties, external and internal validations are necessary. Sequence and network structure alignment, has been used to define the Interlog Protein Network (IPN). This network is an evolutionarily conserved network with communal nodes and less false-positive links. In the current study, the IPN is employed as an evolution-based benchmark in the validation of the module finding methods. The clustering results of five algorithms; Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Cartographic Representation (CR), Laplacian Dynamics (LD) and Genetic Algorithm; to find communities in Protein-Protein Interaction networks (GAPPI) are assessed by IPN in four distinct Protein-Protein Interaction Networks (PPINs). The MCL shows a more accurate algorithm based on this evolutionary benchmarking approach. Also, the biological relevance of proteins in the IPN modules generated by MCL is compatible with biological standard databases such as Gene Ontology, KEGG and Reactome. In this study, the IPN shows its potential for validation of clustering algorithms due to its biological logic and straightforward implementation.
New Combustion CFD Algorithms Designed for Rapid GPU Computations Project
National Aeronautics and Space Administration — We propose development of new algorithms specifically designed to exploit the highly parallel structure of graphics processing units (GPUs) for performing the...
HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN
While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...
Formulations and algorithms to design communication networks
Fernanda Sumika Hojo de Souza
2012-01-01
O estudo de redes tem raízes na teoria dos grafos que remota a 1730. Desde então, redes vem tem sido utilizadas para modelar e simular interações entre elementos de sistemas complexos, tais como de transporte, de comunicação e de computadores. Redes de comunicação são amplamente utilizadas para trocar informações entre entidades de um sistema. A importância das redes de comunicação aumentou dramaticamente nos últimos anos, chamando atenção para o estágio de projeto de um sistema real, dando o...
Analog Circuit Design Optimization Based on Evolutionary Algorithms
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Mansour Barari
2014-01-01
Full Text Available This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs. Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.
An enhanced simulated annealing routing algorithm for semi-diagonal torus network
Adzhar, Noraziah; Salleh, Shaharuddin
2017-09-01
Multiprocessor is another great technology that helps in advancing human civilization due to high demands for solving complex problems. A multiprocessing system can have a lot of replicated processor-memory pairs (henceforth regard as net) or also called as processing nodes. Each of these nodes is connected to each other through interconnection networks and passes message using a standard message passing mechanism. In this paper, we present a routing algorithm based on enhanced simulated annealing technique to provide the connection between nodes in a semi-diagonal torus (SD-Torus) network. This network is both symmetric and regular; thus, make it very beneficial in the implementation process. The main objective is to maximize the number of established connection between nodes in this SD-Torus network. In order to achieve this objective, each node must be connected in its shortest way as possible. We start our algorithm by designing shortest path algorithm based on Dijkstra’s method. While this algorithm guarantees to find the shortest path for each single net, if it exists, each routed net will form obstacle for later paths. This increases the complexity to route later nets and makes routing longer than optimal, or sometimes impossible to complete. The solution is further refined by re-routing all nets in different orders using simulated annealing method. Through simulation program, our proposed algorithm succeeded in performing complete routing up to 81 nodes with 40 nets in 9×9 SD-Torus network size.
Zhang, Lin; Yin, Na; Fu, Xiong; Lin, Qiaomin; Wang, Ruchuan
2017-01-01
With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR). This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes’ reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes’ communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service. PMID:28282894
Zhang, Ying; Wang, Jun; Hao, Guan
2018-01-08
With the development of autonomous unmanned intelligent systems, such as the unmanned boats, unmanned planes and autonomous underwater vehicles, studies on Wireless Sensor-Actor Networks (WSANs) have attracted more attention. Network connectivity algorithms play an important role in data exchange, collaborative detection and information fusion. Due to the harsh application environment, abnormal nodes often appear, and the network connectivity will be prone to be lost. Network self-healing mechanisms have become critical for these systems. In order to decrease the movement overhead of the sensor-actor nodes, an autonomous connectivity restoration algorithm based on finite state machine is proposed. The idea is to identify whether a node is a critical node by using a finite state machine, and update the connected dominating set in a timely way. If an abnormal node is a critical node, the nearest non-critical node will be relocated to replace the abnormal node. In the case of multiple node abnormality, a regional network restoration algorithm is introduced. It is designed to reduce the overhead of node movements while restoration happens. Simulation results indicate the proposed algorithm has better performance on the total moving distance and the number of total relocated nodes compared with some other representative restoration algorithms.
Directory of Open Access Journals (Sweden)
Lin Zhang
2017-03-01
Full Text Available With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR. This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes’ reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes’ communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service.
Zhang, Lin; Yin, Na; Fu, Xiong; Lin, Qiaomin; Wang, Ruchuan
2017-03-08
With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR). This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes' reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes' communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service.
Optimized feed-forward neural-network algorithm trained for cyclotron-cavity modeling
Mohamadian, Masoumeh; Afarideh, Hossein; Ghergherehchi, Mitra
2017-01-01
The cyclotron cavity presented in this paper is modeled by a feed-forward neural network trained by the authors’ optimized back-propagation (BP) algorithm. The training samples were obtained from simulation results that are for a number of defined situations and parameters and were achieved parametrically using MWS CST software; furthermore, the conventional BP algorithm with different hidden-neuron numbers, structures, and other optimal parameters such as learning rate that are applied for our purpose was also used here. The present study shows that an optimized FFN can be used to estimate the cyclotron-model parameters with an acceptable error function. A neural network trained by an optimized algorithm therefore shows a proper approximation and an acceptable ability regarding the modeling of the proposed structure. The cyclotron-cavity parameter-modeling results demonstrate that an FNN that is trained by the optimized algorithm could be a suitable method for the estimation of the design parameters in this case.
An efficient Lagrangean relaxation-based object tracking algorithm in wireless sensor networks.
Lin, Frank Yeong-Sung; Lee, Cheng-Ta
2010-01-01
In this paper we propose an energy-efficient object tracking algorithm in wireless sensor networks (WSNs). Such sensor networks have to be designed to achieve energy-efficient object tracking for any given arbitrary topology. We consider in particular the bi-directional moving objects with given frequencies for each pair of sensor nodes and link transmission cost. This problem is formulated as a 0/1 integer-programming problem. A Lagrangean relaxation-based (LR-based) heuristic algorithm is proposed for solving the optimization problem. Experimental results showed that the proposed algorithm achieves near optimization in energy-efficient object tracking. Furthermore, the algorithm is very efficient and scalable in terms of the solution time.
DS+: Reliable Distributed Snapshot Algorithm for Wireless Sensor Networks
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Gamze Uslu
2013-01-01
Full Text Available Acquiring the snapshot of a distributed system helps gathering system related global state. In wireless sensor networks (WSNs, global state shows if a node is terminated or deadlock occurs along with many other situations which prevents a WSN from fully functioning. In this paper, we present a fully distributed snapshot acquisition algorithm adapted to tree topology wireless sensor networks (WSNs. Since snapshot acquisition is through control messages sent over highly lossy wireless channels and congested nodes, we enhanced the snapshot algorithm with a sink based reliability suit to achieve robustness. We analyzed the performance of the algorithm in terms of snapshot success ratio and response time in simulation and experimental small test bed environment. The results reveal that the proposed tailor made reliability model increases snapshot acquisition performance by a factor of seven and response time by a factor of two in a 30-node network. We have also shown that the proposed algorithm outperforms its counterparts in the specified network setting.
The power-series algorithm for Markovian queueing networks
van den Hout, W.B.; Blanc, J.P.C.
1994-01-01
A newversion of the Power-Series Algorithm is developed to compute the steady-state distribution of a rich class of Markovian queueing networks. The arrival process is a Multi-queue Markovian Arrival Process, which is a multi-queue generalization of the BMAP. It includes Poisson, fork and
Multimedia over cognitive radio networks algorithms, protocols, and experiments
Hu, Fei
2014-01-01
PrefaceAbout the EditorsContributorsNetwork Architecture to Support Multimedia over CRNA Management Architecture for Multimedia Communication in Cognitive Radio NetworksAlexandru O. Popescu, Yong Yao, Markus Fiedler , and Adrian P. PopescuPaving a Wider Way for Multimedia over Cognitive Radios: An Overview of Wideband Spectrum Sensing AlgorithmsBashar I. Ahmad, Hongjian Sun, Cong Ling, and Arumugam NallanathanBargaining-Based Spectrum Sharing for Broadband Multimedia Services in Cognitive Radio NetworkYang Yan, Xiang Chen, Xiaofeng Zhong, Ming Zhao, and Jing WangPhysical Layer Mobility Challen
The No-Prop algorithm: a new learning algorithm for multilayer neural networks.
Widrow, Bernard; Greenblatt, Aaron; Kim, Youngsik; Park, Dookun
2013-01-01
A new learning algorithm for multilayer neural networks that we have named No-Propagation (No-Prop) is hereby introduced. With this algorithm, the weights of the hidden-layer neurons are set and fixed with random values. Only the weights of the output-layer neurons are trained, using steepest descent to minimize mean square error, with the LMS algorithm of Widrow and Hoff. The purpose of introducing nonlinearity with the hidden layers is examined from the point of view of Least Mean Square Error Capacity (LMS Capacity), which is defined as the maximum number of distinct patterns that can be trained into the network with zero error. This is shown to be equal to the number of weights of each of the output-layer neurons. The No-Prop algorithm and the Back-Prop algorithm are compared. Our experience with No-Prop is limited, but from the several examples presented here, it seems that the performance regarding training and generalization of both algorithms is essentially the same when the number of training patterns is less than or equal to LMS Capacity. When the number of training patterns exceeds Capacity, Back-Prop is generally the better performer. But equivalent performance can be obtained with No-Prop by increasing the network Capacity by increasing the number of neurons in the hidden layer that drives the output layer. The No-Prop algorithm is much simpler and easier to implement than Back-Prop. Also, it converges much faster. It is too early to definitively say where to use one or the other of these algorithms. This is still a work in progress. Copyright © 2012 Elsevier Ltd. All rights reserved.
Designing a Rational Distribution Network for Trading Companies
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Dybskaya V. V.
2017-09-01
Full Text Available This article considers the modern methods and approaches to design the company’s distribution network. The authors point out the relevance of this problem for the modern trading and manufacturing companies, give examples of the strategic goal setting of the company in the logistics network reorganization, and the benchmarks of possible economic effects of its conduction. The work reviews the scientific articles of contemporary American, European and Russian authors devoted to the approaches, concerning the implementation of projects for designing a distribution network, methods and models for its optimization. The article concludes that there is no single “language” and an approach to design the logistics networks, with a proper level of detail that takes into account the strategic features and industry specificity of the certain company. The authors propose an algorithm for designing a rational distribution network.
A robust regularization algorithm for polynomial networks for machine learning
Jaenisch, Holger M.; Handley, James W.
2011-06-01
We present an improvement to the fundamental Group Method of Data Handling (GMDH) Data Modeling algorithm that overcomes the parameter sensitivity to novel cases presented to derived networks. We achieve this result by regularization of the output and using a genetic weighting that selects intermediate models that do not exhibit divergence. The result is the derivation of multi-nested polynomial networks following the Kolmogorov-Gabor polynomial that are robust to mean estimators as well as novel exemplars for input. The full details of the algorithm are presented. We also introduce a new method for approximating GMDH in a single regression model using F, H, and G terms that automatically exports the answers as ordinary differential equations. The MathCAD 15 source code for all algorithms and results are provided.
Robust Optimization of Fourth Party Logistics Network Design under Disruptions
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Jia Li
2015-01-01
Full Text Available The Fourth Party Logistics (4PL network faces disruptions of various sorts under the dynamic and complex environment. In order to explore the robustness of the network, the 4PL network design with consideration of random disruptions is studied. The purpose of the research is to construct a 4PL network that can provide satisfactory service to customers at a lower cost when disruptions strike. Based on the definition of β-robustness, a robust optimization model of 4PL network design under disruptions is established. Based on the NP-hard characteristic of the problem, the artificial fish swarm algorithm (AFSA and the genetic algorithm (GA are developed. The effectiveness of the algorithms is tested and compared by simulation examples. By comparing the optimal solutions of the 4PL network for different robustness level, it is indicated that the robust optimization model can evade the market risks effectively and save the cost in the maximum limit when it is applied to 4PL network design.
A Compression Algorithm in Wireless Sensor Networks of Bearing Monitoring
Energy Technology Data Exchange (ETDEWEB)
Zheng Bin; Meng Qingfeng; Wang Nan [Theory of Lubrication and Bearing Institute, Xi' an Jiaotong University Xi' an, 710049 (China); Li Zhi, E-mail: rthree.zhengbin@stu.xjtu.edu.cn [Dalian Machine Tool Group Corp. Dalian, 116620 (China)
2011-07-19
The energy consumption of wireless sensor networks (WSNs) is always an important problem in the application of wireless sensor networks. This paper proposes a data compression algorithm to reduce amount of data and energy consumption during the data transmission process in the on-line WSNs-based bearing monitoring system. The proposed compression algorithm is based on lifting wavelets, Zerotree coding and Hoffman coding. Among of that, 5/3 lifting wavelets is used for dividing data into different frequency bands to extract signal characteristics. Zerotree coding is applied to calculate the dynamic thresholds to retain the attribute data. The attribute data are then encoded by Hoffman coding to further enhance the compression ratio. In order to validate the algorithm, simulation is carried out by using Matlab. The result of simulation shows that the proposed algorithm is very suitable for the compression of bearing monitoring data. The algorithm has been successfully used in online WSNs-based bearing monitoring system, in which TI DSP TMS320F2812 is used to realize the algorithm.
Configurable intelligent optimization algorithm design and practice in manufacturing
Tao, Fei; Laili, Yuanjun
2014-01-01
Presenting the concept and design and implementation of configurable intelligent optimization algorithms in manufacturing systems, this book provides a new configuration method to optimize manufacturing processes. It provides a comprehensive elaboration of basic intelligent optimization algorithms, and demonstrates how their improvement, hybridization and parallelization can be applied to manufacturing. Furthermore, various applications of these intelligent optimization algorithms are exemplified in detail, chapter by chapter. The intelligent optimization algorithm is not just a single algorit
Space mapping optimization algorithms for engineering design
DEFF Research Database (Denmark)
Koziel, Slawomir; Bandler, John W.; Madsen, Kaj
2006-01-01
A simple, efficient optimization algorithm based on space mapping (SM) is presented. It utilizes input SM to reduce the misalignment between the coarse and fine models of the optimized object over a region of interest, and output space mapping (OSM) to ensure matching of response and first......-order derivatives between the mapped coarse model and the fine model at the current iteration point. We also consider an enhanced version in which the input SM coefficients are frequency dependent. The performance of our new algorithms is comparable with the recently published SMIS algorithm when applied...
Metaheuristic Algorithms for Convolution Neural Network.
Rere, L M Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni
2016-01-01
A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).
Metaheuristic Algorithms for Convolution Neural Network
Directory of Open Access Journals (Sweden)
L. M. Rasdi Rere
2016-01-01
Full Text Available A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN, a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent.
An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.
Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen
2016-01-01
The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.
IJA: An Efficient Algorithm for Query Processing in Sensor Networks
Directory of Open Access Journals (Sweden)
Dong Hwa Kim
2011-01-01
Full Text Available One of main features in sensor networks is the function that processes real time state information after gathering needed data from many domains. The component technologies consisting of each node called a sensor node that are including physical sensors, processors, actuators and power have advanced significantly over the last decade. Thanks to the advanced technology, over time sensor networks have been adopted in an all-round industry sensing physical phenomenon. However, sensor nodes in sensor networks are considerably constrained because with their energy and memory resources they have a very limited ability to process any information compared to conventional computer systems. Thus query processing over the nodes should be constrained because of their limitations. Due to the problems, the join operations in sensor networks are typically processed in a distributed manner over a set of nodes and have been studied. By way of example while simple queries, such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor networks environments. At the same time, the simulation result shows that the proposed IJA algorithm significantly reduces the number of bytes to be moved to join nodes compared to the popular synopsis join algorithm.
IJA: An Efficient Algorithm for Query Processing in Sensor Networks
Lee, Hyun Chang; Lee, Young Jae; Lim, Ji Hyang; Kim, Dong Hwa
2011-01-01
One of main features in sensor networks is the function that processes real time state information after gathering needed data from many domains. The component technologies consisting of each node called a sensor node that are including physical sensors, processors, actuators and power have advanced significantly over the last decade. Thanks to the advanced technology, over time sensor networks have been adopted in an all-round industry sensing physical phenomenon. However, sensor nodes in sensor networks are considerably constrained because with their energy and memory resources they have a very limited ability to process any information compared to conventional computer systems. Thus query processing over the nodes should be constrained because of their limitations. Due to the problems, the join operations in sensor networks are typically processed in a distributed manner over a set of nodes and have been studied. By way of example while simple queries, such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor networks environments. At the same time, the simulation result shows that the proposed IJA algorithm significantly reduces the number of bytes to be moved to join nodes compared to the popular synopsis join algorithm. PMID:22319375
Genetic algorithm application in optimization of wireless sensor networks.
Norouzi, Ali; Zaim, A Halim
2014-01-01
There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs.
A blind matching algorithm for cognitive radio networks
Hamza, Doha R.
2016-08-15
We consider a cognitive radio network where secondary users (SUs) are allowed access time to the spectrum belonging to the primary users (PUs) provided that they relay primary messages. PUs and SUs negotiate over allocations of the secondary power that will be used to relay PU data. We formulate the problem as a generalized assignment market to find an epsilon pairwise stable matching. We propose a distributed blind matching algorithm (BLMA) to produce the pairwise-stable matching plus the associated power allocations. We stipulate a limited information exchange in the network so that agents only calculate their own utilities but no information is available about the utilities of any other users in the network. We establish convergence to epsilon pairwise stable matchings in finite time. Finally we show that our algorithm exhibits a limited degradation in PU utility when compared with the Pareto optimal results attained using perfect information assumptions. © 2016 IEEE.
Intelligent Control of Urban Road Networks: Algorithms, Systems and Communications
Smith, Mike
This paper considers control in road networks. Using a simple example based on the well-known Braess network [1] the paper shows that reducing delay for traffic, assuming that the traffic distribution is fixed, may increase delay when travellers change their travel choices in light of changes in control settings and hence delays. It is shown that a similar effect occurs within signal controlled networks. In this case the effect appears at first sight to be much stronger: the overall capacity of a network may be substantially reduced by utilising standard responsive signal control algorithms. In seeking to reduce delays for existing flows, these policies do not allow properly for consequent routeing changes by travellers. Control methods for signal-controlled networks that do take proper account of the reactions of users are suggested; these require further research, development, and careful real-life trials.
Adaptive Load-Balancing Algorithms using Symmetric Broadcast Networks
Das, Sajal K.; Harvey, Daniel J.; Biswas, Rupak; Biegel, Bryan A. (Technical Monitor)
2002-01-01
In a distributed computing environment, it is important to ensure that the processor workloads are adequately balanced, Among numerous load-balancing algorithms, a unique approach due to Das and Prasad defines a symmetric broadcast network (SBN) that provides a robust communication pattern among the processors in a topology-independent manner. In this paper, we propose and analyze three efficient SBN-based dynamic load-balancing algorithms, and implement them on an SGI Origin2000. A thorough experimental study with Poisson distributed synthetic loads demonstrates that our algorithms are effective in balancing system load. By optimizing completion time and idle time, the proposed algorithms are shown to compare favorably with several existing approaches.
A Message-Passing Algorithm for Wireless Network Scheduling.
Paschalidis, Ioannis Ch; Huang, Fuzhuo; Lai, Wei
2015-10-01
We consider scheduling in wireless networks and formulate it as Maximum Weighted Independent Set (MWIS) problem on a "conflict" graph that captures interference among simultaneous transmissions. We propose a novel, low-complexity, and fully distributed algorithm that yields high-quality feasible solutions. Our proposed algorithm consists of two phases, each of which requires only local information and is based on message-passing. The first phase solves a relaxation of the MWIS problem using a gradient projection method. The relaxation we consider is tighter than the simple linear programming relaxation and incorporates constraints on all cliques in the graph. The second phase of the algorithm starts from the solution of the relaxation and constructs a feasible solution to the MWIS problem. We show that our algorithm always outputs an optimal solution to the MWIS problem for perfect graphs. Simulation results compare our policies against Carrier Sense Multiple Access (CSMA) and other alternatives and show excellent performance.
Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks
Abdelgawad, Ahmed
2012-01-01
This book introduces resource-aware data fusion algorithms to gather and combine data from multiple sources (e.g., sensors) in order to achieve inferences. These techniques can be used in centralized and distributed systems to overcome sensor failure, technological limitation, and spatial and temporal coverage problems. The algorithms described in this book are evaluated with simulation and experimental results to show they will maintain data integrity and make data useful and informative. Describes techniques to overcome real problems posed by wireless sensor networks deployed in circumstances that might interfere with measurements provided, such as strong variations of pressure, temperature, radiation, and electromagnetic noise; Uses simulation and experimental results to evaluate algorithms presented and includes real test-bed; Includes case study implementing data fusion algorithms on a remote monitoring framework for sand production in oil pipelines.
Directory of Open Access Journals (Sweden)
P.-Y. Chen
2009-01-01
Full Text Available This study proposes a neural network-family competition genetic algorithm (NN-FCGA for solving the electromagnetic (EM optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are comparable to those of the FCGA, but reducing a large amount of computation time and a well-trained NN model that can serve as a nonlinear approximator was developed during the optimization process of the NN-FCGA.
A Survey of Linear Network Coding and Network Error Correction Code Constructions and Algorithms
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Michele Sanna
2011-01-01
Full Text Available Network coding was introduced by Ahlswede et al. in a pioneering work in 2000. This paradigm encompasses coding and retransmission of messages at the intermediate nodes of the network. In contrast with traditional store-and-forward networking, network coding increases the throughput and the robustness of the transmission. Linear network coding is a practical implementation of this new paradigm covered by several research works that include rate characterization, error-protection coding, and construction of codes. Especially determining the coding characteristics has its importance in providing the premise for an efficient transmission. In this paper, we review the recent breakthroughs in linear network coding for acyclic networks with a survey of code constructions literature. Deterministic construction algorithms and randomized procedures are presented for traditional network coding and for network-control network coding.
Consensus algorithm in smart grid and communication networks
Alfagee, Husain Abdulaziz
On a daily basis, consensus theory attracts more and more researches from different areas of interest, to apply its techniques to solve technical problems in a way that is faster, more reliable, and even more precise than ever before. A power system network is one of those fields that consensus theory employs extensively. The use of the consensus algorithm to solve the Economic Dispatch and Load Restoration Problems is a good example. Instead of a conventional central controller, some researchers have explored an algorithm to solve the above mentioned problems, in a distribution manner, using the consensus algorithm, which is based on calculation methods, i.e., non estimation methods, for updating the information consensus matrix. Starting from this point of solving these types of problems mentioned, specifically, in a distribution fashion, using the consensus algorithm, we have implemented a new advanced consensus algorithm. It is based on the adaptive estimation techniques, such as the Gradient Algorithm and the Recursive Least Square Algorithm, to solve the same problems. This advanced work was tested on different case studies that had formerly been explored, as seen in references 5, 7, and 18. Three and five generators, or agents, with different topologies, correspond to the Economic Dispatch Problem and the IEEE 16-Bus power system corresponds to the Load Restoration Problem. In all the cases we have studied, the results met our expectations with extreme accuracy, and completely matched the results of the previous researchers. There is little question that this research proves the capability and dependability of using the consensus algorithm, based on the estimation methods as the Gradient Algorithm and the Recursive Least Square Algorithm to solve such power problems.
Properties of healthcare teaming networks as a function of network construction algorithms
Trayhan, Melissa; Farooq, Samir A.; Fucile, Christopher; Ghoshal, Gourab; White, Robert J.; Quill, Caroline M.; Rosenberg, Alexander; Barbosa, Hugo Serrano; Bush, Kristen; Chafi, Hassan; Boudreau, Timothy
2017-01-01
Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other, and to map how patients traverse the network of providers. Most healthcare service network models have been constructed from patient claims data, using billing claims to link a patient with a specific provider in time. The data sets can be quite large (106–108 individual claims per year), making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks, which as we demonstrate, can be dramatically different. To address this issue, we compared the properties of healthcare networks constructed using different algorithms from 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We find that each algorithm produced networks with substantially different topological properties, as reflected by numbers of edges, network density, assortativity, clustering coefficients and other structural measures. Provider networks adhered to a power law, while organization networks were best fit by a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and network density, and greatly altered measures of vertex prominence such as the betweenness centrality. Data analysis identified patterns in the distance patients travel between network providers, and a striking set of teaming relationships between providers in the Northeast United States and
Parallel optimization algorithms and their implementation in VLSI design
Lee, G.; Feeley, J. J.
1991-01-01
Two new parallel optimization algorithms based on the simplex method are described. They may be executed by a SIMD parallel processor architecture and be implemented in VLSI design. Several VLSI design implementations are introduced. An application example is reported to demonstrate that the algorithms are effective.
GPS-free localization algorithm for wireless sensor networks.
Wang, Lei; Xu, Qingzheng
2010-01-01
Localization is one of the most fundamental problems in wireless sensor networks, since the locations of the sensor nodes are critical to both network operations and most application level tasks. A GPS-free localization scheme for wireless sensor networks is presented in this paper. First, we develop a standardized clustering-based approach for the local coordinate system formation wherein a multiplication factor is introduced to regulate the number of master and slave nodes and the degree of connectivity among master nodes. Second, using homogeneous coordinates, we derive a transformation matrix between two Cartesian coordinate systems to efficiently merge them into a global coordinate system and effectively overcome the flip ambiguity problem. The algorithm operates asynchronously without a centralized controller; and does not require that the location of the sensors be known a priori. A set of parameter-setting guidelines for the proposed algorithm is derived based on a probability model and the energy requirements are also investigated. A simulation analysis on a specific numerical example is conducted to validate the mathematical analytical results. We also compare the performance of the proposed algorithm under a variety multiplication factor, node density and node communication radius scenario. Experiments show that our algorithm outperforms existing mechanisms in terms of accuracy and convergence time.
Distributed interference alignment iterative algorithms in symmetric wireless network
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YANG Jingwen
2015-02-01
Full Text Available Interference alignment is a novel interference alignment way,which is widely noted all of the world.Interference alignment overlaps interference in the same signal space at receiving terminal by precoding so as to thoroughly eliminate the influence of interference impacted on expected signals,thus making the desire user achieve the maximum degree of freedom.In this paper we research three typical algorithms for realizing interference alignment,including minimizing the leakage interference,maximizing Signal to Interference plus Noise Ratio (SINR and minimizing mean square error(MSE.All of these algorithms utilize the reciprocity of wireless network,and iterate the precoders between original network and the reverse network so as to achieve interference alignment.We use the uplink transmit rate to analyze the performance of these three algorithms.Numerical simulation results show the advantages of these algorithms.which is the foundation for the further study in the future.The feasibility and future of interference alignment are also discussed at last.
Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network.
Lin, Kai; Wang, Di; Hu, Long
2016-07-01
With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC). The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S) evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods.
Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network
Directory of Open Access Journals (Sweden)
Kai Lin
2016-07-01
Full Text Available With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC. The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods.
Qiao, Jun-Fei; Han, Hong-Gui
2010-02-01
This paper presents a repair algorithm for the design of a Radial Basis Function (RBF) neural network. The proposed repair RBF (RRBF) algorithm starts from a single prototype randomly initialized in the feature space. The algorithm has two main phases: an architecture learning phase and a parameter adjustment phase. The architecture learning phase uses a repair strategy based on a sensitivity analysis (SA) of the network's output to judge when and where hidden nodes should be added to the network. New nodes are added to repair the architecture when the prototype does not meet the requirements. The parameter adjustment phase uses an adjustment strategy where the capabilities of the network are improved by modifying all the weights. The algorithm is applied to two application areas: approximating a non-linear function, and modeling the key parameter, chemical oxygen demand (COD) used in the waste water treatment process. The results of simulation show that the algorithm provides an efficient solution to both problems.
Advances in metaheuristic algorithms for optimal design of structures
Kaveh, A
2017-01-01
This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally ...
Advances in metaheuristic algorithms for optimal design of structures
Kaveh, A
2014-01-01
This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally ...
Algorithms for dairy barn design: maternity and milking areas.
Fernández, M E; Mariño, R A; Carreira, X C
2009-05-01
This paper presents a series of algorithms for dairy barn design. The aim of the study was to develop algorithms for the design of new barn areas that can added to the main barn areas (resting, feeding, and exercise) to complement barn layout. More specifically, we developed design and dimensioning schemes to incorporate the maternity area and the milking area (holding pen and milking parlor) into dairy barns. Each algorithm requires input design parameters related to the characteristics of the areas considered. The output of the algorithms provides layout solutions for the maternity and milking areas and the overall dimensions of both areas, which enables the user to assess the possibility of incorporating maternity and milking into a dairy barn composed of resting, exercise, and feeding areas. The algorithms presented in this paper allow for space optimization by using the values defined for the design parameters considered.
Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks.
Directory of Open Access Journals (Sweden)
Seungseob Lee
Full Text Available Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets, in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators' careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms
Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks.
Lee, Seungseob; Lee, SuKyoung; Kim, Kyungsoo; Kim, Yoon Hyuk
2015-01-01
Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets), in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators' careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE) cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs) to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system
Chemical optimization algorithm for fuzzy controller design
Astudillo, Leslie; Castillo, Oscar
2014-01-01
In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application
New Scheduling Algorithms for Agile All-Photonic Networks
Mehri, Mohammad Saleh; Ghaffarpour Rahbar, Akbar
2017-12-01
An optical overlaid star network is a class of agile all-photonic networks that consists of one or more core node(s) at the center of the star network and a number of edge nodes around the core node. In this architecture, a core node may use a scheduling algorithm for transmission of traffic through the network. A core node is responsible for scheduling optical packets that arrive from edge nodes and switching them toward their destinations. Nowadays, most edge nodes use virtual output queue (VOQ) architecture for buffering client packets to achieve high throughput. This paper presents two efficient scheduling algorithms called discretionary iterative matching (DIM) and adaptive DIM. These schedulers find maximum matching in a small number of iterations and provide high throughput and incur low delay. The number of arbiters in these schedulers and the number of messages exchanged between inputs and outputs of a core node are reduced. We show that DIM and adaptive DIM can provide better performance in comparison with iterative round-robin matching with SLIP (iSLIP). SLIP means the act of sliding for a short distance to select one of the requested connections based on the scheduling algorithm.
Quantifying the multi-scale performance of network inference algorithms.
Oates, Chris J; Amos, Richard; Spencer, Simon E F
2014-10-01
Graphical models are widely used to study complex multivariate biological systems. Network inference algorithms aim to reverse-engineer such models from noisy experimental data. It is common to assess such algorithms using techniques from classifier analysis. These metrics, based on ability to correctly infer individual edges, possess a number of appealing features including invariance to rank-preserving transformation. However, regulation in biological systems occurs on multiple scales and existing metrics do not take into account the correctness of higher-order network structure. In this paper novel performance scores are presented that share the appealing properties of existing scores, whilst capturing ability to uncover regulation on multiple scales. Theoretical results confirm that performance of a network inference algorithm depends crucially on the scale at which inferences are to be made; in particular strong local performance does not guarantee accurate reconstruction of higher-order topology. Applying these scores to a large corpus of data from the DREAM5 challenge, we undertake a data-driven assessment of estimator performance. We find that the "wisdom of crowds" network, that demonstrated superior local performance in the DREAM5 challenge, is also among the best performing methodologies for inference of regulation on multiple length scales.
James, Jason; Dagli, Cihan H.
1995-04-01
In this study an attempt is being made to encode the architecture of a neural network in a chromosome string for evolving robust, fast-learning, minimal neural network architectures through genetic algorithms. Various attributes affecting the learning of the network are represented as genes. The performance of the networks is used as the fitness value. Neural network architecture design concepts are initially demonstrated using a backpropagation architecture with the standard data set of Rosenberg and Sejnowski for text to speech conversion on Adaptive Solutions Inc.'s CNAPS Neuro-Computer. The architectures obtained are compared with the one reported in the literature for the standard data set used. The study concludes by providing some insights regarding the architecture encoding for other artificial neural network paradigms.
An Intuitive Dominant Test Algorithm of CP-nets Applied on Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Liu Zhaowei
2014-07-01
Full Text Available A wireless sensor network is of spatially distributed with autonomous sensors, just like a multi-Agent system with single Agent. Conditional Preference networks is a qualitative tool for representing ceteris paribus (all other things being equal preference statements, it has been a research hotspot in artificial intelligence recently. But the algorithm and complexity of strong dominant test with respect to binary-valued structure CP-nets have not been solved, and few researchers address the application to other domain. In this paper, strong dominant test and application of CP-nets are studied in detail. Firstly, by constructing induced graph of CP-nets and studying its properties, we make a conclusion that the problem of strong dominant test on binary-valued CP-nets is single source shortest path problem essentially, so strong dominant test problem can be solved by improved Dijkstra’s algorithm. Secondly, we apply the algorithm above mentioned to the completeness of wireless sensor network, and design a completeness judging algorithm based on strong dominant test. Thirdly, we apply the algorithm on wireless sensor network to solve routing problem. In the end, we point out some interesting work in the future.
A review on models and algorithms for motif discovery in protein-protein interaction networks.
Ciriello, Giovanni; Guerra, Concettina
2008-03-01
Several algorithms have been recently designed to identify motifs in biological networks, particularly in protein-protein interaction networks. Motifs correspond to repeated modules in the network that may be of biological interest. The approaches proposed in the literature often differ in the definition of a motif, the way the occurrences of a motif are counted and the way their statistical significance is assessed. This has strong implications on the computational complexity of the discovery process and on the type of results that can be expected. This review presents in a systematic way the different computational settings outlining their main features and limitations.
Algorithmic test design using classical item parameters
van der Linden, Willem J.; Adema, Jos J.
Two optimalization models for the construction of tests with a maximal value of coefficient alpha are given. Both models have a linear form and can be solved by using a branch-and-bound algorithm. The first model assumes an item bank calibrated under the Rasch model and can be used, for instance,
Dias, Maluge Pubuduni Imali; Wong, Elaine
2013-04-22
In this work, we present a comparative study of two just-in-time (JIT) dynamic bandwidth allocation algorithms (DBAs), designed to improve the energy-efficiency of the 10 Gbps Ethernet passive optical networks (10G-EPONs). The algorithms, termed just-in-time with varying polling cycle times (JIT) and just-in-time with fixed polling cycle times (J-FIT), are designed to achieve energy-savings when the idle time of an optical network unit (ONU) is less than the sleep-to-active transition time. This is made possible by a vertical-cavity surface-emitting laser (VCSEL) ONU that can transit into sleep or doze modes during its idle time. We evaluate the performance of the algorithms in terms of polling cycle time, power consumption, percentage of energy-savings, and average delay. The energy-efficiency of a VCSEL ONU that can transition into sleep or doze mode is compared to an always-ON distributed feedback (DFB) laser ONU. Simulation results indicate that both JIT and J-FIT DBA algorithms result in improved energy-efficiency whilst J-FIT performs better in terms of energy-savings at low network loads. The J-FIT DBA however, results in increased average delay in comparison to the JIT DBA. Nonetheless, this increase in average delay is within the acceptable range to support the quality of service (QoS) requirements of the next-generation access networks.
Directory of Open Access Journals (Sweden)
Mohammad Taghi Ameli
2012-01-01
Full Text Available Transmission Network Expansion Planning (TNEP is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI tools such as Genetic Algorithm (GA, Simulated Annealing (SA, Tabu Search (TS and Artificial Neural Networks (ANNs are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs and Harmony Search Algorithm (HSA was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.
VSMURF: A Novel Sliding Window Cleaning Algorithm for RFID Networks
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He Xu
2017-01-01
Full Text Available Radio Frequency Identification (RFID is one of the key technologies of the Internet of Things (IoT and is used in many areas, such as mobile payments, public transportation, smart lock, and environment protection. However, the performance of RFID equipment can be easily affected by the surrounding environment, such as electronic productions and metal appliances. These can impose an impact on the RF signal, which makes the collection of RFID data unreliable. Usually, the unreliability of RFID source data includes three aspects: false negatives, false positives, and dirty data. False negatives are the key problem, as the probability of false positives and dirty data occurrence is relatively small. This paper proposes a novel sliding window cleaning algorithm called VSMURF, which is based on the traditional SMURF algorithm which combines the dynamic change of tags and the value analysis of confidence. Experimental results show that VSMURF algorithm performs better in most conditions and when the tag’s speed is low or high. In particular, if the velocity parameter is set to 2 m/epoch, our proposed VSMURF algorithm performs better than SMURF. The results also show that VSMURF algorithm has better performance than other algorithms in solving the problem of false negatives for RFID networks.
Human matching behavior in social networks: an algorithmic perspective.
Coviello, Lorenzo; Franceschetti, Massimo; McCubbins, Mathew D; Paturi, Ramamohan; Vattani, Andrea
2012-01-01
We argue that algorithmic modeling is a powerful approach to understanding the collective dynamics of human behavior. We consider the task of pairing up individuals connected over a network, according to the following model: each individual is able to propose to match with and accept a proposal from a neighbor in the network; if a matched individual proposes to another neighbor or accepts another proposal, the current match will be broken; individuals can only observe whether their neighbors are currently matched but have no knowledge of the network topology or the status of other individuals; and all individuals have the common goal of maximizing the total number of matches. By examining the experimental data, we identify a behavioral principle called prudence, develop an algorithmic model, analyze its properties mathematically and by simulations, and validate the model with human subject experiments for various network sizes and topologies. Our results include i) a 1/2-approximate maximum matching is obtained in logarithmic time in the network size for bounded degree networks; ii) for any constant ε > 0, a (1 - ε)-approximate maximum matching is obtained in polynomial time, while obtaining a maximum matching can require an exponential time; and iii) convergence to a maximum matching is slower on preferential attachment networks than on small-world networks. These results allow us to predict that while humans can find a "good quality" matching quickly, they may be unable to find a maximum matching in feasible time. We show that the human subjects largely abide by prudence, and their collective behavior is closely tracked by the above predictions.
Aghdam, Rosa; Ganjali, Mojtaba; Zhang, Xiujun; Eslahchi, Changiz
2015-03-01
Inferring Gene Regulatory Networks (GRNs) from gene expression data is a major challenge in systems biology. The Path Consistency (PC) algorithm is one of the popular methods in this field. However, as an order dependent algorithm, PC algorithm is not robust because it achieves different network topologies if gene orders are permuted. In addition, the performance of this algorithm depends on the threshold value used for independence tests. Consequently, selecting suitable sequential ordering of nodes and an appropriate threshold value for the inputs of PC algorithm are challenges to infer a good GRN. In this work, we propose a heuristic algorithm, namely SORDER, to find a suitable sequential ordering of nodes. Based on the SORDER algorithm and a suitable interval threshold for Conditional Mutual Information (CMI) tests, a network inference method, namely the Consensus Network (CN), has been developed. In the proposed method, for each edge of the complete graph, a weighted value is defined. This value is considered as the reliability value of dependency between two nodes. The final inferred network, obtained using the CN algorithm, contains edges with a reliability value of dependency of more than a defined threshold. The effectiveness of this method is benchmarked through several networks from the DREAM challenge and the widely used SOS DNA repair network in Escherichia coli. The results indicate that the CN algorithm is suitable for learning GRNs and it considerably improves the precision of network inference. The source of data sets and codes are available at .
Bioinspired evolutionary algorithm based for improving network coverage in wireless sensor networks.
Abbasi, Mohammadjavad; Bin Abd Latiff, Muhammad Shafie; Chizari, Hassan
2014-01-01
Wireless sensor networks (WSNs) include sensor nodes in which each node is able to monitor the physical area and send collected information to the base station for further analysis. The important key of WSNs is detection and coverage of target area which is provided by random deployment. This paper reviews and addresses various area detection and coverage problems in sensor network. This paper organizes many scenarios for applying sensor node movement for improving network coverage based on bioinspired evolutionary algorithm and explains the concern and objective of controlling sensor node coverage. We discuss area coverage and target detection model by evolutionary algorithm.
Research on Joint Handoff Algorithm in Vehicles Networks
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Yuming Bi
2016-01-01
Full Text Available With the communication services evolution from the fourth generation (4G to the fifth generation (5G, we are going to face diverse challenges from the new network systems. On the one hand, seamless handoff is expected to integrate universal access among various network mechanisms. On the other hand, a variety of 5G technologies will complement each other to provide ubiquitous high speed wireless connectivity. Because the current wireless network cannot support the handoff among Wireless Access for Vehicular Environment (WAVE, WiMAX, and LTE flexibly, the paper provides an advanced handoff algorithm to solve this problem. Firstly, the received signal strength is classified, and the vehicle speed and data rate under different channel conditions are optimized. Then, the optimal network is selected for handoff. Simulation results show that the proposed algorithm can well adapt to high speed environment, guarantee flexible and reasonable vehicles access to a variety of networks, and prevent ping-pong handoff and link access failure effectively.
Designing a Fuzzy Strategic Integrated Multiechelon Agile Supply Chain Network
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Morteza Abbasi
2013-01-01
Full Text Available This paper integrates production, distribution and logistics activities at the strategic decision making level, where the objective is to design a multiechelon supply chain network considering agility as a key design criterion. A network with five echelons of supply chains including suppliers, plants, distribution centers, cross-docks, and customer zones is addressed in this paper. The problem has been mathematically formulated as a biobjective optimization model that aims to minimize the cost (fixed and variable and maximize the plant flexibility and volume flexibility. A novel multiobjective parallel simulating annealing algorithm (MOPSA is proposed to obtain the Pareto-optimal solutions of the problem. The performance of the proposed solution algorithm is compared with two well-known metaheuristics, namely, nondominated sorting genetic algorithm (NSGA-II and Pareto archive evolution strategy (PAES. Computational results show that MOPSA outperforms the other metaheuristics.
Tang, Tingfang; Guo, Peng; Liu, Xuefeng
2016-10-01
In-network processing is an efficient way to reduce the transmission cost in wireless sensor networks (WSNs). The in-network processing of many domain-specific computation tasks in WSNs usually requires to losslessly distribute the computation of the tasks into the sensor nodes, which is however usually not easy. In this paper we are concerned with such kind of tasks whose computation can only be partitioned into recursive computation mode. To distribute the recursive computations into WSNs, it is required to design an appropriate single in-network processing path, along which the intermediate data is forwarded and updated in the WSNs. We address the recursive computation with constant size of computation result, e.g., distributed least square estimation (D-LSE). Finding the optimal in-network processing path to minimize the total transmission cost in WSNs, is a new problem and seldom studied before. To solve it, we propose a novel routing algorithm called as S-TSP, and compare it with some other greedy algorithms. Extensive simulations are conducted, and the results show the good performance of the proposed S-TSP algorithm.
Distributed Multitarget Probabilistic Coverage Control Algorithm for Wireless Sensor Networks
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Ying Tian
2014-01-01
Full Text Available This paper is concerned with the problem of multitarget coverage based on probabilistic detection model. Coverage configuration is an effective method to alleviate the energy-limitation problem of sensors. Firstly, considering the attenuation of node’s sensing ability, the target probabilistic coverage problem is defined and formalized, which is based on Neyman-Peason probabilistic detection model. Secondly, in order to turn off redundant sensors, a simplified judging rule is derived, which makes the probabilistic coverage judgment execute on each node locally. Thirdly, a distributed node schedule scheme is proposed for implementing the distributed algorithm. Simulation results show that this algorithm is robust to the change of network size, and when compared with the physical coverage algorithm, it can effectively minimize the number of active sensors, which guarantees all the targets γ-covered.
Energy Efficient Distributed Fault Identification Algorithm in Wireless Sensor Networks
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Meenakshi Panda
2014-01-01
Full Text Available A distributed fault identification algorithm is proposed here to find both hard and soft faulty sensor nodes present in wireless sensor networks. The algorithm is distributed, self-detectable, and can detect the most common byzantine faults such as stuck at zero, stuck at one, and random data. In the proposed approach, each sensor node gathered the observed data from the neighbors and computed the mean to check whether faulty sensor node is present or not. If a node found the presence of faulty sensor node, then compares observed data with the data of the neighbors and predict probable fault status. The final fault status is determined by diffusing the fault information from the neighbors. The accuracy and completeness of the algorithm are verified with the help of statistical model of the sensors data. The performance is evaluated in terms of detection accuracy, false alarm rate, detection latency and message complexity.
Functional clustering algorithm for the analysis of dynamic network data
Feldt, S.; Waddell, J.; Hetrick, V. L.; Berke, J. D.; Żochowski, M.
2009-05-01
We formulate a technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated neural spike train data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. Using the simulated data, we show that our algorithm performs better than existing methods. In the experimental data, we observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.
NML Computation Algorithms for Tree-Structured Multinomial Bayesian Networks
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Kontkanen Petri
2007-01-01
Full Text Available Typical problems in bioinformatics involve large discrete datasets. Therefore, in order to apply statistical methods in such domains, it is important to develop efficient algorithms suitable for discrete data. The minimum description length (MDL principle is a theoretically well-founded, general framework for performing statistical inference. The mathematical formalization of MDL is based on the normalized maximum likelihood (NML distribution, which has several desirable theoretical properties. In the case of discrete data, straightforward computation of the NML distribution requires exponential time with respect to the sample size, since the definition involves a sum over all the possible data samples of a fixed size. In this paper, we first review some existing algorithms for efficient NML computation in the case of multinomial and naive Bayes model families. Then we proceed by extending these algorithms to more complex, tree-structured Bayesian networks.
Improved Differential Evolution Algorithm for Wireless Sensor Network Coverage Optimization
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Xing Xu
2014-04-01
Full Text Available In order to serve for the ecological monitoring efficiency of Poyang Lake, an improved hybrid algorithm, mixed with differential evolution and particle swarm optimization, is proposed and applied to optimize the coverage problem of wireless sensor network. And then, the affect of the population size and the number of iterations on the coverage performance are both discussed and analyzed. The four kinds of statistical results about the coverage rate are obtained through lots of simulation experiments.
MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm
Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.
2014-01-01
The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339
A density based link clustering algorithm for overlapping community detection in networks
Zhou, Xu; Liu, Yanheng; Wang, Jian; Li, Chun
2017-11-01
Overlapping is an interesting and common characteristic of community structure in networks. Link clustering method for overlapping community detection has attracted a lot of attention in the area of social networks applications. However, it may make the clustering result with excessive overlap and cluster bridge edge and border edge mistakenly to adjacent communities. To solve this problem, a density based link clustering algorithm is proposed to improve the accuracy of detecting overlapping communities in networks in this study. It creates a number of clusters containing core edges only based on concept named as core density reachable during the expansion. Then an updating strategy for unclassified edges is designed to assign them to the closest cluster. In addition, a similarity measure for computing the similarity between two edges is presented. Experiments on synthetic networks and real networks have been conducted. The experimental results demonstrate that our method performs better than other algorithms on detecting community structure and overlapping nodes, it can get nearly 15% higher than the NMI value of other algorithms on some synthetic networks.
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Haisheng Song
2013-01-01
Full Text Available The back propagation neural network (BPNN algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.
Performance analysis of dynamic load balancing algorithm for multiprocessor interconnection network
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M.U. Bokhari
2016-09-01
Full Text Available Multiprocessor interconnection network have become powerful parallel computing system for real-time applications. Nowadays the many researchers posses studies on the dynamic load balancing in multiprocessor system. Load balancing is the method of dividing the total load among the processors of the distributed system to progress task's response time as well as resource utilization whereas ignoring a condition where few processors are overloaded or underloaded or moderately loaded. However, in dynamic load balancing algorithm presumes no priori information about behaviour of tasks or the global state of the system. There are numerous issues while designing an efficient dynamic load balancing algorithm that involves utilization of system, amount of information transferred among processors, selection of tasks for migration, load evaluation, comparison of load levels and many more. This paper enlightens the performance analysis on dynamic load balancing strategy (DLBS algorithm, used for hypercube network in multiprocessor system.
Photovoltaic Cells Mppt Algorithm and Design of Controller Monitoring System
Meng, X. Z.; Feng, H. B.
2017-10-01
This paper combined the advantages of each maximum power point tracking (MPPT) algorithm, put forward a kind of algorithm with higher speed and higher precision, based on this algorithm designed a maximum power point tracking controller with ARM. The controller, communication technology and PC software formed a control system. Results of the simulation and experiment showed that the process of maximum power tracking was effective, and the system was stable.
A Parallel Genetic Algorithm for Automated Electronic Circuit Design
Long, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris
2000-01-01
Parallelized versions of genetic algorithms (GAs) are popular primarily for three reasons: the GA is an inherently parallel algorithm, typical GA applications are very compute intensive, and powerful computing platforms, especially Beowulf-style computing clusters, are becoming more affordable and easier to implement. In addition, the low communication bandwidth required allows the use of inexpensive networking hardware such as standard office ethernet. In this paper we describe a parallel GA and its use in automated high-level circuit design. Genetic algorithms are a type of trial-and-error search technique that are guided by principles of Darwinian evolution. Just as the genetic material of two living organisms can intermix to produce offspring that are better adapted to their environment, GAs expose genetic material, frequently strings of 1s and Os, to the forces of artificial evolution: selection, mutation, recombination, etc. GAs start with a pool of randomly-generated candidate solutions which are then tested and scored with respect to their utility. Solutions are then bred by probabilistically selecting high quality parents and recombining their genetic representations to produce offspring solutions. Offspring are typically subjected to a small amount of random mutation. After a pool of offspring is produced, this process iterates until a satisfactory solution is found or an iteration limit is reached. Genetic algorithms have been applied to a wide variety of problems in many fields, including chemistry, biology, and many engineering disciplines. There are many styles of parallelism used in implementing parallel GAs. One such method is called the master-slave or processor farm approach. In this technique, slave nodes are used solely to compute fitness evaluations (the most time consuming part). The master processor collects fitness scores from the nodes and performs the genetic operators (selection, reproduction, variation, etc.). Because of dependency
Optimal design of an IP/MPLS over DWDM network
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Eduardo Canale
2014-04-01
Full Text Available Different approaches for deploying resilient optical networks of low cost constitute a traditional group of NP-Hard problems that have been widely studied. Most of them are based on the construction of low cost networks that fulfill connectivity constraints. However, recent trends to virtualize optical networks over the legacy fiber infrastructure, modified the nature of network design problems and turned inappropriate many of these models and algorithms. In this paper we study a design problem arising from the deployment of an IP/MPLS network over an existing DWDM infrastructure. Besides cost and resiliency, this problem integrates traffic and capacity constraints. We present: an integer programming formulation for the problem, theoretical results, and describe how several metaheuristics were applied in order to find good quality solutions, for a real application case of a telecommunications company.
Reverse Logistics Network Structures and Design
M. Fleischmann (Moritz)
2001-01-01
textabstractLogistics network design is commonly recognized as a strategic supply chain issue of prime importance. The location of production facilities, storage concepts, and transportation strategies are major determinants of supply chain performance. This chapter considers logistics network
MODA: an efficient algorithm for network motif discovery in biological networks.
Omidi, Saeed; Schreiber, Falk; Masoudi-Nejad, Ali
2009-10-01
In recent years, interest has been growing in the study of complex networks. Since Erdös and Rényi (1960) proposed their random graph model about 50 years ago, many researchers have investigated and shaped this field. Many indicators have been proposed to assess the global features of networks. Recently, an active research area has developed in studying local features named motifs as the building blocks of networks. Unfortunately, network motif discovery is a computationally hard problem and finding rather large motifs (larger than 8 nodes) by means of current algorithms is impractical as it demands too much computational effort. In this paper, we present a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently. We have tested our algorithm and found it able to identify larger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms. While most of the algorithms rely on induced subgraphs as motifs of the networks, MODA is able to extract both induced and non-induced subgraphs simultaneously. The MODA source code is freely available at: http://LBB.ut.ac.ir/Download/LBBsoft/MODA/
Floares, Alexandru George
2008-01-01
Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks.
Algorithmic test design using classical item parameters
van der Linden, Willem J.; Adema, Jos J.
1988-01-01
Two optimalization models for the construction of tests with a maximal value of coefficient alpha are given. Both models have a linear form and can be solved by using a branch-and-bound algorithm. The first model assumes an item bank calibrated under the Rasch model and can be used, for instance, when classical test theory has to serve as an interface between the item bank system and a user not familiar with modern test theory. Maximization of alpha was obtained by inserting a special constra...
Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks
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Rajeev Kumar
2016-01-01
Full Text Available Currently, wireless sensor networks (WSNs are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i selection of optimal number of subregions and further subregion parts, (ii cluster head selection using ABC algorithm, and (iii efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS. The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively.
Analysis of convergence performance of neural networks ranking algorithm.
Zhang, Yongquan; Cao, Feilong
2012-10-01
The ranking problem is to learn a real-valued function which gives rise to a ranking over an instance space, which has gained much attention in machine learning in recent years. This article gives analysis of the convergence performance of neural networks ranking algorithm by means of the given samples and approximation property of neural networks. The upper bounds of convergence rate provided by our results can be considerably tight and independent of the dimension of input space when the target function satisfies some smooth condition. The obtained results imply that neural networks are able to adapt to ranking function in the instance space. Hence the obtained results are able to circumvent the curse of dimensionality on some smooth condition. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.
An Effective Recommender Algorithm for Cold-Start Problem in Academic Social Networks
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Vala Ali Rohani
2014-01-01
Full Text Available Abundance of information in recent years has become a serious challenge for web users. Recommender systems (RSs have been often utilized to alleviate this issue. RSs prune large information spaces to recommend the most relevant items to users by considering their preferences. Nonetheless, in situations where users or items have few opinions, the recommendations cannot be made properly. This notable shortcoming in practical RSs is called cold-start problem. In the present study, we propose a novel approach to address this problem by incorporating social networking features. Coined as enhanced content-based algorithm using social networking (ECSN, the proposed algorithm considers the submitted ratings of faculty mates and friends besides user’s own preferences. The effectiveness of ECSN algorithm was evaluated by implementing it in MyExpert, a newly designed academic social network (ASN for academics in Malaysia. Real feedbacks from live interactions of MyExpert users with the recommended items are recorded for 12 consecutive weeks in which four different algorithms, namely, random, collaborative, content-based, and ECSN were applied every three weeks. The empirical results show significant performance of ECSN in mitigating the cold-start problem besides improving the prediction accuracy of recommendations when compared with other studied recommender algorithms.
Optimised Design and Analysis of All-Optical Networks
DEFF Research Database (Denmark)
Glenstrup, Arne John
2002-01-01
-connects should be drastically cheaper if they are to compete on the cost with ring networks. An MPXS (multi-protocol wavelength switching) simulator is constructed, focusing especially on the timing in the setup phase to asess the blocking probability effect of node and link delays as well as wavelength...... is developed, based on shortest-path algorithms and a comparatively new metaheuristic called simulated allocation. It is able to handle design of all-optical mesh networks with optical cross-connects, considers duct as well as fibre and node costs, and can also design protected networks. The method is assessed...... level is presented. The method uses an unorthodox routing strategy focusing on common subpaths rather than individual connections, and strives to minimise switch port count as well as fibre usage. It is shown to produce cheaper network designs than previous methods when fibre costs are comparatively...
Designers' Cognitive Thinking Based on Evolutionary Algorithms
Zhang Shutao; Jianning Su; Chibing Hu; Peng Wang
2013-01-01
The research on cognitive thinking is important to construct the efficient intelligent design systems. But it is difficult to describe the model of cognitive thinking with reasonable mathematical theory. Based on the analysis of design strategy and innovative thinking, we investigated the design cognitive thinking model that included the external guide thinking of "width priority - depth priority" and the internal dominated thinking of "divergent thinking - convergent thinking", built a reaso...
Vyas, Bhargav Y; Das, Biswarup; Maheshwari, Rudra Prakash
2016-08-01
This paper presents the Chebyshev neural network (ChNN) as an improved artificial intelligence technique for power system protection studies and examines the performances of two ChNN learning algorithms for fault classification of series compensated transmission line. The training algorithms are least-square Levenberg-Marquardt (LSLM) and recursive least-square algorithm with forgetting factor (RLSFF). The performances of these algorithms are assessed based on their generalization capability in relating the fault current parameters with an event of fault in the transmission line. The proposed algorithm is fast in response as it utilizes postfault samples of three phase currents measured at the relaying end corresponding to half-cycle duration only. After being trained with only a small part of the generated fault data, the algorithms have been tested over a large number of fault cases with wide variation of system and fault parameters. Based on the studies carried out in this paper, it has been found that although the RLSFF algorithm is faster for training the ChNN in the fault classification application for series compensated transmission lines, the LSLM algorithm has the best accuracy in testing. The results prove that the proposed ChNN-based method is accurate, fast, easy to design, and immune to the level of compensations. Thus, it is suitable for digital relaying applications.
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D.W. Kim
2013-08-01
Full Text Available Estimation of distribution algorithms (EDAs constitute a new branch of evolutionary optimization algorithms that were developed as a natural alternative to genetic algorithms (GAs. Several studies have demonstrated that the heuristic scheme of EDAs is effective and efficient for many optimization problems. Recently, it has been reported that the incorporation of mutation into EDAs increases the diversity of genetic information in the population, thereby avoiding premature convergence into a suboptimal solution. In this study, we propose a new mutation operator, a transpose mutation, designed for Bayesian structure learning. It enhances the diversity of the offspring and it increases the possibility of inferring the correct arc direction by considering the arc directions in candidate solutions as bi-directional, using the matrix transpose operator. As compared to the conventional EDAs, the transpose mutation-adopted EDAs are superior and effective algorithms for learning Bayesian networks.
Empirically determining the sample size for large-scale gene network inference algorithms.
Altay, G
2012-04-01
The performance of genome-wide gene regulatory network inference algorithms depends on the sample size. It is generally considered that the larger the sample size, the better the gene network inference performance. Nevertheless, there is not adequate information on determining the sample size for optimal performance. In this study, the author systematically demonstrates the effect of sample size on information-theory-based gene network inference algorithms with an ensemble approach. The empirical results showed that the inference performances of the considered algorithms tend to converge after a particular sample size region. As a specific example, the sample size region around ≃64 is sufficient to obtain the most of the inference performance with respect to precision using the representative algorithm C3NET on the synthetic steady-state data sets of Escherichia coli and also time-series data set of a homo sapiens subnetworks. The author verified the convergence result on a large, real data set of E. coli as well. The results give evidence to biologists to better design experiments to infer gene networks. Further, the effect of cutoff on inference performances over various sample sizes is considered. [Includes supplementary material].
Xu, Lei; Jeavons, Peter
2015-11-01
Leader election in anonymous rings and complete networks is a very practical problem in distributed computing. Previous algorithms for this problem are generally designed for a classical message passing model where complex messages are exchanged. However, the need to send and receive complex messages makes such algorithms less practical for some real applications. We present some simple synchronous algorithms for distributed leader election in anonymous rings and complete networks that are inspired by the development of the neural system of the fruit fly. Our leader election algorithms all assume that only one-bit messages are broadcast by nodes in the network and processors are only able to distinguish between silence and the arrival of one or more messages. These restrictions allow implementations to use a simpler message-passing architecture. Even with these harsh restrictions our algorithms are shown to achieve good time and message complexity both analytically and experimentally.
Antenna Design by Means of the Fruit Fly Optimization Algorithm
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Lucas Polo-López
2018-01-01
Full Text Available In this work a heuristic optimization algorithm known as the Fruit fly Optimization Algorithm is applied to antenna design problems. The original formulation of the algorithm is presented and it is adapted to array factor and horn antenna optimization problems. Specifically, it is applied to the array factor synthesis of uniformly-fed, non-equispaced arrays and to the profile optimization of multimode horn antennas. Several numerical examples are presented and the obtained results are compared with those provided by a deterministic optimization based on a simplex method and another well-known heuristic approach, the Genetic Algorithm.
An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.
Yoon, Yourim; Kim, Yong-Hyuk
2013-10-01
Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.
Robust reactor power control system design by genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Lee, Yoon Joon; Cho, Kyung Ho; Kim, Sin [Cheju National University, Cheju (Korea, Republic of)
1997-12-31
The H{sub {infinity}} robust controller for the reactor power control system is designed by use of the mixed weight sensitivity. The system is configured into the typical two-port model with which the weight functions are augmented. Since the solution depends on the weighting functions and the problem is of nonconvex, the genetic algorithm is used to determine the weighting functions. The cost function applied in the genetic algorithm permits the direct control of the power tracking performances. In addition, the actual operating constraints such as rod velocity and acceleration can be treated as design parameters. Compared with the conventional approach, the controller designed by the genetic algorithm results in the better performances with the realistic constraints. Also, it is found that the genetic algorithm could be used as an effective tool in the robust design. 4 refs., 6 figs. (Author)
Research on wind field algorithm of wind lidar based on BP neural network and grey prediction
Chen, Yong; Chen, Chun-Li; Luo, Xiong; Zhang, Yan; Yang, Ze-hou; Zhou, Jie; Shi, Xiao-ding; Wang, Lei
2018-01-01
This paper uses the BP neural network and grey algorithm to forecast and study radar wind field. In order to reduce the residual error in the wind field prediction which uses BP neural network and grey algorithm, calculating the minimum value of residual error function, adopting the residuals of the gray algorithm trained by BP neural network, using the trained network model to forecast the residual sequence, using the predicted residual error sequence to modify the forecast sequence of the grey algorithm. The test data show that using the grey algorithm modified by BP neural network can effectively reduce the residual value and improve the prediction precision.
Designing synthetic networks in silico
Smith, Robert W.; Sluijs, van Bob; Fleck, Christian
2017-01-01
Background: Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused
Directory of Open Access Journals (Sweden)
M. Kalpana
2014-01-01
Full Text Available This research work proposes a mathematical model for the lifetime of wireless sensor networks (WSN. It also proposes an energy efficient routing algorithm for WSN called hierarchical energy tree based routing algorithm (HETRA based on hierarchical energy tree constructed using the available energy in each node. The energy efficiency is further augmented by reducing the packet drops using exponential congestion control algorithm (TCP/EXP. The algorithms are evaluated in WSNs interconnected to fixed network with seven distribution patterns, simulated in ns2 and compared with the existing algorithms based on the parameters such as number of data packets, throughput, network lifetime, and data packets average network lifetime product. Evaluation and simulation results show that the combination of HETRA and TCP/EXP maximizes longer network lifetime in all the patterns. The lifetime of the network with HETRA algorithm has increased approximately 3.2 times that of the network implemented with AODV.
Kalpana, M; Dhanalakshmi, R; Parthiban, P
2014-01-01
This research work proposes a mathematical model for the lifetime of wireless sensor networks (WSN). It also proposes an energy efficient routing algorithm for WSN called hierarchical energy tree based routing algorithm (HETRA) based on hierarchical energy tree constructed using the available energy in each node. The energy efficiency is further augmented by reducing the packet drops using exponential congestion control algorithm (TCP/EXP). The algorithms are evaluated in WSNs interconnected to fixed network with seven distribution patterns, simulated in ns2 and compared with the existing algorithms based on the parameters such as number of data packets, throughput, network lifetime, and data packets average network lifetime product. Evaluation and simulation results show that the combination of HETRA and TCP/EXP maximizes longer network lifetime in all the patterns. The lifetime of the network with HETRA algorithm has increased approximately 3.2 times that of the network implemented with AODV.
A multi-agent genetic algorithm for community detection in complex networks
Li, Zhangtao; Liu, Jing
2016-05-01
Complex networks are popularly used to represent a lot of practical systems in the domains of biology and sociology, and the structure of community is one of the most important network attributes which has received an enormous amount of attention. Community detection is the process of discovering the community structure hidden in complex networks, and modularity Q is one of the best known quality functions measuring the quality of communities of networks. In this paper, a multi-agent genetic algorithm, named as MAGA-Net, is proposed to optimize modularity value for the community detection. An agent, coded by a division of a network, represents a candidate solution. All agents live in a lattice-like environment, with each agent fixed on a lattice point. A series of operators are designed, namely split and merging based neighborhood competition operator, hybrid neighborhood crossover, adaptive mutation and self-learning operator, to increase modularity value. In the experiments, the performance of MAGA-Net is validated on both well-known real-world benchmark networks and large-scale synthetic LFR networks with 5000 nodes. The systematic comparisons with GA-Net and Meme-Net show that MAGA-Net outperforms these two algorithms, and can detect communities with high speed, accuracy and stability.
Reliable Multi-Fractal Characterization of Weighted Complex Networks: Algorithms and Implications.
Xue, Yuankun; Bogdan, Paul
2017-08-08
Through an elegant geometrical interpretation, the multi-fractal analysis quantifies the spatial and temporal irregularities of the structural and dynamical formation of complex networks. Despite its effectiveness in unweighted networks, the multi-fractal geometry of weighted complex networks, the role of interaction intensity, the influence of the embedding metric spaces and the design of reliable estimation algorithms remain open challenges. To address these challenges, we present a set of reliable multi-fractal estimation algorithms for quantifying the structural complexity and heterogeneity of weighted complex networks. Our methodology uncovers that (i) the weights of complex networks and their underlying metric spaces play a key role in dictating the existence of multi-fractal scaling and (ii) the multi-fractal scaling can be localized in both space and scales. In addition, this multi-fractal characterization framework enables the construction of a scaling-based similarity metric and the identification of community structure of human brain connectome. The detected communities are accurately aligned with the biological brain connectivity patterns. This characterization framework has no constraint on the target network and can thus be leveraged as a basis for both structural and dynamic analysis of networks in a wide spectrum of applications.
Starting design for use in variance exchange algorithms | Iwundu ...
African Journals Online (AJOL)
A new method of constructing the initial design for use in variance exchange algorithms is presented. The method chooses support points to go into the design as measures of distances of the support points from the centre of the geometric region and of permutation-invariant sets. The initial design is as close as possible to ...
Supporting Exploration of Design Alternatives using Multivariate Analysis Algorithms
Šileryte, R.; D'Aquilio, A.; Di Stefano, D.; Yang, D.; Turrin, M.; Attar, Ramtin; Chronis, Angelos; Hanna, Sean; Turrin, Michela
2016-01-01
Parametric modelling allows quick generation of a large number of design alternatives. Ultimately, it can be combined with optimization algorithms for obtaining optimal performance-driven design. However, setup of design space for optimization is a very complex task requiring designer’s a priori
A generic synthesis algorithm for well-defined parametric design
Schotborgh, W.O.; Kokkeler, Frans; Tragter, Hendrik; Bomhoff, M.J.; van Houten, Frederikus J.A.M.; van Houten, F.J.A.M.; Miedema, J.; Lutters, D.
2008-01-01
This paper aims to improve the way synthesis tools can be built by formalizing: 1) the design artefact, 2) related knowledge and 3) an algorithm to generate solutions. This paper focuses on well-defined parametric engineering design, ranging from machine elements to industrial products. A design
Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T
2015-02-01
Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks. Copyright © 2014 Elsevier Inc. All rights reserved.
An algorithm to design a multiple-channel communications system
Nguyen, Tien M.; Gevargiz, John M.; Hinedi, Sami
1991-01-01
The authors present a novel algorithm to design a phase-modulated residual carrier communications system for optimum performance. The algorithm has been developed as an aid in the design of two telemetry data channels operating simultaneously with a turn-around ranging signal. The algorithm provides the selection of an optimum subcarrier frequency and modulation indices for minimizing the mutual interferences among the channels to achieve desired link performance margins. The set of modulation indices provided by this algorithm will also achieve an optimum power division between the carrier, the ranging and the high data rate and low data rate channels. Finally, a software program using this algorithm has been developed for use with personal computers. This program provides the computation of the optimum subcarrier frequencies and modulation indices for a standard case that is recommended by the international Consultative Committee for Space Data Systems.
Comparison of evolutionary algorithms in gene regulatory network model inference.
LENUS (Irish Health Repository)
2010-01-01
ABSTRACT: BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.
Embedding Design in a Mental Health Network
Pierri, Paola; Warwick, Laura; Garber, Jake
2016-01-01
Service Design in Mind (SDiM) is a programme run by Mind, the national mental health charity. The programme aims to embed service design techniques and methods into a network of local voluntary organisations that deliver mental health services. This case study describes how the programme, based on the idea that everybody designs and everyone can be a designer, aimed to create a diffused design culture (Manzini, 2015) across the charity and its network. By capitalising on existing internal des...
An algorithm J-SC of detecting communities in complex networks
Hu, Fang; Wang, Mingzhu; Wang, Yanran; Hong, Zhehao; Zhu, Yanhui
2017-11-01
Currently, community detection in complex networks has become a hot-button topic. In this paper, based on the Spectral Clustering (SC) algorithm, we introduce the idea of Jacobi iteration, and then propose a novel algorithm J-SC for community detection in complex networks. Furthermore, the accuracy and efficiency of this algorithm are tested by some representative real-world networks and several computer-generated networks. The experimental results indicate that the J-SC algorithm can accurately and effectively detect the community structure in these networks. Meanwhile, compared with the state-of-the-art community detecting algorithms SC, SOM, K-means, Walktrap and Fastgreedy, the J-SC algorithm has better performance, reflecting that this new algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SOM and Walktrap algorithms.
A new dynamical layout algorithm for complex biochemical reaction networks.
Wegner, Katja; Kummer, Ursula
2005-08-26
To study complex biochemical reaction networks in living cells researchers more and more rely on databases and computational methods. In order to facilitate computational approaches, visualisation techniques are highly important. Biochemical reaction networks, e.g. metabolic pathways are often depicted as graphs and these graphs should be drawn dynamically to provide flexibility in the context of different data. Conventional layout algorithms are not sufficient for every kind of pathway in biochemical research. This is mainly due to certain conventions to which biochemists/biologists are used to and which are not in accordance to conventional layout algorithms. A number of approaches has been developed to improve this situation. Some of these are used in the context of biochemical databases and make more or less use of the information in these databases to aid the layout process. However, visualisation becomes also more and more important in modelling and simulation tools which mostly do not offer additional connections to databases. Therefore, layout algorithms used in these tools have to work independently of any databases. In addition, all of the existing algorithms face some limitations with respect to the number of edge crossings when it comes to larger biochemical systems due to the interconnectivity of these. Last but not least, in some cases, biochemical conventions are not met properly. Out of these reasons we have developed a new algorithm which tackles these problems by reducing the number of edge crossings in complex systems, taking further biological conventions into account to identify and visualise cycles. Furthermore the algorithm is independent from database information in order to be easily adopted in any application. It can also be tested as part of the SimWiz package (free to download for academic users at 1). The new algorithm reduces the complexity of pathways, as well as edge crossings and edge length in the resulting graphical representation
Using Network Science to Support Design Research
DEFF Research Database (Denmark)
Parraguez Ruiz, Pedro; Maier, Anja
2016-01-01
A network-based perspective on designing permits research on the complexity of product, process, and people interactions. Strengthened by the latest advances in information technologies and accessibility of data, a network-based perspective and use of appropriate network analysis metrics, theories......, and tools allow us to explore new data-driven research approaches in design. These approaches allow us to move from counting to connecting, meaning to explicitly link disconnected pieces of data, information, and knowledge, and thus to answer far-reaching research questions with strong industrial...... and societal impact. This chapter contributes to the use of network science in empirical studies of design organisations. It focuses on introducing a network-based perspective on the design process and in particular on making use of network science to support design research and practice. The main contribution...
A Flexible Reservation Algorithm for Advance Network Provisioning
Energy Technology Data Exchange (ETDEWEB)
Balman, Mehmet; Chaniotakis, Evangelos; Shoshani, Arie; Sim, Alex
2010-04-12
Many scientific applications need support from a communication infrastructure that provides predictable performance, which requires effective algorithms for bandwidth reservations. Network reservation systems such as ESnet's OSCARS, establish guaranteed bandwidth of secure virtual circuits for a certain bandwidth and length of time. However, users currently cannot inquire about bandwidth availability, nor have alternative suggestions when reservation requests fail. In general, the number of reservation options is exponential with the number of nodes n, and current reservation commitments. We present a novel approach for path finding in time-dependent networks taking advantage of user-provided parameters of total volume and time constraints, which produces options for earliest completion and shortest duration. The theoretical complexity is only O(n2r2) in the worst-case, where r is the number of reservations in the desired time interval. We have implemented our algorithm and developed efficient methodologies for incorporation into network reservation frameworks. Performance measurements confirm the theoretical predictions.
Novel Spectrum Sensing Algorithms for OFDM Cognitive Radio Networks.
Shi, Zhenguo; Wu, Zhilu; Yin, Zhendong; Cheng, Qingqing
2015-06-15
Spectrum sensing technology plays an increasingly important role in cognitive radio networks. Consequently, several spectrum sensing algorithms have been proposed in the literature. In this paper, we present a new spectrum sensing algorithm "Differential Characteristics-Based OFDM (DC-OFDM)" for detecting OFDM signal on account of differential characteristics. We put the primary value on channel gain θ around zero to detect the presence of primary user. Furthermore, utilizing the same method of differential operation, we improve two traditional OFDM sensing algorithms (cyclic prefix and pilot tones detecting algorithms), and propose a "Differential Characteristics-Based Cyclic Prefix (DC-CP)" detector and a "Differential Characteristics-Based Pilot Tones (DC-PT)" detector, respectively. DC-CP detector is based on auto-correlation vector to sense the spectrum, while the DC-PT detector takes the frequency-domain cross-correlation of PT as the test statistic to detect the primary user. Moreover, the distributions of the test statistics of the three proposed methods have been derived. Simulation results illustrate that all of the three proposed methods can achieve good performance under low signal to noise ratio (SNR) with the presence of timing delay. Specifically, the DC-OFDM detector gets the best performance among the presented detectors. Moreover, both of the DC-CP and DC-PT detector achieve significant improvements compared with their corresponding original detectors.
Node Deployment Algorithm Based on Connected Tree for Underwater Sensor Networks
Directory of Open Access Journals (Sweden)
Peng Jiang
2015-07-01
Full Text Available Designing an efficient deployment method to guarantee optimal monitoring quality is one of the key topics in underwater sensor networks. At present, a realistic approach of deployment involves adjusting the depths of nodes in water. One of the typical algorithms used in such process is the self-deployment depth adjustment algorithm (SDDA. This algorithm mainly focuses on maximizing network coverage by constantly adjusting node depths to reduce coverage overlaps between two neighboring nodes, and thus, achieves good performance. However, the connectivity performance of SDDA is irresolute. In this paper, we propose a depth adjustment algorithm based on connected tree (CTDA. In CTDA, the sink node is used as the first root node to start building a connected tree. Finally, the network can be organized as a forest to maintain network connectivity. Coverage overlaps between the parent node and the child node are then reduced within each sub-tree to optimize coverage. The hierarchical strategy is used to adjust the distance between the parent node and the child node to reduce node movement. Furthermore, the silent mode is adopted to reduce communication cost. Simulations show that compared with SDDA, CTDA can achieve high connectivity with various communication ranges and different numbers of nodes. Moreover, it can realize coverage as high as that of SDDA with various sensing ranges and numbers of nodes but with less energy consumption. Simulations under sparse environments show that the connectivity and energy consumption performances of CTDA are considerably better than those of SDDA. Meanwhile, the connectivity and coverage performances of CTDA are close to those depth adjustment algorithms base on connected dominating set (CDA, which is an algorithm similar to CTDA. However, the energy consumption of CTDA is less than that of CDA, particularly in sparse underwater environments.
Transmission design in a multimode HFC network
Cambron, G. Keith
1996-11-01
Contemporary hybrid fiber coaxial (HFC) networks are capable of supporting a wide range of services including traditional analog video, telephony, digital video, and data services. Each service has unique performance or service requirements. This contribution examines transmission design for one such network. Pacific Bell's advanced communications network (ACN). The design methodology begins with a set of end to end service quality objectives. Network impairments, such as noise, distortion and delay, are allocated across the network elements using a set of standard network models. These models are a representative set of the actual field designs and bound the network operating parameters. Network components, headend equipment, and customer premises equipment are specified analytically or characterized empirically in relationship to the chosen impairment set. The component parameters are then included in analytical models to estimate overall network performance. In addition to the forward path transmission considerations examined by traditional coaxial network designers, other dimensions including power consumption, traffic demand, and message latency are taken into account. Analytical models are used to estimate the effects of multiple modulation schemes within the unified network. The variability introduced by on demand services such as telephony and interactive digital services changes the base computational domain from deterministic models to stochastic ones. These models are then used to set operating parameters at measurable points throughout the network for proof of performance prior to turn up, and for ongoing performance monitoring. For closure, empirical results are compared with model projections as a way of verifying and improving the predictive models.
Smart Business Networks Design and Business Genetics
L-F. Pau (Louis-François)
2006-01-01
textabstractWith the emergence of smart business networks, agile networks, etc. as important research areas in management, for all the attractiveness of these concepts, a major issue remains around their design and the selection rules. While smart business networks should provide advantages due to
APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION
Energy Technology Data Exchange (ETDEWEB)
Musson, John C. [JLAB; Seaton, Chad [JLAB; Spata, Mike F. [JLAB; Yan, Jianxun [JLAB
2012-11-01
Stripline BPM sensors contain inherent non-linearities, as a result of field distortions from the pickup elements. Many methods have been devised to facilitate corrections, often employing polynomial fitting. The cost of computation makes real-time correction difficult, particulalry when integer math is utilized. The application of neural-network technology, particularly the multi-layer perceptron algorithm, is proposed as an efficient alternative for electrode linearization. A process of supervised learning is initially used to determine the weighting coefficients, which are subsequently applied to the incoming electrode data. A non-linear layer, known as an activation layer, is responsible for the removal of saturation effects. Implementation of a perceptron in an FPGA-based software-defined radio (SDR) is presented, along with performance comparisons. In addition, efficient calculation of the sigmoidal activation function via the CORDIC algorithm is presented.
Methods of information theory and algorithmic complexity for network biology.
Zenil, Hector; Kiani, Narsis A; Tegnér, Jesper
2016-03-01
We survey and introduce concepts and tools located at the intersection of information theory and network biology. We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdös-Rényi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data. We provide exact theoretical calculations, numerical approximations and error estimations of entropy, algorithmic probability and Kolmogorov complexity for different types of graphs, characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity. Copyright © 2016 Elsevier Ltd. All rights reserved.
Using of coevolutionary algorithm on P2P networks
Directory of Open Access Journals (Sweden)
Rezaee Alireza
2009-01-01
Full Text Available Multicast routing is the basic demand to provide QOS (Quality of service in multimedia streaming on peer to peer networks. Making multicast trees optimizing their delay cost and considering nodal and links limited bandwidth (load balance constraints is a NP-hard (Nondeterministic Polynomial time hard problem. In this paper we have used Co-evolutionary Algorithm to make multicast trees with optimized average delay from source to the clients considering the limited capacity of nodes and links in application layer. The numeric results obtained are shown that the costs have been much improved comparing with other existent non-GA (Genetic Algorithm approaches. Also we have used only a portion of every nodal outage degree and this has improved the results comparing to use of the entire outage degree.
The Forward-Reverse Algorithm for Stochastic Reaction Networks
Bayer, Christian
2015-01-07
In this work, we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem of approximating the reaction coefficients based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which we solve a set of deterministic optimization problems where the SRNs are replaced by the classical ODE rates; then, during the second phase, the Monte Carlo version of the EM algorithm is applied starting from the output of the previous phase. Starting from a set of over-dispersed seeds, the output of our two-phase method is a cluster of maximum likelihood estimates obtained by using convergence assessment techniques from the theory of Markov chain Monte Carlo.
Neural network implementations of data association algorithms for sensor fusion
Brown, Donald E.; Pittard, Clarence L.; Martin, Worthy N.
1989-01-01
The paper is concerned with locating a time varying set of entities in a fixed field when the entities are sensed at discrete time instances. At a given time instant a collection of bivariate Gaussian sensor reports is produced, and these reports estimate the location of a subset of the entities present in the field. A database of reports is maintained, which ideally should contain one report for each entity sensed. Whenever a collection of sensor reports is received, the database must be updated to reflect the new information. This updating requires association processing between the database reports and the new sensor reports to determine which pairs of sensor and database reports correspond to the same entity. Algorithms for performing this association processing are presented. Neural network implementation of the algorithms, along with simulation results comparing the approaches are provided.
Theory of Algorithm Structure and Design
1990-10-05
Science of Computer Programming ). 6 P.I. Name: Douglas R. Smith Insitution: Kestrel Institute Telephone: (415) 493-6871 E-mail: smith@kestrel.edu...Theories and Design Tactics, invited paper to appear in a special issue on the Mathematics of Program Construction, Science of Computer Programming , 1990
MIRA: mutual information-based reporter algorithm for metabolic networks.
Cicek, A Ercument; Roeder, Kathryn; Ozsoyoglu, Gultekin
2014-06-15
Discovering the transcriptional regulatory architecture of the metabolism has been an important topic to understand the implications of transcriptional fluctuations on metabolism. The reporter algorithm (RA) was proposed to determine the hot spots in metabolic networks, around which transcriptional regulation is focused owing to a disease or a genetic perturbation. Using a z-score-based scoring scheme, RA calculates the average statistical change in the expression levels of genes that are neighbors to a target metabolite in the metabolic network. The RA approach has been used in numerous studies to analyze cellular responses to the downstream genetic changes. In this article, we propose a mutual information-based multivariate reporter algorithm (MIRA) with the goal of eliminating the following problems in detecting reporter metabolites: (i) conventional statistical methods suffer from small sample sizes, (ii) as z-score ranges from minus to plus infinity, calculating average scores can lead to canceling out opposite effects and (iii) analyzing genes one by one, then aggregating results can lead to information loss. MIRA is a multivariate and combinatorial algorithm that calculates the aggregate transcriptional response around a metabolite using mutual information. We show that MIRA's results are biologically sound, empirically significant and more reliable than RA. We apply MIRA to gene expression analysis of six knockout strains of Escherichia coli and show that MIRA captures the underlying metabolic dynamics of the switch from aerobic to anaerobic respiration. We also apply MIRA to an Autism Spectrum Disorder gene expression dataset. Results indicate that MIRA reports metabolites that highly overlap with recently found metabolic biomarkers in the autism literature. Overall, MIRA is a promising algorithm for detecting metabolic drug targets and understanding the relation between gene expression and metabolic activity. The code is implemented in C# language using
Cross-layer design in optical networks
Brandt-Pearce, Maïté; Demeester, Piet; Saradhi, Chava
2013-01-01
Optical networks have become an integral part of the communications infrastructure needed to support society’s demand for high-speed connectivity. Cross-Layer Design in Optical Networks addresses topics in optical network design and analysis with a focus on physical-layer impairment awareness and network layer service requirements, essential for the implementation and management of robust scalable networks. The cross-layer treatment includes bottom-up impacts of the physical and lambda layers, such as dispersion, noise, nonlinearity, crosstalk, dense wavelength packing, and wavelength line rates, as well as top-down approaches to handle physical-layer impairments and service requirements.
Optimization of neural network algorithm of the land market description
Directory of Open Access Journals (Sweden)
M. A. Karpovich
2016-01-01
Full Text Available The advantages of neural network technology is shown in comparison of traditional descriptions of dynamically changing systems, which include a modern land market. The basic difficulty arising in the practical implementation of neural network models of the land market and construction products is revealed It is the formation of a representative set of training and test examples. The requirements which are necessary for the correct description of the current economic situation has been determined, it consists in the fact that Train-paid-set in the feature space should not has the ranges with a low density of observations. The methods of optimization of empirical array, which allow to avoid the long-range extrapolation of data from range of concentration of the set of examples are formulated. It is shown that a radical method of optimization a set of training and test examples enclosing to collect supplemantary information, is associated with significant costs time and resources for the economic problems and the ratio of cost / efficiency is less efficient than an algorithm optimization neural network models the earth market fixed set of empirical data. Algorithm of optimization based on the transformation of arrays of information which represents the expansion of the ranges of concentration of the set of examples and compression the ranges of low density of observations is analyzed in details. The significant reduction in the relative error of land price description is demonstrated on the specific example of Voronezh region market of lands which intend for road construction, it makes the using of radical method of empirical optimization of the array costeffective with accounting the significant absolute value of the land. The high economic efficiency of the proposed algorithms is demonstrated.
Directory of Open Access Journals (Sweden)
Rongxin Tang
2015-10-01
Full Text Available Mobile sensor networks are an important part of modern robotics systems and are widely used in robotics applications. Therefore, sensor deployment is a key issue in current robotics systems research. Since it is one of the most popular deployment methods, in recent years the virtual force algorithm has been studied in detail by many scientists. In this paper, we focus on the virtual force algorithm and present a corresponding parameter investigation for mobile sensor deployment. We introduce an optimized virtual force algorithm based on the exchange force, in which a new shielding rule grounded in Delaunay triangulation is adopted. The algorithm employs a new performance metric called ‘pair-correlation diversion', designed to evaluate the uniformity and topology of the sensor distribution. We also discuss the implementation of the algorithm's computation and analyse the influence of experimental parameters on the algorithm. Our results indicate that the area ratio, φs, and the exchange force constant, G, influence the final performance of the sensor deployment in terms of the coverage rate, the convergence time and topology uniformity. Using simulations, we were able to verify the effectiveness of our algorithm and we obtained an optimal region for the (φs, G-parameter space which, in the future, could be utilized as an aid for experiments in robotic sensor deployment.
Directory of Open Access Journals (Sweden)
Tao Ma
2016-10-01
Full Text Available The development of intrusion detection systems (IDS that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC and deep neural network (DNN algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN, support vector machine (SVM, random forest (RF and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.
Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun
2016-10-13
The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.
Mixed Transportation Network Design under a Sustainable Development Perspective
Qin, Jin; Ni, Ling-lin; Shi, Feng
2013-01-01
A mixed transportation network design problem considering sustainable development was studied in this paper. Based on the discretization of continuous link-grade decision variables, a bilevel programming model was proposed to describe the problem, in which sustainability factors, including vehicle exhaust emissions, land-use scale, link load, and financial budget, are considered. The objective of the model is to minimize the total amount of resources exploited under the premise of meeting all the construction goals. A heuristic algorithm, which combined the simulated annealing and path-based gradient projection algorithm, was developed to solve the model. The numerical example shows that the transportation network optimized with the method above not only significantly alleviates the congestion on the link, but also reduces vehicle exhaust emissions within the network by up to 41.56%. PMID:23476142
Mixed Transportation Network Design under a Sustainable Development Perspective
Directory of Open Access Journals (Sweden)
Jin Qin
2013-01-01
Full Text Available A mixed transportation network design problem considering sustainable development was studied in this paper. Based on the discretization of continuous link-grade decision variables, a bilevel programming model was proposed to describe the problem, in which sustainability factors, including vehicle exhaust emissions, land-use scale, link load, and financial budget, are considered. The objective of the model is to minimize the total amount of resources exploited under the premise of meeting all the construction goals. A heuristic algorithm, which combined the simulated annealing and path-based gradient projection algorithm, was developed to solve the model. The numerical example shows that the transportation network optimized with the method above not only significantly alleviates the congestion on the link, but also reduces vehicle exhaust emissions within the network by up to 41.56%.
On Evaluating Power Loss with HATSGA Algorithm for Power Network Reconfiguration in the Smart Grid
Calhau, Flavio Galvão; Pezzutti, Alysson; Martins, Joberto S. B.
2017-01-01
This paper presents the power network reconfig-uration algorithm HATSGA with a " R " modeling approach and evaluates its behavior in computing new reconfiguration topologies for the power network in the Smart Grid context. The modelling of the power distribution network with the language " R " is used to represent the network and support computation of distinct algorithm configurations towards the evaluation of new reconfiguration topologies. The HATSGA algorithm adopts hybrid Tabu Search and...
A constructive algorithm for unsupervised learning with incremental neural network
Directory of Open Access Journals (Sweden)
Jenq-Haur Wang
2015-04-01
In our experiment, Reuters-21578 was used as the dataset to show the effectiveness of the proposed method on text classification. The experimental results showed that our method can effectively classify texts with the best F1-measure of 92.5%. It also showed the learning algorithm can enhance the accuracy effectively and efficiently. This framework also validates scalability in terms of the network size, in which the training and testing times both showed a constant trend. This also validates the feasibility of the method for practical uses.
The production route selection algorithm in virtual manufacturing networks
Krenczyk, D.; Skolud, B.; Olender, M.
2017-08-01
The increasing requirements and competition in the global market are challenges for the companies profitability in production and supply chain management. This situation became the basis for construction of virtual organizations, which are created in response to temporary needs. The problem of the production flow planning in virtual manufacturing networks is considered. In the paper the algorithm of the production route selection from the set of admissible routes, which meets the technology and resource requirements and in the context of the criterion of minimum cost is proposed.
Finite-Size Geometric Entanglement from Tensor Network Algorithms
Shi, Qian-Qian; Orus, Roman; Fjaerestad, John Ove; Zhou, Huan-Qiang
2009-01-01
The global geometric entanglement is studied in the context of newly-developed tensor network algorithms for finite systems. For one-dimensional quantum spin systems it is found that, at criticality, the leading finite-size correction to the global geometric entanglement per site behaves as $b/n$, where $n$ is the size of the system and $b$ a given coefficient. Our conclusion is based on the computation of the geometric entanglement per spin for the quantum Ising model in a transverse magneti...
Information Flows in Networked Engineering Design Projects
DEFF Research Database (Denmark)
Parraguez, Pedro; Maier, Anja
networks at the project level or in studying the social networks that deliver the “actual information flow”. In this paper we propose and empirically test a model and method that integrates both social and activity networks into one compact representation, allowing to compare actual and required......Complex engineering design projects need to manage simultaneously multiple information flows across design activities associated with different areas of the design process. Previous research on this area has mostly focused on either analysing the “required information flows” through activity...... information flows between design spaces, and to assess the influence that these misalignments could have on the performance of engineering design projects....
Generative Algorithmic Techniques for Architectural Design
DEFF Research Database (Denmark)
Larsen, Niels Martin
2012-01-01
, a series of methods directed towards specific types of geometric and architectural problems is developed, and form the basis for discussing the potentials and implications related to each problem type. These methods are often inspired by models developed within natural sciences, biology in particular....... In this situation, the architect’s attention is directed towards the form-generating rules, which then reflect design intents, contextual parameters and production constraints. An advantage of this approach is the capability of managing substantial complexity in terms of geometry and performance. This is partly due....... The principles are further developed to form new modes of articulation in architectural design. Certain methods are contributions, which suggest a potential for future use and development. Thus, a method is directed towards bottom-up generation of surface topology through the use of an agentbased logic. Another...
Robustness and Optimization of Complex Networks : Reconstructability, Algorithms and Modeling
Liu, D.
2013-01-01
The infrastructure networks, including the Internet, telecommunication networks, electrical power grids, transportation networks (road, railway, waterway, and airway networks), gas networks and water networks, are becoming more and more complex. The complex infrastructure networks are crucial to our
Evaluation of clustering algorithms for protein-protein interaction networks
Directory of Open Access Journals (Sweden)
van Helden Jacques
2006-11-01
Full Text Available Abstract Background Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism. In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods (mass spectrometry, two-hybrid methods, genetic studies. High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions. The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL, Restricted Neighborhood Search Clustering (RNSC, Super Paramagnetic Clustering (SPC, and Molecular Complex Detection (MCODE. Results A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions. Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes. We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values. We also evaluated their robustness to alterations of the test graph. We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes. Conclusion This
Congestion Relief of Contingent Power Network with Evolutionary Optimization Algorithm
Directory of Open Access Journals (Sweden)
Abhinandan De
2012-03-01
Full Text Available This paper presents a differential evolution optimization technique based methodology for congestion management cost optimization of contingent power networks. In Deregulated systems, line congestion apart from causing stability problems can increase the cost of electricity. Restraining line flow to a particular level of congestion is quite imperative from stability as well as economy point of view. Employing Congestion Sensitivity Index proposed in this paper, the algorithm proposed can be adopted for selecting the congested lines in a power networks and then to search for a congestion constrained optimal generation schedule at the cost of a minimum congestion management charge without any load curtailment and installation of FACTS devices. It has been depicted that the methodology on application can provide better operating conditions in terms of improvement of bus voltage and loss profile of the system. The efficiency of the proposed methodology has been tested on an IEEE 30 bus benchmark system and the results look promising.
Structural design optimization of vehicle components using Cuckoo Search Algorithm
Energy Technology Data Exchange (ETDEWEB)
Yildiz, Ali Riza [Bursa Technical Univ., Bursa (Turkey). Dept. of Mechanical Engineering; Durgun, Ismail
2012-07-01
In order to meet today's vehicle design requirements and to improve the cost and fuel efficiency, there is an increasing interest to design light-weight and cost-effective vehicle components. In this research, a new optimization algorithm, called the Cuckoo Search Algorithm (CS) algorithm, is introduced for solving structural design optimization problems. This research is the first application of the CS to the shape design optimization problems in the literature. The CS algorithm is applied to the structural design optimization of a vehicle component to illustrate how the present approach can be applied for solving structural design problems. Results show the ability of the CS to find better optimal structural design. [German] Um heutige Anforderungen an das Fahrzeugdesign zu beruecksichtigen und um die Kosten- und Kraftstoffeffektivitaet zu erhoehen, nimmt das Interesse am Design leichter und kosteneffektiver Fahrzeugkomponenten weiterhin zu. In der diesem Beitrag zugrunde liegenden Studie wurde ein neuer Optimierungsalgorithmus angewendet, der so genannte Cuckoo Suchalgorithmus (CS). Es handelt sich um die erste CS-Applikation fuer das Formdesign in der Literatur. Der CS-Algorithmus wird hierbei zur Strukturdesignoptimierung einer Fahrzeugkomponente angewendet, um zu zeigen, wie er bei der Loesung von Strukturdesignaufgaben angewendet werden kann. Die Ergebnisse zeigen, wie damit ein verbessertes Design erreicht werden kann.
Validating module network learning algorithms using simulated data.
Michoel, Tom; Maere, Steven; Bonnet, Eric; Joshi, Anagha; Saeys, Yvan; Van den Bulcke, Tim; Van Leemput, Koenraad; van Remortel, Piet; Kuiper, Martin; Marchal, Kathleen; Van de Peer, Yves
2007-05-03
In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators. We show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network
Directory of Open Access Journals (Sweden)
Young-Bo Sim
2017-11-01
Full Text Available In this paper, we proposed and developed Function-Oriented Networking (FON, a platform for network users. It has a different philosophy as opposed to technologies for network managers of Software-Defined Networking technology, OpenFlow. It is a technology that can immediately reflect the demands of the network users in the network, unlike the existing OpenFlow and Network Functions Virtualization (NFV, which do not reflect directly the needs of the network users. It allows the network user to determine the policy of the direct network, so it can be applied more precisely than the policy applied by the network manager. This is expected to increase the satisfaction of the service users when the network users try to provide new services. We developed FON function that performs on-demand routing for Low-Delay Required service. We analyzed the characteristics of the Ant Colony Optimization (ACO algorithm and found that the algorithm is suitable for low-delay required services. It was also the first in the world to implement the routing software using ACO Algorithm in the real Ethernet network. In order to improve the routing performance, several algorithms of the ACO Algorithm have been developed to enable faster path search-routing and path recovery. The relationship between the network performance index and the ACO routing parameters is derived, and the results are compared and analyzed. Through this, it was possible to develop the ACO algorithm.
Fast Moving Sampling Designs in Temporal Networks
Thompson, Steven K
2015-01-01
In a study related to this one I set up a temporal network simulation environment for evaluating network intervention strategies. A network intervention strategy consists of a sampling design to select nodes in the network. An intervention is applied to nodes in the sample for the purpose of changing the wider network in some desired way. The network intervention strategies can represent natural agents such as viruses that spread in the network, programs to prevent or reduce the virus spread, and the agency of individual nodes, such as people, in forming and dissolving the links that create, maintain or change the network. The present paper examines idealized versions of the sampling designs used to that study. The purpose is to better understand the natural and human network designs in real situations and to provide a simple inference of design-based properties that in turn measure properties of the time-changing network. The designs use link tracing and sometimes other probabilistic procedures to add units ...
Algorithme intelligent d'optimisation d'un design structurel de grande envergure
Dominique, Stephane
The implementation of an automated decision support system in the field of design and structural optimisation can give a significant advantage to any industry working on mechanical designs. Indeed, by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work, the system may reduce the project cycle time, or allow more time to produce a better design. This thesis presents a new approach to automate a design process based on Case-Based Reasoning (CBR), in combination with a new genetic algorithm named Genetic Algorithm with Territorial core Evolution (GATE). This approach was developed in order to reduce the operating cost of the process. However, as the system implementation cost is quite expensive, the approach is better suited for large scale design problem, and particularly for design problems that the designer plans to solve for many different specification sets. First, the CBR process uses a databank filled with every known solution to similar design problems. Then, the closest solutions to the current problem in term of specifications are selected. After this, during the adaptation phase, an artificial neural network (ANN) interpolates amongst known solutions to produce an additional solution to the current problem using the current specifications as inputs. Each solution produced and selected by the CBR is then used to initialize the population of an island of the genetic algorithm. The algorithm will optimise the solution further during the refinement phase. Using progressive refinement, the algorithm starts using only the most important variables for the problem. Then, as the optimisation progress, the remaining variables are gradually introduced, layer by layer. The genetic algorithm that is used is a new algorithm specifically created during this thesis to solve optimisation problems from the field of mechanical device structural design. The algorithm is named GATE, and is essentially a real number
Design and analysis of cryptographic algorithms
DEFF Research Database (Denmark)
Kölbl, Stefan
In today’s world computers are ubiquitous. They can be found in virtually any industry and most households own at least one personal computer or have a mobile phone. Apart from these fairly large and complex devices, we also see computers on a much smaller scale appear in everyday objects....... From securing our passwords and personal data to protecting mobile communication from eavesdroppers and our electronic bank transactions from manipulation. These applications would be impossible without cryptography. The main topic of this thesis is the design and security analysis of the most...
Mandal, Sudip; Saha, Goutam; Pal, Rajat Kumar
2017-08-01
Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.
Han, Guangjie; Li, Shanshan; Zhu, Chunsheng; Jiang, Jinfang; Zhang, Wenbo
2017-02-08
Marine environmental monitoring provides crucial information and support for the exploitation, utilization, and protection of marine resources. With the rapid development of information technology, the development of three-dimensional underwater acoustic sensor networks (3D UASNs) provides a novel strategy to acquire marine environment information conveniently, efficiently and accurately. However, the specific propagation effects of acoustic communication channel lead to decreased successful information delivery probability with increased distance. Therefore, we investigate two probabilistic neighborhood-based data collection algorithms for 3D UASNs which are based on a probabilistic acoustic communication model instead of the traditional deterministic acoustic communication model. An autonomous underwater vehicle (AUV) is employed to traverse along the designed path to collect data from neighborhoods. For 3D UASNs without prior deployment knowledge, partitioning the network into grids can allow the AUV to visit the central location of each grid for data collection. For 3D UASNs in which the deployment knowledge is known in advance, the AUV only needs to visit several selected locations by constructing a minimum probabilistic neighborhood covering set to reduce data latency. Otherwise, by increasing the transmission rounds, our proposed algorithms can provide a tradeoff between data collection latency and information gain. These algorithms are compared with basic Nearest-neighbor Heuristic algorithm via simulations. Simulation analyses show that our proposed algorithms can efficiently reduce the average data collection completion time, corresponding to a decrease of data latency.
Acoustic design of rotor blades using a genetic algorithm
Wells, V. L.; Han, A. Y.; Crossley, W. A.
1995-01-01
A genetic algorithm coupled with a simplified acoustic analysis was used to generate low-noise rotor blade designs. The model includes thickness, steady loading and blade-vortex interaction noise estimates. The paper presents solutions for several variations in the fitness function, including thickness noise only, loading noise only, and combinations of the noise types. Preliminary results indicate that the analysis provides reasonable assessments of the noise produced, and that genetic algorithm successfully searches for 'good' designs. The results show that, for a given required thrust coefficient, proper blade design can noticeably reduce the noise produced at some expense to the power requirements.
Optimal fractional order PID design via Tabu Search based algorithm.
Ateş, Abdullah; Yeroglu, Celaleddin
2016-01-01
This paper presents an optimization method based on the Tabu Search Algorithm (TSA) to design a Fractional-Order Proportional-Integral-Derivative (FOPID) controller. All parameter computations of the FOPID employ random initial conditions, using the proposed optimization method. Illustrative examples demonstrate the performance of the proposed FOPID controller design method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
An ellipsoid algorithm for probabilistic robust controller design
Kanev, S.K.; de Schutter, B.; Verhaegen, M.H.G.
2003-01-01
In this paper, a new iterative approach to probabilistic robust controller design is presented, which is applicable to any robust controller/filter design problem that can be represented as an LMI feasibility problem. Recently, a probabilistic Subgradient Iteration algorithm was proposed for solving
Foundations of digital signal processing theory, algorithms and hardware design
Gaydecki, Patrick
2005-01-01
An excellent introductory text, this book covers the basic theoretical, algorithmic and real-time aspects of digital signal processing (DSP). Detailed information is provided on off-line, real-time and DSP programming and the reader is effortlessly guided through advanced topics such as DSP hardware design, FIR and IIR filter design and difference equation manipulation.
Optimization of cocoa butter analog synthesis variables using neural networks and genetic algorithm.
Shekarchizadeh, Hajar; Tikani, Reza; Kadivar, Mahdi
2014-09-01
Cocoa butter analog was prepared from camel hump fat and tristearin by enzymatic interesterification in supercritical carbon dioxide (SC-CO2) using immobilized Thermomyces lanuginosus lipase (Lipozyme TL IM) as a biocatalyst. Optimal process conditions were determined using neural networks and genetic algorithm optimization. Response surfaces methodology was used to design the experiments to collect data for the neural network modelling. A general regression neural network model was developed to predict the response of triacylglycerol (TAG) distribution of cocoa butter analog from the process pressure, temperature, tristearin/camel hump fat ratio, water content, and incubation time. A genetic algorithm was used to search for a combination of the process variables for production of most similar cocoa butter analog to the corresponding cocoa butter. The combinations of the process variables during genetic algorithm optimization were evaluated using the neural network model. The pressure of 10 MPa; temperature of 40 °C; SSS/CHF ratio of 0.6:1; water content of 13 % (w/w); and incubation time of 4.5 h were found to be the optimum conditions to achieve the most similar cocoa butter analog to the corresponding cocoa butter.
Automatic design of decision-tree induction algorithms
Barros, Rodrigo C; Freitas, Alex A
2015-01-01
Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning, and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain o
Liu, Huanlin; Wang, Chujun; Chen, Yong
2018-01-01
Large-capacity encoding fiber Bragg grating (FBG) sensor network is widely used in modern long-term health monitoring system. Encoding FBG sensors have greatly improved the capacity of distributed FBG sensor network. However, the error of addressing increases correspondingly with the enlarging of capacity. To address the issue, an improved algorithm called genetic tracking algorithm (GTA) is proposed in the paper. In the GTA, for improving the success rate of matching and reducing the large number of redundant matching operations generated by sequential matching, the individuals are designed based on the feasible matching. Then, two kinds of self-crossover ways and a dynamic variation during mutation process are designed to increase the diversity of individuals and to avoid falling into local optimum. Meanwhile, an assistant decision is proposed to handle the issue that the GTA cannot solve when the variation of sensor information is highly overlapped. The simulation results indicate that the proposed GTA has higher accuracy compared with the traditional tracking algorithm and the enhanced tracking algorithm. In order to address the problems of spectrum fragmentation and low sharing degree of spectrum resources in survivable.
Design Methodology for Self-organized Mobile Networks Based
Directory of Open Access Journals (Sweden)
John Petearson Anzola
2016-06-01
Full Text Available The methodology proposed in this article enables a systematic design of routing algorithms based on schemes of biclustering, which allows you to respond with timely techniques, clustering heuristics proposed by a researcher, and a focused approach to routing in the choice of clusterhead nodes. This process uses heuristics aimed at improving the different costs in communication surface groups called biclusters. This methodology globally enables a variety of techniques and heuristics of clustering that have been addressed in routing algorithms, but we have not explored all possible alternatives and their different assessments. Therefore, the methodology oriented design research of routing algorithms based on biclustering schemes will allow new concepts of evolutionary routing along with the ability to adapt the topological changes that occur in self-organized data networks.
A Fair Resource Allocation Algorithm for Data and Energy Integrated Communication Networks
Directory of Open Access Journals (Sweden)
Qin Yu
2016-01-01
Full Text Available With the rapid advancement of wireless network technologies and the rapid increase in the number of mobile devices, mobile users (MUs have an increasing high demand to access the Internet with guaranteed quality-of-service (QoS. Data and energy integrated communication networks (DEINs are emerging as a new type of wireless networks that have the potential to simultaneously transfer wireless energy and information via the same base station (BS. This means that a physical BS is virtualized into two parts: one is transferring energy and the other is transferring information. The former is called virtual energy base station (eBS and the latter is named as data base station (dBS. One important issue in such setting is dynamic resource allocation. Here the resource concerned includes both power and time. In this paper, we propose a fair data-and-energy resource allocation algorithm for DEINs by jointly designing the downlink energy beamforming and a power-and-time allocation scheme, with the consideration of finite capacity batteries at MUs and power sensitivity of radio frequency (RF to direct current (DC conversion circuits. Simulation results demonstrate that our proposed algorithm outperforms the existing algorithms in terms of fairness, beamforming design, sensitivity, and average throughput.
Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization
Directory of Open Access Journals (Sweden)
Lianbo Ma
2014-01-01
Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.
Combining neural networks and genetic algorithms for hydrological flow forecasting
Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr
2010-05-01
We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and
Energy Technology Data Exchange (ETDEWEB)
Arteaga A, T.; Ortiz R, J. M.; Vega C, H. R., E-mail: tarcicio70@yahoo.co.uk [Universidad Autonoma de Zacatecas, Av. Lopez Velarde 117, 98600 Zacatecas, Zac. (Mexico)
2016-10-15
Artificial neural networks (Ann) are widely used; it which consist of an input layer, one or more hidden layers and an output layer; these layers contain neurons and each has connections called weights, where the knowledge are allowed and let to Ann solve problems proposed. These Ann is used to reconstruction of the energy spectrum of neutrons from count rates and develop Bonner sphere neutron dosimetry. Currently, we have developed Ann with high performance and generalization ability. Determine your optimal architecture is usually a difficult task, an exhaustive search of all possible combinations of parameters is rarely possible further training of the neural network with random initial weights can cause two major drawbacks: it can stuck in local minima or converge very slowly. In this project it will be used Genetic Algorithms (Ga); which are based on the principle or analogy of evolution through natural selection and has shown to be very effective in optimizing complex search functions and large spaces or to find a near optimal overall efficiency. The aim is to decrease the architecture in number of hidden neurons and therefore the total number of connections is reducing. The benefits obtained by optimizing the network are that the number of connections would be considerably smaller and thus the computational complexity, hardware integration, resources will be lower such that will allow to be even more viable implemented. To use the Ga three problems must be solve: 1) coding the problem into chromosomes. 2) Construct a fitness function. 3) Proper selection of genetic operators; crossover, selection, mutation. As a result, the scientific knowledge obtained can to be applied to similar problems having a reference parameters used and their impact on the optimization would to be generated. It concluded that the input layer and output are subject to the problem; the Ga propose the optimal number of neurons in the hidden layer without losing the quality of the
Tel, G.
We define the notion of total algorithms for networks of processes. A total algorithm enforces that a "decision" is taken by a subset of the processes, and that participation of all processes is required to reach this decision. Total algorithms are an important building block in the design of
Energy Efficient Routing Algorithms in Dynamic Optical Core Networks with Dual Energy Sources
DEFF Research Database (Denmark)
Wang, Jiayuan; Fagertun, Anna Manolova; Ruepp, Sarah Renée
2013-01-01
This paper proposes new energy efficient routing algorithms in optical core networks, with the application of solar energy sources and bundled links. A comprehensive solar energy model is described in the proposed network scenarios. Network performance in energy savings, connection blocking...... probability, resource utilization and bundled link usage are evaluated with dynamic network simulations. Results show that algorithms proposed aiming for reducing the dynamic part of the energy consumption of the network may raise the fixed part of the energy consumption meanwhile....
Protocol and networking design issues for local access WDM networks
Salvador, M.R.; Heemstra de Groot, S.M.; Niemegeers, I.G.M.M.
This report gives an overview of some of the protocol and networking design issues that have been addressed in Flamingo, a major ongoing project which investigates the use of WDM optical technology in local access networks. Quality of service delivery and wavelength assignment are focused on in this
Green corridors and network design
DEFF Research Database (Denmark)
Panagakos, George
2016-01-01
major overhaul of the EU transportation infrastructure policy is outlined and the basic differences with the past are pinpointed. The provisions of the new TEN-T Guidelines are scrutinized so as to check whether the TEN-T core network corridors exhibit the characteristics of a green corridor......The purpose of this chapter is to investigate the relation between the Trans-European Transport Network (TEN-T) and the green corridor concept. First, the need is established for a corridor governance structure that enables the close cooperation among the numerous stakeholders from both the public......, as they have been identified in the previous chapter. Based on the results of this analysis, it is concluded that the TEN-T core network is, as far as its freight dimension is concerned, a network of green corridors....
Directory of Open Access Journals (Sweden)
Hongliang Zhu
2017-01-01
Full Text Available In big data era, the single detection techniques have already not met the demand of complex network attacks and advanced persistent threats, but there is no uniform standard to make different correlation analysis detection be performed efficiently and accurately. In this paper, we put forward a universal correlation analysis detection model and algorithm by introducing state transition diagram. Based on analyzing and comparing the current correlation detection modes, we formalize the correlation patterns and propose a framework according to data packet timing and behavior qualities and then design a new universal algorithm to implement the method. Finally, experiment, which sets up a lightweight intrusion detection system using KDD1999 dataset, shows that the correlation detection model and algorithm can improve the performance and guarantee high detection rates.
Neural networks art: solving problems with multiple solutions and new teaching algorithm.
Dmitrienko, V D; Zakovorotnyi, A Yu; Leonov, S Yu; Khavina, I P
2014-01-01
A new discrete neural networks adaptive resonance theory (ART), which allows solving problems with multiple solutions, is developed. New algorithms neural networks teaching ART to prevent degradation and reproduction classes at training noisy input data is developed. Proposed learning algorithms discrete ART networks, allowing obtaining different classification methods of input.
Kun Zhang; Zhao Hu; Xiao-Ting Gan; Jian-Bo Fang
2016-01-01
Due to the fact that the fluctuation of network traffic is affected by various factors, accurate prediction of network traffic is regarded as a challenging task of the time series prediction process. For this purpose, a novel prediction method of network traffic based on QPSO algorithm and fuzzy wavelet neural network is proposed in this paper. Firstly, quantum-behaved particle swarm optimization (QPSO) was introduced. Then, the structure and operation algorithms of WFNN are presented. The pa...
Photonic network-on-chip design
Bergman, Keren; Biberman, Aleksandr; Chan, Johnnie; Hendry, Gilbert
2013-01-01
This book provides a comprehensive synthesis of the theory and practice of photonic devices for networks-on-chip. It outlines the issues in designing photonic network-on-chip architectures for future many-core high performance chip multiprocessors. The discussion is built from the bottom up: starting with the design and implementation of key photonic devices and building blocks, reviewing networking and network-on-chip theory and existing research, and finishing with describing various architectures, their characteristics, and the impact they will have on a computing system. After acquainting
Microarchitecture of network-on-chip routers a designer's perspective
Dimitrakopoulos, Giorgos; Seitanidis, Ioannis
2014-01-01
This book provides a unified overview of network-on-chip router micro-architecture, the corresponding design opportunities and challenges, and existing solutions to overcome these challenges. The discussion focuses on the heart of a NoC, the NoC router, and how it interacts with the rest of the system. Coverage includes both basic and advanced design techniques that cover the entire router design space including router organization, flow control, pipelined operation, buffering architectures, as well as allocators' structure and algorithms. Router micro-architectural options are presented in a
Mean field analysis of algorithms for scale-free networks in molecular biology.
Konini, S; Janse van Rensburg, E J
2017-01-01
The sampling of scale-free networks in Molecular Biology is usually achieved by growing networks from a seed using recursive algorithms with elementary moves which include the addition and deletion of nodes and bonds. These algorithms include the Barabási-Albert algorithm. Later algorithms, such as the Duplication-Divergence algorithm, the Solé algorithm and the iSite algorithm, were inspired by biological processes underlying the evolution of protein networks, and the networks they produce differ essentially from networks grown by the Barabási-Albert algorithm. In this paper the mean field analysis of these algorithms is reconsidered, and extended to variant and modified implementations of the algorithms. The degree sequences of scale-free networks decay according to a powerlaw distribution, namely P(k) ∼ k-γ, where γ is a scaling exponent. We derive mean field expressions for γ, and test these by numerical simulations. Generally, good agreement is obtained. We also found that some algorithms do not produce scale-free networks (for example some variant Barabási-Albert and Solé networks).
Improved Quantum-Inspired Evolutionary Algorithm for Engineering Design Optimization
Directory of Open Access Journals (Sweden)
Jinn-Tsong Tsai
2012-01-01
Full Text Available An improved quantum-inspired evolutionary algorithm is proposed for solving mixed discrete-continuous nonlinear problems in engineering design. The proposed Latin square quantum-inspired evolutionary algorithm (LSQEA combines Latin squares and quantum-inspired genetic algorithm (QGA. The novel contribution of the proposed LSQEA is the use of a QGA to explore the optimal feasible region in macrospace and the use of a systematic reasoning mechanism of the Latin square to exploit the better solution in microspace. By combining the advantages of exploration and exploitation, the LSQEA provides higher computational efficiency and robustness compared to QGA and real-coded GA when solving global numerical optimization problems with continuous variables. Additionally, the proposed LSQEA approach effectively solves mixed discrete-continuous nonlinear design optimization problems in which the design variables are integers, discrete values, and continuous values. The computational experiments show that the proposed LSQEA approach obtains better results compared to existing methods reported in the literature.
Design of an acoustic metamaterial lens using genetic algorithms.
Li, Dennis; Zigoneanu, Lucian; Popa, Bogdan-Ioan; Cummer, Steven A
2012-10-01
The present work demonstrates a genetic algorithm approach to optimizing the effective material parameters of an acoustic metamaterial. The target device is an acoustic gradient index (GRIN) lens in air, which ideally possesses a maximized index of refraction, minimized frequency dependence of the material properties, and minimized acoustic impedance mismatch. Applying this algorithm results in complex designs with certain common features, and effective material properties that are better than those present in previous designs. After modifying the optimized unit cell designs to make them suitable for fabrication, a two-dimensional lens was built and experimentally tested. Its performance was in good agreement with simulations. Overall, the optimization approach was able to improve the refractive index but at the cost of increased frequency dependence. The optimal solutions found by the algorithm provide a numerical description of how the material parameters compete with one another and thus describes the level of performance achievable in the GRIN lens.
Breadth-first search-based single-phase algorithms for bridge detection in wireless sensor networks.
Akram, Vahid Khalilpour; Dagdeviren, Orhan
2013-07-10
Wireless sensor networks (WSNs) are promising technologies for exploring harsh environments, such as oceans, wild forests, volcanic regions and outer space. Since sensor nodes may have limited transmission range, application packets may be transmitted by multi-hop communication. Thus, connectivity is a very important issue. A bridge is a critical edge whose removal breaks the connectivity of the network. Hence, it is crucial to detect bridges and take preventions. Since sensor nodes are battery-powered, services running on nodes should consume low energy. In this paper, we propose energy-efficient and distributed bridge detection algorithms for WSNs. Our algorithms run single phase and they are integrated with the Breadth-First Search (BFS) algorithm, which is a popular routing algorithm. Our first algorithm is an extended version of Milic's algorithm, which is designed to reduce the message length. Our second algorithm is novel and uses ancestral knowledge to detect bridges. We explain the operation of the algorithms, analyze their proof of correctness, message, time, space and computational complexities. To evaluate practical importance, we provide testbed experiments and extensive simulations. We show that our proposed algorithms provide less resource consumption, and the energy savings of our algorithms are up by 5.5-times.
Genetic Algorithm for Restricted Maximum k-Satisfiability in the Hopfield Network
Directory of Open Access Journals (Sweden)
Mohd Shareduwan Bin Mohd Kasihmuddin
2016-12-01
Full Text Available The restricted Maximum k-Satisfiability MAX- kSAT is an enhanced Boolean satisfiability counterpart that has attracted numerous amount of research. Genetic algorithm has been the prominent optimization heuristic algorithm to solve constraint optimization problem. The core motivation of this paper is to introduce Hopfield network incorporated with genetic algorithm in solving MAX-kSAT problem. Genetic algorithm will be integrated with Hopfield network as a single network. The proposed method will be compared with the conventional Hopfield network. The results demonstrate that Hopfield network with genetic algorithm outperforms conventional Hopfield networks. Furthermore, the outcome had provided a solid evidence of the robustness of our proposed algorithms to be used in other satisfiability problem.
A VLSI design concept for parallel iterative algorithms
Directory of Open Access Journals (Sweden)
C. C. Sun
2009-05-01
Full Text Available Modern VLSI manufacturing technology has kept shrinking down to the nanoscale level with a very fast trend. Integration with the advanced nano-technology now makes it possible to realize advanced parallel iterative algorithms directly which was almost impossible 10 years ago. In this paper, we want to discuss the influences of evolving VLSI technologies for iterative algorithms and present design strategies from an algorithmic and architectural point of view. Implementing an iterative algorithm on a multiprocessor array, there is a trade-off between the performance/complexity of processors and the load/throughput of interconnects. This is due to the behavior of iterative algorithms. For example, we could simplify the parallel implementation of the iterative algorithm (i.e., processor elements of the multiprocessor array in any way as long as the convergence is guaranteed. However, the modification of the algorithm (processors usually increases the number of required iterations which also means that the switch activity of interconnects is increasing. As an example we show that a 25×25 full Jacobi EVD array could be realized into one single FPGA device with the simplified μ-rotation CORDIC architecture.
Network design management and technical perspectives
Piliouras, Teresa C
2004-01-01
MAKING THE BUSINESS CASE FOR THE NETWORKManagement Overview of Network DesignDefine the Business ObjectivesDetermine Potential Risks, Dependencies, Costs andBenefitsIdentify Project RequirementsDevelop Project Implementation ApproachStrategic Positioning Using NetworksCase StudiesCalculation of Technology's Strategic ValueDealing with Major Design ChallengesOrganizational Concerns and RecommendationsTechnology Concerns and RecommendationsSimilarities and Differences between Voice and DataNetwork Design and PlanningMajor Trends and Technology EnablersSocietal TrendsService Provider TrendsMajor
Zhang, Jian
2017-06-24
Traditional methods for image compressive sensing (CS) reconstruction solve a well-defined inverse problem that is based on a predefined CS model, which defines the underlying structure of the problem and is generally solved by employing convergent iterative solvers. These optimization-based CS methods face the challenge of choosing optimal transforms and tuning parameters in their solvers, while also suffering from high computational complexity in most cases. Recently, some deep network based CS algorithms have been proposed to improve CS reconstruction performance, while dramatically reducing time complexity as compared to optimization-based methods. Despite their impressive results, the proposed networks (either with fully-connected or repetitive convolutional layers) lack any structural diversity and they are trained as a black box, void of any insights from the CS domain. In this paper, we combine the merits of both types of CS methods: the structure insights of optimization-based method and the performance/speed of network-based ones. We propose a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $l_1$ norm CS reconstruction model. ISTA-Net essentially implements a truncated form of ISTA, where all ISTA-Net parameters are learned end-to-end to minimize a reconstruction error in training. Borrowing more insights from the optimization realm, we propose an accelerated version of ISTA-Net, dubbed FISTA-Net, which is inspired by the fast iterative shrinkage-thresholding algorithm (FISTA). Interestingly, this acceleration naturally leads to skip connections in the underlying network design. Extensive CS experiments demonstrate that the proposed ISTA-Net and FISTA-Net outperform existing optimization-based and network-based CS methods by large margins, while maintaining a fast runtime.
Topology Management Algorithms for Large Scale Aerial High Capacity Directional Networks
2016-11-01
Topology Management Algorithms for Large-Scale Aerial High Capacity Directional Networks Joy Wang, Thomas Shake, Patricia Deutsch, Andrea Coyle, Bow...airborne backbone network is large- scale topology management of directional links in a dynamic environment. In this paper, we present several... topology manage- ment algorithms for large scale airborne networks and evaluate the performance of these algorithms under various scenarios. In each case
Quality of Service Issues for Reinforcement Learning Based Routing Algorithm for Ad-Hoc Networks
Kulkarni, Shrirang Ambaji; Rao, G. Raghavendra
2012-01-01
Mobile ad-hoc networks are dynamic networks which are decentralized and autonomous in nature. Many routing algorithms have been proposed for these dynamic networks. It is an important problem to model Quality of Service requirements on these types of algorithms which traditionally have certain limitations. To model this scenario we have considered a reinforcement learning algorithm SAMPLE. SAMPLE promises to deal effectively with congestion and under high traffic load. As it is natural for ad...
Inference in Belief Network using Logic Sampling and Likelihood Weighing algorithms
Directory of Open Access Journals (Sweden)
K. S. JASMINE
2013-11-01
Full Text Available Over the time in computational history, belief networks have become an increasingly popular mechanism for dealing with uncertainty in systems. It is known that identifying the probability values of belief network nodes given a set of evidence is not amenable in general. Many different simulation algorithms for approximating solution to this problem have been proposed and implemented. This paper details the implementation of such algorithms, in particular the two algorithms of the belief networks namely Logic sampling and the likelihood weighing are discussed. A detailed description of the algorithm is given with observed results. These algorithms play crucial roles in dynamic decision making in any situation of uncertainty.
Energy Technology Data Exchange (ETDEWEB)
Madduri, Kamesh; Bader, David A.
2009-02-15
Graph-theoretic abstractions are extensively used to analyze massive data sets. Temporal data streams from socioeconomic interactions, social networking web sites, communication traffic, and scientific computing can be intuitively modeled as graphs. We present the first study of novel high-performance combinatorial techniques for analyzing large-scale information networks, encapsulating dynamic interaction data in the order of billions of entities. We present new data structures to represent dynamic interaction networks, and discuss algorithms for processing parallel insertions and deletions of edges in small-world networks. With these new approaches, we achieve an average performance rate of 25 million structural updates per second and a parallel speedup of nearly28 on a 64-way Sun UltraSPARC T2 multicore processor, for insertions and deletions to a small-world network of 33.5 million vertices and 268 million edges. We also design parallel implementations of fundamental dynamic graph kernels related to connectivity and centrality queries. Our implementations are freely distributed as part of the open-source SNAP (Small-world Network Analysis and Partitioning) complex network analysis framework.
Consensus-based sparse signal reconstruction algorithm for wireless sensor networks
National Research Council Canada - National Science Library
Peng, Bao; Zhao, Zhi; Han, Guangjie; Shen, Jian
2016-01-01
This article presents a distributed Bayesian reconstruction algorithm for wireless sensor networks to reconstruct the sparse signals based on variational sparse Bayesian learning and consensus filter...
The Design and Analysis of Virtual Network Configuration for a Wireless Mobile ATM Network
Bush, Stephen F.
1999-05-01
This research concentrates on the design and analysis of an algorithm referred to as Virtual Network Configuration (VNC) which uses predicted future states of a system for faster network configuration and management. VNC is applied to the configuration of a wireless mobile ATM network. VNC is built on techniques from parallel discrete event simulation merged with constraints from real-time systems and applied to mobile ATM configuration and handoff. Configuration in a mobile network is a dynamic and continuous process. Factors such as load, distance, capacity and topology are all constantly changing in a mobile environment. The VNC algorithm anticipates configuration changes and speeds the reconfiguration process by pre-computing and caching results. VNC propagates local prediction results throughout the VNC enhanced system. The Global Positioning System is an enabling technology for the use of VNC in mobile networks because it provides location information and accurate time for each node. This research has resulted in well defined structures for the encapsulation of physical processes within Logical Processes and a generic library for enhancing a system with VNC. Enhancing an existing system with VNC is straight forward assuming the existing physical processes do not have side effects. The benefit of prediction is gained at the cost of additional traffic and processing. This research includes an analysis of VNC and suggestions for optimization of the VNC algorithm and its parameters.
Performance Analysis of User Pairing Algorithm in Full-Duplex Cellular Networks
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Wonjong Noh
2017-01-01
Full Text Available In a full duplexing (FD wireless cellular network, a base station operates in FD mode, while the downlink (DL and uplink (UL users operate in half duplexing (HD mode. Thus, the downlink and uplink transmissions occur simultaneously so that interuser interference from a UL to a DL user occurs. In an FD network, the main challenge to minimize the interuser interference is user pairing, which determines a pair of DL and UL users who use the same radio resource simultaneously. We formulate a nonconvex optimization problem for user pairing to maximize the cell throughput. Then, we propose a heuristic user pairing algorithm with low complexity. This algorithm is designed such that the DL user having a better signal quality has higher priority to choose its paired UL user for throughput maximization. Thereafter, we conduct theoretical performance analysis of the FD cellular system based on stochastic geometry and analyze the impact of the user paring algorithm on the performance of the FD cellular system. Results show that the FD system that uses the proposed user pairing algorithm effectively reduces the interuser interference and approaches optimal performance. It also considerably outperforms the FD system using a random user pairing and almost doubles the conventional HD system in terms of cell throughput.
A Biologically Inspired Energy-Efficient Duty Cycle Design Method for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Jie Zhou
2017-01-01
Full Text Available The recent success of emerging wireless sensor networks technology has encouraged researchers to develop new energy-efficient duty cycle design algorithm in this field. The energy-efficient duty cycle design problem is a typical NP-hard combinatorial optimization problem. In this paper, we investigate an improved elite immune evolutionary algorithm (IEIEA strategy to optimize energy-efficient duty cycle design scheme and monitored area jointly to enhance the network lifetimes. Simulation results show that the network lifetime of the proposed IEIEA method increased compared to the other two methods, which means that the proposed method improves the full coverage constraints.
Design of Automatic Extraction Algorithm of Knowledge Points for MOOCs
Chen, Haijian; Han, Dongmei; Zhao, Lina
2015-01-01
In recent years, Massive Open Online Courses (MOOCs) are very popular among college students and have a powerful impact on academic institutions. In the MOOCs environment, knowledge discovery and knowledge sharing are very important, which currently are often achieved by ontology techniques. In building ontology, automatic extraction technology is crucial. Because the general methods of text mining algorithm do not have obvious effect on online course, we designed automatic extracting course knowledge points (AECKP) algorithm for online course. It includes document classification, Chinese word segmentation, and POS tagging for each document. Vector Space Model (VSM) is used to calculate similarity and design the weight to optimize the TF-IDF algorithm output values, and the higher scores will be selected as knowledge points. Course documents of “C programming language” are selected for the experiment in this study. The results show that the proposed approach can achieve satisfactory accuracy rate and recall rate. PMID:26448738
Design of Automatic Extraction Algorithm of Knowledge Points for MOOCs
Directory of Open Access Journals (Sweden)
Haijian Chen
2015-01-01
Full Text Available In recent years, Massive Open Online Courses (MOOCs are very popular among college students and have a powerful impact on academic institutions. In the MOOCs environment, knowledge discovery and knowledge sharing are very important, which currently are often achieved by ontology techniques. In building ontology, automatic extraction technology is crucial. Because the general methods of text mining algorithm do not have obvious effect on online course, we designed automatic extracting course knowledge points (AECKP algorithm for online course. It includes document classification, Chinese word segmentation, and POS tagging for each document. Vector Space Model (VSM is used to calculate similarity and design the weight to optimize the TF-IDF algorithm output values, and the higher scores will be selected as knowledge points. Course documents of “C programming language” are selected for the experiment in this study. The results show that the proposed approach can achieve satisfactory accuracy rate and recall rate.
Distributed genetic algorithms for the floorplan design problem
Cohoon, James P.; Hegde, Shailesh U.; Martin, Worthy N.; Richards, Dana S.
1991-01-01
Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried.
Design of Automatic Extraction Algorithm of Knowledge Points for MOOCs.
Chen, Haijian; Han, Dongmei; Dai, Yonghui; Zhao, Lina
2015-01-01
In recent years, Massive Open Online Courses (MOOCs) are very popular among college students and have a powerful impact on academic institutions. In the MOOCs environment, knowledge discovery and knowledge sharing are very important, which currently are often achieved by ontology techniques. In building ontology, automatic extraction technology is crucial. Because the general methods of text mining algorithm do not have obvious effect on online course, we designed automatic extracting course knowledge points (AECKP) algorithm for online course. It includes document classification, Chinese word segmentation, and POS tagging for each document. Vector Space Model (VSM) is used to calculate similarity and design the weight to optimize the TF-IDF algorithm output values, and the higher scores will be selected as knowledge points. Course documents of "C programming language" are selected for the experiment in this study. The results show that the proposed approach can achieve satisfactory accuracy rate and recall rate.
A superlinear interior points algorithm for engineering design optimization
Herskovits, J.; Asquier, J.
1990-01-01
We present a quasi-Newton interior points algorithm for nonlinear constrained optimization. It is based on a general approach consisting of the iterative solution in the primal and dual spaces of the equalities in Karush-Kuhn-Tucker optimality conditions. This is done in such a way to have primal and dual feasibility at each iteration, which ensures satisfaction of those optimality conditions at the limit points. This approach is very strong and efficient, since at each iteration it only requires the solution of two linear systems with the same matrix, instead of quadratic programming subproblems. It is also particularly appropriate for engineering design optimization inasmuch at each iteration a feasible design is obtained. The present algorithm uses a quasi-Newton approximation of the second derivative of the Lagrangian function in order to have superlinear asymptotic convergence. We discuss theoretical aspects of the algorithm and its computer implementation.
A Hybrid Optimization Algorithm for Low RCS Antenna Design
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W. Shao
2012-12-01
Full Text Available In this article, a simple and efficient method is presented to design low radar cross section (RCS patch antennas. This method consists of a hybrid optimization algorithm, which combines a genetic algorithm (GA with tabu search algorithm (TSA, and electromagnetic field solver. The TSA, embedded into the GA frame, defines the acceptable neighborhood region of parameters and screens out the poor-scoring individuals. Thus, the repeats of search are avoided and the amount of time-consuming electromagnetic simulations is largely reduced. Moreover, the whole design procedure is auto-controlled by programming the VBScript language. A slot patch antenna example is provided to verify the accuracy and efficiency of the proposed method.
Designing and Evaluating Sustainable Logistics Networks
J. Quariguasi Frota Neto (João); J.M. Bloemhof-Ruwaard (Jacqueline); J.A.E.E. van Nunen (Jo); H.W.G.M. van Heck (Eric)
2006-01-01
textabstractThe objective in this paper is to shed light into the design of logistic networks balancing profit and the environment. More specifically we intend to i) determine the main factors influencing environmental performance and costs in logistic networks ii) present a comprehensive framework
Robust Network Design - Connectivity and Beyond
2015-01-15
data integrity part in subsections IIC, IID, IIE and IIF. II.A. RESOURCE EFFICIENT BACKBONE NETWORK DESIGN IN FAULT-FREE SCENARIO Prior to our...Optical Networks”, IEEE Transactions on Networking 25. S. Shirazipourazad, P. Ghosh and A. Sen, “On Connectivity of Airborne Networks” AIAA Journal of
Network architecture functional description and design
Energy Technology Data Exchange (ETDEWEB)
Stans, L.; Bencoe, M.; Brown, D.; Kelly, S.; Pierson, L.; Schaldach, C.
1989-05-25
This report provides a top level functional description and design for the development and implementation of the central network to support the next generation of SNL, Albuquerque supercomputer in a UNIX{reg sign} environment. It describes the network functions and provides an architecture and topology.