Extended shortest path selection for package routing of complex networks
Ye, Fan; Zhang, Lei; Wang, Bing-Hong; Liu, Lu; Zhang, Xing-Yi
The routing strategy plays a very important role in complex networks such as Internet system and Peer-to-Peer networks. However, most of the previous work concentrates only on the path selection, e.g. Flooding and Random Walk, or finding the shortest path (SP) and rarely considering the local load information such as SP and Distance Vector Routing. Flow-based Routing mainly considers load balance and still cannot achieve best optimization. Thus, in this paper, we propose a novel dynamic routing strategy on complex network by incorporating the local load information into SP algorithm to enhance the traffic flow routing optimization. It was found that the flow in a network is greatly affected by the waiting time of the network, so we should not consider only choosing optimized path for package transformation but also consider node congestion. As a result, the packages should be transmitted with a global optimized path with smaller congestion and relatively short distance. Analysis work and simulation experiments show that the proposed algorithm can largely enhance the network flow with the maximum throughput within an acceptable calculating time. The detailed analysis of the algorithm will also be provided for explaining the efficiency.
Shortest Paths and Vehicle Routing
DEFF Research Database (Denmark)
Petersen, Bjørn
This thesis presents how to parallelize a shortest path labeling algorithm. It is shown how to handle Chvátal-Gomory rank-1 cuts in a column generation context. A Branch-and-Cut algorithm is given for the Elementary Shortest Paths Problem with Capacity Constraint. A reformulation of the Vehicle...... Routing Problem based on partial paths is presented. Finally, a practical application of finding shortest paths in the telecommunication industry is shown....
Evaluation of Shortest Paths in Road Network
Directory of Open Access Journals (Sweden)
Farrukh Shehzad
2009-06-01
Full Text Available Optimization is a key factor in almost all the topics of operations research / management science and economics.The road networks can be optimized within different constraints like time, distance, cost and traffic running onthe roads.This study is based on optimization of real road network by means of distances. Two main objectives arepursued in this research: 1 road distances among different routes are composed in detail; 2 two standardalgorithms (Dijkstra and Floyd-Warshall algoritms are applied to optimize/minimize these distances for bothsingle-source and all-pairs shortest path problems.
An Efficient Shortest Path Routing Algorithm for Directed Indoor Environments
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Sultan Alamri
2018-03-01
Full Text Available Routing systems for outdoor space have become the focus of many research works. Such routing systems are based on spatial road networks where moving objects (such as cars are affected by the directed roads and the movement of traffic, which may include traffic jams. Indoor routing, on the other hand, must take into account the features of indoor space such as walls and rooms. In this paper, we take indoor routing in a new direction whereby we consider the features that a building has in common with outdoor spaces. Inside some buildings, there may be directed floors where moving objects must move in a certain direction through directed corridors in order to reach a certain location. For example, on train platforms or in museums, movement in the corridors may be directed. In these directed floor spaces, a routing system enabling a visitor to take the shortest path to a certain location is essential. Therefore, this work proposes a new approach for buildings with directed indoor spaces, where each room can be affected by the density of the moving objects. The proposed system obtains the shortest path between objects or rooms taking into consideration the directed indoor space and the capacity of the objects to move within each room/cell.
Directory of Open Access Journals (Sweden)
Mario Miler
2014-02-01
Full Text Available In the field of geoinformation and transportation science, the shortest path is calculated on graph data mostly found in road and transportation networks. This data is often stored in various database systems. Many applications dealing with transportation network require calculation of the shortest path. The objective of this research is to compare the performance of Dijkstra shortest path calculation in PostgreSQL (with pgRouting and Neo4j graph database for the purpose of determining if there is any difference regarding the speed of the calculation. Benchmarking was done on commodity hardware using OpenStreetMap road network. The first assumption is that Neo4j graph database would be well suited for the shortest path calculation on transportation networks but this does not come without some cost. Memory proved to be an issue in Neo4j setup when dealing with larger transportation networks.
comparative analysis and implementation of dijkstra's shortest path
African Journals Online (AJOL)
user
path problem requires finding a single shortest-path between given vertices s and t; ... Bridge in 1735, [5 – 10]. This problem led to the .... their advancements from new design paradigms, data structures ..... .
Algorithm for shortest path search in Geographic Information Systems by using reduced graphs.
Rodríguez-Puente, Rafael; Lazo-Cortés, Manuel S
2013-01-01
The use of Geographic Information Systems has increased considerably since the eighties and nineties. As one of their most demanding applications we can mention shortest paths search. Several studies about shortest path search show the feasibility of using graphs for this purpose. Dijkstra's algorithm is one of the classic shortest path search algorithms. This algorithm is not well suited for shortest path search in large graphs. This is the reason why various modifications to Dijkstra's algorithm have been proposed by several authors using heuristics to reduce the run time of shortest path search. One of the most used heuristic algorithms is the A* algorithm, the main goal is to reduce the run time by reducing the search space. This article proposes a modification of Dijkstra's shortest path search algorithm in reduced graphs. It shows that the cost of the path found in this work, is equal to the cost of the path found using Dijkstra's algorithm in the original graph. The results of finding the shortest path, applying the proposed algorithm, Dijkstra's algorithm and A* algorithm, are compared. This comparison shows that, by applying the approach proposed, it is possible to obtain the optimal path in a similar or even in less time than when using heuristic algorithms.
Dynamic shortest path problems : hybrid routing policies considering network disruptions
Sever, D.; Dellaert, N.P.; Woensel, van T.; Kok, de A.G.
2013-01-01
Traffic network disruptions lead to significant increases in transportation costs. We consider networks in which a number of links are vulnerable to these disruptions leading to a significantly higher travel time on these links. For these vulnerable links, we consider known link disruption
Experiments with the auction algorithm for the shortest path problem
DEFF Research Database (Denmark)
Larsen, Jesper; Pedersen, Ib
1999-01-01
The auction approach for the shortest path problem (SPP) as introduced by Bertsekas is tested experimentally. Parallel algorithms using the auction approach are developed and tested. Both the sequential and parallel auction algorithms perform significantly worse than a state-of-the-art Dijkstra-l......-like reference algorithm. Experiments are run on a distributed-memory MIMD class Meiko parallel computer....
Directory of Open Access Journals (Sweden)
Yunyue He
Full Text Available Emergency evacuation aims to transport people from dangerous places to safe shelters as quickly as possible. Police play an important role in the evacuation process, as they can handle traffic accidents immediately and help people move smoothly on roads. This paper investigates an evacuation routing problem that involves police resource allocation. We propose a novel k-th-shortest-path-based technique that uses explicit congestion control to optimize evacuation routing and police resource allocation. A nonlinear mixed-integer programming model is presented to formulate the problem. The model's objective is to minimize the overall evacuation clearance time. Two algorithms are given to solve the problem. The first one linearizes the original model and solves the linearized problem with CPLEX. The second one is a heuristic algorithm that uses a police resource utilization efficiency index to directly solve the original model. This police resource utilization efficiency index significantly aids in the evaluation of road links from an evacuation throughput perspective. The proposed algorithms are tested with a number of examples based on real data from cities of different sizes. The computational results show that the police resource utilization efficiency index is very helpful in finding near-optimal solutions. Additionally, comparing the performance of the heuristic algorithm and the linearization method by using randomly generated examples indicates that the efficiency of the heuristic algorithm is superior.
Bakar, Sumarni Abu; Ibrahim, Milbah
2017-08-01
The shortest path problem is a popular problem in graph theory. It is about finding a path with minimum length between a specified pair of vertices. In any network the weight of each edge is usually represented in a form of crisp real number and subsequently the weight is used in the calculation of shortest path problem using deterministic algorithms. However, due to failure, uncertainty is always encountered in practice whereby the weight of edge of the network is uncertain and imprecise. In this paper, a modified algorithm which utilized heuristic shortest path method and fuzzy approach is proposed for solving a network with imprecise arc length. Here, interval number and triangular fuzzy number in representing arc length of the network are considered. The modified algorithm is then applied to a specific example of the Travelling Salesman Problem (TSP). Total shortest distance obtained from this algorithm is then compared with the total distance obtained from traditional nearest neighbour heuristic algorithm. The result shows that the modified algorithm can provide not only on the sequence of visited cities which shown to be similar with traditional approach but it also provides a good measurement of total shortest distance which is lesser as compared to the total shortest distance calculated using traditional approach. Hence, this research could contribute to the enrichment of methods used in solving TSP.
Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping
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Fronita Mona
2018-01-01
Full Text Available Traveling Salesman Problem (TSP is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour’s to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.
Euclidean shortest paths exact or approximate algorithms
Li, Fajie
2014-01-01
This book reviews algorithms for the exact or approximate solution of shortest-path problems, with a specific focus on a class of algorithms called rubberband algorithms. The coverage includes mathematical proofs for many of the given statements.
Directory of Open Access Journals (Sweden)
Yogang Singh
2018-03-01
Full Text Available The growing need of ocean surveying and exploration for scientific and industrial application has led to the requirement of routing strategies for ocean vehicles which are optimal in nature. Most of the op-timal path planning for marine vehicles had been conducted offline in a self-made environment. This paper takes into account a practical marine environment, i.e. Portsmouth Harbour, for finding an optimal path in terms of computational time between source and end points on a real time map for an USV. The current study makes use of a grid map generated from original and uses a Dijkstra algorithm to find the shortest path for a single USV. In order to benchmark the study, a path planning study using a well-known local path planning method artificial path planning (APF has been conducted in a real time marine environment and effectiveness is measured in terms of path length and computational time.
A minimum resource neural network framework for solving multiconstraint shortest path problems.
Zhang, Junying; Zhao, Xiaoxue; He, Xiaotao
2014-08-01
Characterized by using minimum hard (structural) and soft (computational) resources, a novel parameter-free minimal resource neural network (MRNN) framework is proposed for solving a wide range of single-source shortest path (SP) problems for various graph types. The problems are the k-shortest time path problems with any combination of three constraints: time, hop, and label constraints, and the graphs can be directed, undirected, or bidirected with symmetric and/or asymmetric traversal time, which can be real and time dependent. Isomorphic to the graph where the SP is to be sought, the network is activated by generating autowave at source neuron and the autowave travels automatically along the paths with the speed of a hop in an iteration. Properties of the network are studied, algorithms are presented, and computation complexity is analyzed. The framework guarantees globally optimal solutions of a series of problems during the iteration process of the network, which provides insight into why even the SP is still too long to be satisfied. The network facilitates very large scale integrated circuit implementation and adapt to very large scale problems due to its massively parallel processing and minimum resource utilization. When implemented in a sequentially processing computer, experiments on synthetic graphs, road maps of cities of the USA, and vehicle routing with time windows indicate that the MRNN is especially efficient for large scale sparse graphs and even dense graphs with some constraints, e.g., the CPU time taken and the iteration number used for the road maps of cities of the USA is even less than ∼ 2% and 0.5% that of the Dijkstra's algorithm.
A MODIFIED GENETIC ALGORITHM FOR FINDING FUZZY SHORTEST PATHS IN UNCERTAIN NETWORKS
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A. A. Heidari
2016-06-01
Full Text Available In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical instances are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.
The shortest-path problem analysis and comparison of methods
Ortega-Arranz, Hector; Gonzalez-Escribano, Arturo
2014-01-01
Many applications in different domains need to calculate the shortest-path between two points in a graph. In this paper we describe this shortest path problem in detail, starting with the classic Dijkstra's algorithm and moving to more advanced solutions that are currently applied to road network routing, including the use of heuristics and precomputation techniques. Since several of these improvements involve subtle changes to the search space, it may be difficult to appreciate their benefits in terms of time or space requirements. To make methods more comprehensive and to facilitate their co
Degree distribution of shortest path trees and bias of network sampling algorithms
Bhamidi, S.; Goodman, J.A.; Hofstad, van der R.W.; Komjáthy, J.
2013-01-01
In this article, we explicitly derive the limiting distribution of the degree distribution of the shortest path tree from a single source on various random network models with edge weights. We determine the power-law exponent of the degree distribution of this tree and compare it to the degree
Degree distribution of shortest path trees and bias of network sampling algorithms
Bhamidi, S.; Goodman, J.A.; Hofstad, van der R.W.; Komjáthy, J.
2015-01-01
In this article, we explicitly derive the limiting degree distribution of the shortest path tree from a single source on various random network models with edge weights. We determine the asymptotics of the degree distribution for large degrees of this tree and compare it to the degree distribution
Establishment Of The Shortest Route A Prototype For Facilitation In Road Network
Directory of Open Access Journals (Sweden)
Sumaira Yousuf Khan
2015-08-01
Full Text Available Calculating the shortest path between two locations in a road network is a significant problem in network analysis. Roads play a pivotal role in day to day activities of masses live in places and areas. They travel for various purposes that are to study to work to shop and to supply their goods and the like from one place to another place. Even in this modern era roads remain one of the mediums used most frequently for travel and transportation. Being ignorant of the shortest routes people sometimes have to travel long distances consume extra precious time money and bare undesirable mental stress. Karachi is the second most populated and the seventh biggest city of the world. It is the central place of Pakistan which is famous for industry banking trade and economic activity and there are places which frequently visit by the inhabitants for their miscellaneous requirements. Federal Board of Revenue FBR is one of the departments that deal with taxation and revenue generation in the country. A common man residing near or distant areas often visit FBR to settle their business property and tax related issues. In order to facilitate the masses an effort is being made to develop a prototype based on Dijkstras Algorithm DA to establish a shortest route that will help individuals in navigation and subsequently alleviate difficulties faced by them in travelling road networks.
ESHOPPS: A COMPUTATIONAL TOOL TO AID THE TEACHING OF SHORTEST PATH ALGORITHMS
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S. J. de A. LIMA
2015-07-01
Full Text Available The development of a computational tool called EShoPPS – Environment for Shortest Path Problem Solving, which is used to assist students in understanding the working of Dijkstra, Greedy search and A*(star algorithms is presented in this paper. Such algorithms are commonly taught in graduate and undergraduate courses of Engineering and Informatics and are used for solving many optimization problems that can be characterized as Shortest Path Problem. The EShoPPS is an interactive tool that allows students to create a graph representing the problem and also helps in developing their knowledge of each specific algorithm. Experiments performed with 155 students of undergraduate and graduate courses such as Industrial Engineering, Computer Science and Information Systems have shown that by using the EShoPPS tool students were able to improve their interpretation of investigated algorithms.
Hoomod, Haider K.; Kareem Jebur, Tuka
2018-05-01
Mobile ad hoc networks (MANETs) play a critical role in today’s wireless ad hoc network research and consist of active nodes that can be in motion freely. Because it consider very important problem in this network, we suggested proposed method based on modified radial basis function networks RBFN and Self-Organizing Map SOM. These networks can be improved by the use of clusters because of huge congestion in the whole network. In such a system, the performance of MANET is improved by splitting the whole network into various clusters using SOM. The performance of clustering is improved by the cluster head selection and number of clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network. The SOM clustering average time (1-10 msec for stall nodes) and (8-75 msec for mobile nodes). While the routing time range (92-510 msec).The proposed system is faster than the Dijkstra by 150-300%, and faster from the RBFNN (without modify) by 145-180%.
Multiple Object Tracking Using the Shortest Path Faster Association Algorithm
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Zhenghao Xi
2014-01-01
Full Text Available To solve the persistently multiple object tracking in cluttered environments, this paper presents a novel tracking association approach based on the shortest path faster algorithm. First, the multiple object tracking is formulated as an integer programming problem of the flow network. Then we relax the integer programming to a standard linear programming problem. Therefore, the global optimum can be quickly obtained using the shortest path faster algorithm. The proposed method avoids the difficulties of integer programming, and it has a lower worst-case complexity than competing methods but better robustness and tracking accuracy in complex environments. Simulation results show that the proposed algorithm takes less time than other state-of-the-art methods and can operate in real time.
Solving fuzzy shortest path problem by genetic algorithm
Syarif, A.; Muludi, K.; Adrian, R.; Gen, M.
2018-03-01
Shortest Path Problem (SPP) is known as one of well-studied fields in the area Operations Research and Mathematical Optimization. It has been applied for many engineering and management designs. The objective is usually to determine path(s) in the network with minimum total cost or traveling time. In the past, the cost value for each arc was usually assigned or estimated as a deteministic value. For some specific real world applications, however, it is often difficult to determine the cost value properly. One way of handling such uncertainty in decision making is by introducing fuzzy approach. With this situation, it will become difficult to solve the problem optimally. This paper presents the investigations on the application of Genetic Algorithm (GA) to a new SPP model in which the cost values are represented as Triangular Fuzzy Number (TFN). We adopts the concept of ranking fuzzy numbers to determine how good the solutions. Here, by giving his/her degree value, the decision maker can determine the range of objective value. This would be very valuable for decision support system in the real world applications.Simulation experiments were carried out by modifying several test problems with 10-25 nodes. It is noted that the proposed approach is capable attaining a good solution with different degree of optimism for the tested problems.
An Improved Routing Optimization Algorithm Based on Travelling Salesman Problem for Social Networks
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Naixue Xiong
2017-06-01
Full Text Available A social network is a social structure, which is organized by the relationships or interactions between individuals or groups. Humans link the physical network with social network, and the services in the social world are based on data and analysis, which directly influence decision making in the physical network. In this paper, we focus on a routing optimization algorithm, which solves a well-known and popular problem. Ant colony algorithm is proposed to solve this problem effectively, but random selection strategy of the traditional algorithm causes evolution speed to be slow. Meanwhile, positive feedback and distributed computing model make the algorithm quickly converge. Therefore, how to improve convergence speed and search ability of algorithm is the focus of the current research. The paper proposes the improved scheme. Considering the difficulty about searching for next better city, new parameters are introduced to improve probability of selection, and delay convergence speed of algorithm. To avoid the shortest path being submerged, and improve sensitive speed of finding the shortest path, it updates pheromone regulation formula. The results show that the improved algorithm can effectively improve convergence speed and search ability for achieving higher accuracy and optimal results.
Ranking shortest paths in Stochastic time-denpendent networks
DEFF Research Database (Denmark)
Nielsen, Lars Relund; Andersen, Kim Allan; Pretolani, Daniele
A substantial amount of research has been devoted to the shortest path problem in networks where travel times are stochastic or (deterministic and) time-dependent. More recently, a growing interest has been attracted by networks that are both stochastic and time-dependent. In these networks, the ...... present a computational comparison of time-adaptive and a priori route choices, pointing out the effect of travel time and cost distributions. The reported results show that, under realistic distributions, our solution methods are effective.......A substantial amount of research has been devoted to the shortest path problem in networks where travel times are stochastic or (deterministic and) time-dependent. More recently, a growing interest has been attracted by networks that are both stochastic and time-dependent. In these networks...
K shortest paths in stochastic time-dependent networks
DEFF Research Database (Denmark)
Nielsen, Lars Relund; Pretolani, Daniele; Andersen, Kim Allan
2004-01-01
A substantial amount of research has been devoted to the shortest path problem in networks where travel times are stochastic or (deterministic and) time-dependent. More recently, a growing interest has been attracted by networks that are both stochastic and time-dependent. In these networks, the ...... present a computational comparison of time-adaptive and a priori route choices, pointing out the effect of travel time and cost distributions. The reported results show that, under realistic distributions, our solution methods are effective.......A substantial amount of research has been devoted to the shortest path problem in networks where travel times are stochastic or (deterministic and) time-dependent. More recently, a growing interest has been attracted by networks that are both stochastic and time-dependent. In these networks...
2012-09-13
46, 1989. [75] S. Melkote and M.S. Daskin . An integrated model of facility location and transportation network design. Transportation Research Part A ... a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT/DS/ENS/12-09 THE AVERAGE NETWORK FLOW PROBLEM...focused thinking (VFT) are used sparingly, as is the case across the entirety of the supply chain literature. We provide a VFT tutorial for supply chain
Calculating Graph Algorithms for Dominance and Shortest Path
DEFF Research Database (Denmark)
Sergey, Ilya; Midtgaard, Jan; Clarke, Dave
2012-01-01
We calculate two iterative, polynomial-time graph algorithms from the literature: a dominance algorithm and an algorithm for the single-source shortest path problem. Both algorithms are calculated directly from the definition of the properties by fixed-point fusion of (1) a least fixed point...... expressing all finite paths through a directed graph and (2) Galois connections that capture dominance and path length. The approach illustrates that reasoning in the style of fixed-point calculus extends gracefully to the domain of graph algorithms. We thereby bridge common practice from the school...... of program calculation with common practice from the school of static program analysis, and build a novel view on iterative graph algorithms as instances of abstract interpretation...
Software complex for finding the path routings in synchronous digital hierarchy networks
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V. A. Bulanov
2014-01-01
Full Text Available Communication network constructions based on the synchronous digital hierarchy (SDH are widely used in design and operation of telephone networks and data transfer ones on the basis of dedicated links. The providing the link for data transmission from one node to another one is a nontrivial objective. The network equipment vendors, as a rule, may provide also a network system control. However, these systems can be used only with the manufacturer's equipment, but are not intended for finding the optimal paths for the current network operation. They serve for examination to choose a network structure at the stage of its development or modernization for the known in advance matrix of connections. From the practice it is seen that the real networks structures use diverse equipment. Therefore to develop a complex to find the paths in SDH-based network is a challenge.Dijkstra algorithm is implemented to find the shortest network path routings in the complex. The use of the classical algorithm is impossible because of the restrictions in providing main and reserve links. There are multiplex sections with no routes to be expected. Such restriction is due to real arrangement of some multiplex sections in the same cables, but in different fibres. When providing the main link, the appropriate structuring sections should be preferable. For example, the link E3 (34 Mbit/s can pass only across the section of relevant configuration. The reserve link cannot pass across those multiplex sections where the main link has passed. This rule is not 'extra' considering the first restriction since the main and reserve link sections cannot crisscross the node. At the dead ends reserving has no sense, and the reserve link has to coincide with the main one to save a multiplex section resource of terminal switching. In implementation all these restrictions have been taken into consideration.In addition to the path routings, complex enables saving information on the network
A new approach to shortest paths on networks based on the quantum bosonic mechanism
Energy Technology Data Exchange (ETDEWEB)
Jiang Xin; Wang Hailong; Tang Shaoting; Ma Lili; Zhang Zhanli; Zheng Zhiming, E-mail: jiangxin@ss.buaa.edu.cn [Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Ministry of Education, Beijing University of Aeronautics and Astronautics, 100191 Beijing (China)
2011-01-15
This paper presents quantum bosonic shortest path searching (QBSPS), a natural, practical and highly heuristic physical algorithm for reasoning about the recognition of network structure via quantum dynamics. QBSPS is based on an Anderson-like itinerant bosonic system in which a boson's Green function is used as a navigation pointer for one to accurately approach the terminals. QBSPS is demonstrated by rigorous mathematical and physical proofs and plenty of simulations, showing how it can be used as a greedy routing to seek the shortest path between different locations. In methodology, it is an interesting and new algorithm rooted in the quantum mechanism other than combinatorics. In practice, for the all-pairs shortest-path problem in a random scale-free network with N vertices, QBSPS runs in O({mu}(N) ln ln N) time. In application, we suggest that the corresponding experimental realizations are feasible by considering path searching in quantum optical communication networks; in this situation, the method performs a pure local search on networks without requiring the global structure that is necessary for current graph algorithms.
Shortest path problem on a grid network with unordered intermediate points
Saw, Veekeong; Rahman, Amirah; Eng Ong, Wen
2017-10-01
We consider a shortest path problem with single cost factor on a grid network with unordered intermediate points. A two stage heuristic algorithm is proposed to find a feasible solution path within a reasonable amount of time. To evaluate the performance of the proposed algorithm, computational experiments are performed on grid maps of varying size and number of intermediate points. Preliminary results for the problem are reported. Numerical comparisons against brute forcing show that the proposed algorithm consistently yields solutions that are within 10% of the optimal solution and uses significantly less computation time.
On the application of Dijkstra\\'s algorithms in solving the GSM ...
African Journals Online (AJOL)
On the application of Dijkstra\\'s algorithms in solving the GSM Network problem. A W Gbolagade, R K Odunaike, Z O Ogunwobi. Abstract. Journal of the Nigerian Association of Mathematical Physics Vol. 8 2004: pp. 319-322. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL ...
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.
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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.
External Memory Algorithms for Diameter and All-Pair Shortest-Paths on Sparse Graphs
DEFF Research Database (Denmark)
Arge, Lars; Meyer, Ulrich; Toma, Laura
2004-01-01
We present several new external-memory algorithms for finding all-pairs shortest paths in a V -node, Eedge undirected graph. For all-pairs shortest paths and diameter in unweighted undirected graphs we present cache-oblivious algorithms with O(V · E B logM B E B) I/Os, where B is the block-size a...
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J. Trdlicka
2010-12-01
Full Text Available This work proposes a distributed algorithm for energy optimal routing in a wireless sensor network. The routing problem is described as a mathematical problem by the minimum-cost multi-commodity network flow problem. Due to the separability of the problem, we use the duality theorem to derive the distributed algorithm. The algorithm computes the energy optimal routing in the network without any central node or knowledge of the whole network structure. Each node only needs to know the flow which is supposed to send or receive and the costs and capacities of the neighboring links. An evaluation of the presented algorithm on benchmarks for the energy optimal data flow routing in sensor networks with up to 100 nodes is presented.
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David S. Hardin
2013-04-01
Full Text Available As Graphics Processing Units (GPUs have gained in capability and GPU development environments have matured, developers are increasingly turning to the GPU to off-load the main host CPU of numerically-intensive, parallelizable computations. Modern GPUs feature hundreds of cores, and offer programming niceties such as double-precision floating point, and even limited recursion. This shift from CPU to GPU, however, raises the question: how do we know that these new GPU-based algorithms are correct? In order to explore this new verification frontier, we formalized a parallelizable all-pairs shortest path (APSP algorithm for weighted graphs, originally coded in NVIDIA's CUDA language, in ACL2. The ACL2 specification is written using a single-threaded object (stobj and tail recursion, as the stobj/tail recursion combination yields the most straightforward translation from imperative programming languages, as well as efficient, scalable executable specifications within ACL2 itself. The ACL2 version of the APSP algorithm can process millions of vertices and edges with little to no garbage generation, and executes at one-sixth the speed of a host-based version of APSP coded in C – a very respectable result for a theorem prover. In addition to formalizing the APSP algorithm (which uses Dijkstra's shortest path algorithm at its core, we have also provided capability that the original APSP code lacked, namely shortest path recovery. Path recovery is accomplished using a secondary ACL2 stobj implementing a LIFO stack, which is proven correct. To conclude the experiment, we ported the ACL2 version of the APSP kernels back to C, resulting in a less than 5% slowdown, and also performed a partial back-port to CUDA, which, surprisingly, yielded a slight performance increase.
Directory of Open Access Journals (Sweden)
Petruseva Silvana
2006-01-01
Full Text Available This paper discusses the comparison of the efficiency of two algorithms, by estimation of their complexity. For solving the problem, the Neural Network Crossbar Adaptive Array (NN-CAA is used as the agent architecture, implementing a model of an emotion. The problem discussed is how to find the shortest path in an environment with n states. The domains concerned are environments with n states, one of which is the starting state, one is the goal state, and some states are undesirable and they should be avoided. It is obtained that finding one path (one solution is efficient, i.e. in polynomial time by both algorithms. One of the algorithms is faster than the other only in the multiplicative constant, and it shows a step forward toward the optimality of the learning process. However, finding the optimal solution (the shortest path by both algorithms is in exponential time which is asserted by two theorems. It might be concluded that the concept of subgoal is one step forward toward the optimality of the process of the agent learning. Yet, it should be explored further on, in order to obtain an efficient, polynomial algorithm.
Algorithms for finding optimal paths in network games with p players
Directory of Open Access Journals (Sweden)
R. Boliac
1997-08-01
Full Text Available We study the problem of finding optimal paths in network games with p players. Some polynomial-time algorithms for finding optimal paths and optimal by Nash strategies of the players in network games with p players are proposed.
Directory of Open Access Journals (Sweden)
Yifan Hu
2012-01-01
Full Text Available The fault-tolerant routing problem is important consideration in the design of heterogeneous wireless sensor networks (H-WSNs applications, and has recently been attracting growing research interests. In order to maintain k disjoint communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which multiple paths are calculated and maintained in advance, and alternate paths are created once the previous routing is broken. Then, we propose an immune cooperative particle swarm optimization algorithm (ICPSOA in the model to provide the fast routing recovery and reconstruct the network topology for path failure in H-WSNs. In the ICPSOA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by immune mechanism, which can enhance the capacity of global search and improve the converging rate of the algorithm. Then we validate this theoretical model with simulation results. The results indicate that the ICPSOA-based fault-tolerant routing protocol outperforms several other protocols due to its capability of fast routing recovery mechanism, reliable communications, and prolonging the lifetime of WSNs.
Directory of Open Access Journals (Sweden)
J R Managbanag
Full Text Available BACKGROUND: Identification of genes that modulate longevity is a major focus of aging-related research and an area of intense public interest. In addition to facilitating an improved understanding of the basic mechanisms of aging, such genes represent potential targets for therapeutic intervention in multiple age-associated diseases, including cancer, heart disease, diabetes, and neurodegenerative disorders. To date, however, targeted efforts at identifying longevity-associated genes have been limited by a lack of predictive power, and useful algorithms for candidate gene-identification have also been lacking. METHODOLOGY/PRINCIPAL FINDINGS: We have utilized a shortest-path network analysis to identify novel genes that modulate longevity in Saccharomyces cerevisiae. Based on a set of previously reported genes associated with increased life span, we applied a shortest-path network algorithm to a pre-existing protein-protein interaction dataset in order to construct a shortest-path longevity network. To validate this network, the replicative aging potential of 88 single-gene deletion strains corresponding to predicted components of the shortest-path longevity network was determined. Here we report that the single-gene deletion strains identified by our shortest-path longevity analysis are significantly enriched for mutations conferring either increased or decreased replicative life span, relative to a randomly selected set of 564 single-gene deletion strains or to the current data set available for the entire haploid deletion collection. Further, we report the identification of previously unknown longevity genes, several of which function in a conserved longevity pathway believed to mediate life span extension in response to dietary restriction. CONCLUSIONS/SIGNIFICANCE: This work demonstrates that shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity and represents the first application of
Directory of Open Access Journals (Sweden)
Matheswaran Saravanan
2014-01-01
Full Text Available Wireless sensor network (WSN consists of sensor nodes that need energy efficient routing techniques as they have limited battery power, computing, and storage resources. WSN routing protocols should enable reliable multihop communication with energy constraints. Clustering is an effective way to reduce overheads and when this is aided by effective resource allocation, it results in reduced energy consumption. In this work, a novel hybrid evolutionary algorithm called Bee Algorithm-Simulated Annealing Weighted Minimal Spanning Tree (BASA-WMST routing is proposed in which randomly deployed sensor nodes are split into the best possible number of independent clusters with cluster head and optimal route. The former gathers data from sensors belonging to the cluster, forwarding them to the sink. The shortest intrapath selection for the cluster is selected using Weighted Minimum Spanning Tree (WMST. The proposed algorithm computes the distance-based Minimum Spanning Tree (MST of the weighted graph for the multihop network. The weights are dynamically changed based on the energy level of each sensor during route selection and optimized using the proposed bee algorithm simulated annealing algorithm.
Congestion control and routing over satellite networks
Cao, Jinhua
) method and then develop a novel on-demand routing system named Cross Entropy Accelerated Ant Routing System (CEAARS) for regular constellation LEO satellite networks. By implementing simulations on an Iridium-like satellite network, we compare the proposed CEAARS algorithm with the two approaches to adaptive routing protocols on the Internet: distance-vector (DV) and link-state (LS), as well as with the original Cross Entropy Ant Routing System (CEARS). DV algorithms are based on distributed Bellman Ford algorithm, and LS algorithms are implementation of Dijkstras single source shortest path. The results show that CEAARS not only remarkably improves the convergence speed of achieving optimal or suboptimal paths, but also reduces the number of overhead ants (management packets).
Parallel algorithms for network routing problems and recurrences
International Nuclear Information System (INIS)
Wisniewski, J.A.; Sameh, A.H.
1982-01-01
In this paper, we consider the parallel solution of recurrences, and linear systems in the regular algebra of Carre. These problems are equivalent to solving the shortest path problem in graph theory, and they also arise in the analysis of Fortran programs. Our methods for solving linear systems in the regular algebra are analogues of well-known methods for solving systems of linear algebraic equations. A parallel version of Dijkstra's method, which has no linear algebraic analogue, is presented. Considerations for choosing an algorithm when the problem is large and sparse are also discussed
Case Study on Optimal Routing in Logistics Network by Priority-based Genetic Algorithm
Wang, Xiaoguang; Lin, Lin; Gen, Mitsuo; Shiota, Mitsushige
Recently, research on logistics caught more and more attention. One of the important issues on logistics system is to find optimal delivery routes with the least cost for products delivery. Numerous models have been developed for that reason. However, due to the diversity and complexity of practical problem, the existing models are usually not very satisfying to find the solution efficiently and convinently. In this paper, we treat a real-world logistics case with a company named ABC Co. ltd., in Kitakyusyu Japan. Firstly, based on the natures of this conveyance routing problem, as an extension of transportation problem (TP) and fixed charge transportation problem (fcTP) we formulate the problem as a minimum cost flow (MCF) model. Due to the complexity of fcTP, we proposed a priority-based genetic algorithm (pGA) approach to find the most acceptable solution to this problem. In this pGA approach, a two-stage path decoding method is adopted to develop delivery paths from a chromosome. We also apply the pGA approach to this problem, and compare our results with the current logistics network situation, and calculate the improvement of logistics cost to help the management to make decisions. Finally, in order to check the effectiveness of the proposed method, the results acquired are compared with those come from the two methods/ software, such as LINDO and CPLEX.
Shortest Path Problems in a Stochastic and Dynamic Environment
National Research Council Canada - National Science Library
Cho, Jae
2003-01-01
.... Particularly, we develop a variety of algorithms to solve the expected shortest path problem in addition to techniques for computing the total travel time distribution along a path in the network...
Directory of Open Access Journals (Sweden)
Mazyar Seraj
2014-10-01
Full Text Available This paper describes an experimental study of learning Dijkstra’s shortest path algorithm on mobile devices. The aim of the study is to investigate and compare the impacts of two different mobile screen user interfaces on students’ satisfaction for learning the technical subject. A mobile learning prototype was developed for learning Dijkstra’s shortest path algorithm on Apple iPhone 4 operated on iPhone operating system (iOS, and Acer Inconia Tab operated on an Android operating system. Thirty students, who are either currently studying or had previously studied Computer Networks, were recruited for the usability trial. At the end of each single session, students’ satisfaction interacting with the two mobile devices was measured using QUIS questionnaire. Although there is no significant difference in students’ satisfaction between the two different mobile screen interfaces, the subjective findings indicate that Acer Inconia Tab gained higher scores as compared to Apple iPhone 4.
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Durisman Durisman
2017-09-01
Full Text Available Banda Aceh city and Aceh Besar Regency are two of the leading tourism areas located in the province of Aceh. For travelling, there are some important things to be considered, such as determining schedule and distance of tourism. Every tourist certainly chooses the shortest route to reach the destination since it can save time, energy, and money. The purpose of this reserach is to develop a method that can be used in calculating the shortest route and applied to the tourism of Banda Aceh city and Aceh Besar regency. In this reserach, Ant Colony Optimization algorithm is used to determine the shortest route to tourism of Banda Aceh city and Aceh Besar regency. From the analysis made by using both manual calculation and GUI MATLAB program application test, the shortest route can be obtained with a minimum distance of 120.85 km in one travel. Based on the test result, the application for tourism (in Banda Aceh city and Aceh Besar regency shortest route searching built by utilizing the Ant Colony Optimization algorithm can find optimal route. Keyword: tourism, the shortest route, Ant Colony Optimization
Floyd-warshall algorithm to determine the shortest path based on android
Ramadiani; Bukhori, D.; Azainil; Dengen, N.
2018-04-01
The development of technology has made all areas of life easier now, one of which is the ease of obtaining geographic information. The use of geographic information may vary according to need, for example, the digital map learning, navigation systems, observations area, and much more. With the support of adequate infrastructure, almost no one will ever get lost to a destination even to foreign places or that have never been visited before. The reasons why many institutions and business entities use technology to improve services to consumers and to streamline the production process undertaken and so forth. Speaking of the efficient, there are many elements related to efficiency in navigation systems, and one of them is the efficiency in terms of distance. The shortest distance determination algorithm required in this research is used Floyd-Warshall Algorithm. Floyd-Warshall algorithm is the algorithm to find the fastest path and the shortest distance between 2 nodes, while the program is intended to find the path of more than 2 nodes.
QoS Routing in Ad-Hoc Networks Using GA and Multi-Objective Optimization
Directory of Open Access Journals (Sweden)
Admir Barolli
2011-01-01
Full Text Available Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN (GA-based routing algorithm for Mobile Ad-hoc Networks to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR (Genetic Load Balancing Routing.
Efficient evaluation of shortest travel-time path queries through spatial mashups
Zhang, Detian; Chow, Chi-Yin; Liu, An; Zhang, Xiangliang; Ding, Qingzhu; Li, Qing
2017-01-01
In the real world, the route/path with the shortest travel time in a road network is more meaningful than that with the shortest network distance for location-based services (LBS). However, not every LBS provider has adequate resources to compute/estimate travel time for routes by themselves. A cost-effective way for LBS providers to estimate travel time for routes is to issue external route requests to Web mapping services (e.g., Google Maps, Bing Maps, and MapQuest Maps). Due to the high cost of processing such external route requests and the usage limits of Web mapping services, we take the advantage of direction sharing, parallel requesting and waypoints supported by Web mapping services to reduce the number of external route requests and the query response time for shortest travel-time route queries in this paper. We first give the definition of sharing ability to reflect the possibility of sharing the direction information of a route with others, and find out the queries that their query routes are independent with each other for parallel processing. Then, we model the problem of selecting the optimal waypoints for an external route request as finding the longest simple path in a weighted complete digraph. As it is a MAX SNP-hard problem, we propose a greedy algorithm with performance guarantee to find the best set of waypoints in an external route request. We evaluate the performance of our approach using a real Web mapping service, a real road network, real and synthetic data sets. Experimental results show the efficiency, scalability, and applicability of our approach.
Efficient evaluation of shortest travel-time path queries through spatial mashups
Zhang, Detian
2017-01-07
In the real world, the route/path with the shortest travel time in a road network is more meaningful than that with the shortest network distance for location-based services (LBS). However, not every LBS provider has adequate resources to compute/estimate travel time for routes by themselves. A cost-effective way for LBS providers to estimate travel time for routes is to issue external route requests to Web mapping services (e.g., Google Maps, Bing Maps, and MapQuest Maps). Due to the high cost of processing such external route requests and the usage limits of Web mapping services, we take the advantage of direction sharing, parallel requesting and waypoints supported by Web mapping services to reduce the number of external route requests and the query response time for shortest travel-time route queries in this paper. We first give the definition of sharing ability to reflect the possibility of sharing the direction information of a route with others, and find out the queries that their query routes are independent with each other for parallel processing. Then, we model the problem of selecting the optimal waypoints for an external route request as finding the longest simple path in a weighted complete digraph. As it is a MAX SNP-hard problem, we propose a greedy algorithm with performance guarantee to find the best set of waypoints in an external route request. We evaluate the performance of our approach using a real Web mapping service, a real road network, real and synthetic data sets. Experimental results show the efficiency, scalability, and applicability of our approach.
Pereira, Hernane Borges de Barros; Pérez Vidal, Lluís; Lozada, Eleazar G. Madrid
2003-01-01
Behavioral impedance domain consists of a theory on route planning for pedestrians, within which constraint management is considered. The goal of this paper is to present the k-shortest path model using the behavioral impedance approach. After the mathematical model building, optimization problem and resolution problem by a behavioral impedance algorithm, it is discussed how behavioral impedance cost function is embedded in the k-shortest path model. From the pedestrian's route planning persp...
SPTH3: subroutine for finding shortest sabotage paths
International Nuclear Information System (INIS)
Hulme, B.L.; Holdridge, D.B.
1977-07-01
This document explains how to construct a sabotage graph which models any fixed-site facility and how to use the subroutine SPTH3 to find shortest paths in the graph. The shortest sabotage paths represent physical routes through the site which would allow an adversary to take advantage of the greatest weaknesses in the system of barriers and alarms. The subroutine SPTH3 is a tool with which safeguards designers and analysts can study the relative effects of design changes on the adversary routing problem. In addition to showing how to use SPTH3, this report discusses the methods used to find shortest paths and several implementation details which cause SPTH3 to be extremely efficient
Calibration of neural networks using genetic algorithms, with application to optimal path planning
Smith, Terence R.; Pitney, Gilbert A.; Greenwood, Daniel
1987-01-01
Genetic algorithms (GA) are used to search the synaptic weight space of artificial neural systems (ANS) for weight vectors that optimize some network performance function. GAs do not suffer from some of the architectural constraints involved with other techniques and it is straightforward to incorporate terms into the performance function concerning the metastructure of the ANS. Hence GAs offer a remarkably general approach to calibrating ANS. GAs are applied to the problem of calibrating an ANS that finds optimal paths over a given surface. This problem involves training an ANS on a relatively small set of paths and then examining whether the calibrated ANS is able to find good paths between arbitrary start and end points on the surface.
The Global Optimal Algorithm of Reliable Path Finding Problem Based on Backtracking Method
Directory of Open Access Journals (Sweden)
Liang Shen
2017-01-01
Full Text Available There is a growing interest in finding a global optimal path in transportation networks particularly when the network suffers from unexpected disturbance. This paper studies the problem of finding a global optimal path to guarantee a given probability of arriving on time in a network with uncertainty, in which the travel time is stochastic instead of deterministic. Traditional path finding methods based on least expected travel time cannot capture the network user’s risk-taking behaviors in path finding. To overcome such limitation, the reliable path finding algorithms have been proposed but the convergence of global optimum is seldom addressed in the literature. This paper integrates the K-shortest path algorithm into Backtracking method to propose a new path finding algorithm under uncertainty. The global optimum of the proposed method can be guaranteed. Numerical examples are conducted to demonstrate the correctness and efficiency of the proposed algorithm.
PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks
Directory of Open Access Journals (Sweden)
Mansour Sheikhan
2012-06-01
Full Text Available Mobile ad-hoc network (MANET is a dynamic collection of mobile computers without the need for any existing infrastructure. Nodes in a MANET act as hosts and routers. Designing of robust routing algorithms for MANETs is a challenging task. Disjoint multipath routing protocols address this problem and increase the reliability, security and lifetime of network. However, selecting an optimal multipath is an NP-complete problem. In this paper, Hopfield neural network (HNN which its parameters are optimized by particle swarm optimization (PSO algorithm is proposed as multipath routing algorithm. Link expiration time (LET between each two nodes is used as the link reliability estimation metric. This approach can find either node-disjoint or link-disjoint paths in singlephase route discovery. Simulation results confirm that PSO-HNN routing algorithm has better performance as compared to backup path set selection algorithm (BPSA in terms of the path set reliability and number of paths in the set.
A new technique for accelerating routing information process in ...
African Journals Online (AJOL)
Previous works on the shortest path are limited to sequential and parallel algorithms on general-purpose architectures. Researchers are increasingly interested in hardware's solutions. In this work , we propose an approach for implementing a routing algorithm which is effective than Dijkstra using a FPGA development ...
One Kind of Routing Algorithm Modified in Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Wei Ni Ni
2016-01-01
Full Text Available The wireless sensor networks are the emerging next generation sensor networks, Routing technology is the wireless sensor network communication layer of the core technology. To build reliable paths in wireless sensor networks, we can consider two ways: providing multiple paths utilizing the redundancy to assure the communication reliability or constructing transmission reliability mechanism to assure the reliability of every hop. Braid multipath algorithm and ReInforM routing algorithm are the realizations of these two mechanisms. After the analysis of these two algorithms, this paper proposes a ReInforM routing algorithm based braid multipath routing algorithm.
Directory of Open Access Journals (Sweden)
Mazyar Seraj
2014-06-01
Full Text Available In recent years, many studies have been carried out on how to engage and support students in e-learning environments. Portable devices such as Personal Digital Assistants (PDAs, Tablet PCs, mobile phones and other mobile equipment have been used as parts of electronic learning environments to facilitate learning and teaching for both lecturers and students. However, there is still a dearth of study investigating the effects of small screen interfaces on mobile-based learning environments. This study aims to address two objectives: (i investigate lecturer and student difficulties encountered in teaching-learning process in traditional face-to-face classroom settings, and (ii to explore lecturer and student perceptions about learning the subject through mobile devices. This paper presents the results of a qualitative study using structured interviews to investigate lecturer and student experiences and perceptions on teaching and learning Dijkstra’s shortest path algorithm via mobile devices. The interview insights were then used as inputs to define user requirements for a mobile learning prototype. The findings show that the lecturers and students raised many issues about interactivity and the flexibility of effective learning applications on small screen devices, especially for a technical subject.
Energy Aware Simple Ant Routing Algorithm for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Sohail Jabbar
2015-01-01
Full Text Available Network lifetime is one of the most prominent barriers in deploying wireless sensor networks for large-scale applications because these networks employ sensors with nonrenewable scarce energy resources. Sensor nodes dissipate most of their energy in complex routing mechanisms. To cope with limited energy problem, we present EASARA, an energy aware simple ant routing algorithm based on ant colony optimization. Unlike most algorithms, EASARA strives to avoid low energy routes and optimizes the routing process through selection of least hop count path with more energy. It consists of three phases, that is, route discovery, forwarding node, and route selection. We have improved the route discovery procedure and mainly concentrate on energy efficient forwarding node and route selection, so that the network lifetime can be prolonged. The four possible cases of forwarding node and route selection are presented. The performance of EASARA is validated through simulation. Simulation results demonstrate the performance supremacy of EASARA over contemporary scheme in terms of various metrics.
Distribution of shortest path lengths in a class of node duplication network models
Steinbock, Chanania; Biham, Ofer; Katzav, Eytan
2017-09-01
We present analytical results for the distribution of shortest path lengths (DSPL) in a network growth model which evolves by node duplication (ND). The model captures essential properties of the structure and growth dynamics of social networks, acquaintance networks, and scientific citation networks, where duplication mechanisms play a major role. Starting from an initial seed network, at each time step a random node, referred to as a mother node, is selected for duplication. Its daughter node is added to the network, forming a link to the mother node, and with probability p to each one of its neighbors. The degree distribution of the resulting network turns out to follow a power-law distribution, thus the ND network is a scale-free network. To calculate the DSPL we derive a master equation for the time evolution of the probability Pt(L =ℓ ) , ℓ =1 ,2 ,⋯ , where L is the distance between a pair of nodes and t is the time. Finding an exact analytical solution of the master equation, we obtain a closed form expression for Pt(L =ℓ ) . The mean distance 〈L〉 t and the diameter Δt are found to scale like lnt , namely, the ND network is a small-world network. The variance of the DSPL is also found to scale like lnt . Interestingly, the mean distance and the diameter exhibit properties of a small-world network, rather than the ultrasmall-world network behavior observed in other scale-free networks, in which 〈L〉 t˜lnlnt .
Chen, Lei; Liu, Tao; Zhao, Xian
2018-06-01
The anatomical therapeutic chemical (ATC) classification system is a widely accepted drug classification scheme. This system comprises five levels and includes several classes in each level. Drugs are classified into classes according to their therapeutic effects and characteristics. The first level includes 14 main classes. In this study, we proposed two network-based models to infer novel potential chemicals deemed to belong in the first level of ATC classification. To build these models, two large chemical networks were constructed using the chemical-chemical interaction information retrieved from the Search Tool for Interactions of Chemicals (STITCH). Two classic network algorithms, shortest path (SP) and random walk with restart (RWR) algorithms, were executed on the corresponding network to mine novel chemicals for each ATC class using the validated drugs in a class as seed nodes. Then, the obtained chemicals yielded by these two algorithms were further evaluated by a permutation test and an association test. The former can exclude chemicals produced by the structure of the network, i.e., false positive discoveries. By contrast, the latter identifies the most important chemicals that have strong associations with the ATC class. Comparisons indicated that the two models can provide quite dissimilar results, suggesting that the results yielded by one model can be essential supplements for those obtained by the other model. In addition, several representative inferred chemicals were analyzed to confirm the reliability of the results generated by the two models. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang. Copyright © 2017 Elsevier B.V. All rights reserved.
UMDR: Multi-Path Routing Protocol for Underwater Ad Hoc Networks with Directional Antenna
Yang, Jianmin; Liu, Songzuo; Liu, Qipei; Qiao, Gang
2018-01-01
This paper presents a new routing scheme for underwater ad hoc networks based on directional antennas. Ad hoc networks with directional antennas have become a hot research topic because of space reuse may increase networks capacity. At present, researchers have applied traditional self-organizing routing protocols (such as DSR, AODV) [1] [2] on this type of networks, and the routing scheme is based on the shortest path metric. However, such routing schemes often suffer from long transmission delays and frequent link fragmentation along the intermediate nodes of the selected route. This is caused by a unique feature of directional transmission, often called as “deafness”. In this paper, we take a different approach to explore the advantages of space reuse through multipath routing. This paper introduces the validity of the conventional routing scheme in underwater ad hoc networks with directional antennas, and presents a special design of multipath routing algorithm for directional transmission. The experimental results show a significant performance improvement in throughput and latency.
Reliable Ant Colony Routing Algorithm for Dual-Channel Mobile Ad Hoc Networks
Directory of Open Access Journals (Sweden)
YongQiang Li
2018-01-01
Full Text Available For the problem of poor link reliability caused by high-speed dynamic changes and congestion owing to low network bandwidth in ad hoc networks, an ant colony routing algorithm, based on reliable path under dual-channel condition (DSAR, is proposed. First, dual-channel communication mode is used to improve network bandwidth, and a hierarchical network model is proposed to optimize the dual-layer network. Thus, we reduce network congestion and communication delay. Second, a comprehensive reliable path selection strategy is designed, and the reliable path is selected ahead of time to reduce the probability of routing restart. Finally, the ant colony algorithm is used to improve the adaptability of the routing algorithm to changes of network topology. Simulation results show that DSAR improves the reliability of routing, packet delivery, and throughput.
Directory of Open Access Journals (Sweden)
Ailian Jiang
2018-03-01
Full Text Available Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs. This paper investigates the existing ant colony optimization (ACO-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime.
Directory of Open Access Journals (Sweden)
MANAR Y. KASHMOLA
2012-06-01
Full Text Available The development of hybrid algorithms for solving complex optimization problems focuses on enhancing the strengths and compensating for the weakness of two or more complementary approaches. The goal is to intelligently combine the key elements of these approaches to find superior solutions to solve optimization problems. Optimal routing in communication network is considering a complex optimization problem. In this paper we propose a hybrid Hopfield Neural Network (HNN and Tabu Search (TS algorithm, this algorithm called hybrid HNN-TS algorithm. The paradigm of this hybridization is embedded. We embed the short-term memory and tabu restriction features from TS algorithm in the HNN model. The short-term memory and tabu restriction control the neuron selection process in the HNN model in order to get around the local minima problem and find an optimal solution using the HNN model to solve complex optimization problem. The proposed algorithm is intended to find the optimal path for packet transmission in the network which is fills in the field of routing problem. The optimal path that will be selected is depending on 4-tuples (delay, cost, reliability and capacity. Test results show that the propose algorithm can find path with optimal cost and a reasonable number of iterations. It also shows that the complexity of the network model won’t be a problem since the neuron selection is done heuristically.
Design of the smart home system based on the optimal routing algorithm and ZigBee network.
Directory of Open Access Journals (Sweden)
Dengying Jiang
Full Text Available 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.
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.
Blocked All-Pairs Shortest Paths Algorithm on Intel Xeon Phi KNL Processor: A Case Study
Rucci, Enzo; De Giusti, Armando Eduardo; Naiouf, Marcelo
2017-01-01
Manycores are consolidating in HPC community as a way of improving performance while keeping power efficiency. Knights Landing is the recently released second generation of Intel Xeon Phi architec- ture.While optimizing applications on CPUs, GPUs and first Xeon Phi’s has been largely studied in the last years, the new features in Knights Landing processors require the revision of programming and optimization techniques for these devices. In this work, we selected the Floyd-Warshall algorithm ...
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......) 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...
An Optimization Routing Algorithm for Green Communication in Underground Mines
Directory of Open Access Journals (Sweden)
Heng Xu
2018-06-01
Full Text Available With the long-term dependence of humans on ore-based energy, underground mines are utilized around the world, and underground mining is often dangerous. Therefore, many underground mines have established networks that manage and acquire information from sensor nodes deployed on miners and in other places. Since the power supplies of many mobile sensor nodes are batteries, green communication is an effective approach of reducing the energy consumption of a network and extending its longevity. To reduce the energy consumption of networks, all factors that negatively influence the lifetime should be considered. The degree constraint minimum spanning tree (DCMST is introduced in this study to consider all the heterogeneous factors and assign weights for the next step of the evaluation. Then, a genetic algorithm (GA is introduced to cluster sensor nodes in the network and balance energy consumption according to several heterogeneous factors and routing paths from DCMST. Based on a comparison of the simulation results, the optimization routing algorithm proposed in this study for use in green communication in underground mines can effectively reduce the network energy consumption and extend the lifetimes of networks.
Directory of Open Access Journals (Sweden)
V. E. Podol'skii
2015-01-01
Full Text Available The paper considers the implementing Bellman-Ford and Lee algorithms to find the shortest graph path on a computer system with multiple instruction stream and single data stream (MISD. The MISD computer is a computer that executes commands of arithmetic-logical processing (on the CPU and commands of structures processing (on the structures processor in parallel on a single data stream. Transformation of sequential programs into the MISD programs is a labor intensity process because it requires a stream of the arithmetic-logical processing to be manually separated from that of the structures processing. Algorithms based on the processing of data structures (e.g., algorithms on graphs show high performance on a MISD computer. Bellman-Ford and Lee algorithms for finding the shortest path on a graph are representatives of these algorithms. They are applied to robotics for automatic planning of the robot movement in-situ. Modification of Bellman-Ford and Lee algorithms for finding the shortest graph path in coprocessor MISD mode and the parallel MISD modification of these algorithms were first obtained in this article. Thus, this article continues a series of studies on the transformation of sequential algorithms into MISD ones (Dijkstra and Ford-Fulkerson 's algorithms and has a pronouncedly applied nature. The article also presents the analysis results of Bellman-Ford and Lee algorithms in MISD mode. The paper formulates the basic trends of a technique for parallelization of algorithms into arithmetic-logical processing stream and structures processing stream. Among the key areas for future research, development of the mathematical approach to provide a subsequently formalized and automated process of parallelizing sequential algorithms between the CPU and structures processor is highlighted. Among the mathematical models that can be used in future studies there are graph models of algorithms (e.g., dependency graph of a program. Due to the high
Optimization of routing strategies for data transfer in peer-to-peer networks
International Nuclear Information System (INIS)
Morioka, Atsushi; Igarashi, Akito
2014-01-01
Since peer-to-peer file-sharing systems have become familiar recently, the information traffic in the networks is increasing. Therefore it causes various traffic problems in peer-to-peer networks. In this paper, we model some features of the peer-to-peer networks, and investigate the traffic problems. Peer-to-peer networks have two notable characters. One is that each peer frequently searches for a file and download it from a peer who has the requested file. To decide whether a peer has the requested file or not in modelling of the search and download process, we introduce file-parameter P j , which expresses the amount of files stored in peer j. It is assumed that if P j is large, peer j has many files and can meet other peers' requests with high probability. The other character is that peers leave and join into the network repeatedly. Many researchers address traffic problems of data transfer in computer communication networks. To our knowledge, however, no reports focus on those in peer-to-peer networks whose topology changes with time. For routing paths of data transfer, generally, the shortest paths are used in usual computer networks. In this paper, we introduce a new optimal routing strategy which uses weights of peers to avoid traffic congestion. We find that the new routing strategy is superior to the shortest path strategy in terms of congestion frequency in data transfer
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...
An algorithm for routing optimization in DiffServ-aware MPLS networks
Luigi Atzori; Fabio D'Andreagiovanni; Carlo Mannino; Tatiana Onali
2010-01-01
This paper addresses the constrained-based routing problem in DiffServaware MPLS networks. We consider a dynamic context in which new requests appear over time, asking for reconfigurations of the previous allocation. In the classical approach, a multi-phase heuristic procedure is adopted: the new requests are evaluated considering available bandwidth; if the bandwidth is not sufficient, preemption and rerouting of one or more connections are performed in sequence. As an alternative, we propos...
Quality-of-Service Routing Using Path and Power Aware Techniques in Mobile Ad Hoc Networks
Directory of Open Access Journals (Sweden)
R. Asokan
2008-01-01
Full Text Available Mobile ad hoc network (MANET is a collection of wireless mobile hosts dynamically forming a temporary network without the aid of any existing established infrastructure. Quality of service (QoS is a set of service requirements that needs to be met by the network while transporting a packet stream from a source to its destination. QoS support MANETs is a challenging task due to the dynamic topology and limited resources. The main objective of this paper is to enhance the QoS routing for MANET using temporally ordered routing algorithm (TORA with self-healing and optimized routing techniques (SHORT. SHORT improves routing optimality by monitoring routing paths continuously and redirecting the path whenever a shortcut path is available. In this paper, the performance comparison of TORA and TORA with SHORT has been analyzed using network simulator for various parameters. TORA with SHORT enhances performance of TORA in terms of throughput, packet loss, end-to-end delay, and energy.
A NEW APPROACH ON SHORTEST PATH IN FUZZY ENVIRONMENT
A. Nagoorgani; A. Mumtaj Begam
2010-01-01
This paper introduces a new type of fuzzy shortest path network problem using triangular fuzzy number. To find the smallest edge by the fuzzy distance using graded mean integration representation of generalized fuzzy number for every node. Thus the optimum shortest path for the given problem is obtained.
Capacity Constrained Routing Algorithms for Evacuation Route Planning
National Research Council Canada - National Science Library
Lu, Qingsong; George, Betsy; Shekhar, Shashi
2006-01-01
.... In this paper, we propose a new approach, namely a capacity constrained routing planner which models capacity as a time series and generalizes shortest path algorithms to incorporate capacity constraints...
The Shortest Path Problems in Battery-Electric Vehicle Dispatching with Battery Renewal
Directory of Open Access Journals (Sweden)
Minfang Huang
2016-06-01
Full Text Available Electric vehicles play a key role for developing an eco-sustainable transport system. One critical component of an electric vehicle is its battery, which can be quickly charged or exchanged before it runs out. The problem of electric vehicle dispatching falls into the category of the shortest path problem with resource renewal. In this paper, we study the shortest path problems in (1 electric transit bus scheduling and (2 electric truck routing with time windows. In these applications, a fully-charged battery allows running a limited operational distance, and the battery before depletion needs to be quickly charged or exchanged with a fully-charged one at a battery management facility. The limited distance and battery renewal result in a shortest path problem with resource renewal. We develop a label-correcting algorithm with state space relaxation to find optimal solutions. In the computational experiments, real-world road geometry data are used to generate realistic travel distances, and other types of data are obtained from the real world or randomly generated. The computational results show that the label-correcting algorithm performs very well.
PROPOSAL OF ALGORITHM FOR ROUTE OPTIMIZATION
Directory of Open Access Journals (Sweden)
Robert Ramon de Carvalho Sousa
2016-06-01
Full Text Available This article uses “Six Sigma” methodology for the elaboration of an algorithm for routing problems which is able to obtain more efficient results than those from Clarke and Wright´s (CW algorithm (1964 in situations of random increase of product delivery demands, facing the incapability of service level increase . In some situations, the algorithm proposed obtained more efficient results than the CW algorithm. The key factor was a reduction in the number of mistakes (one way routes and in the level of result variation.
PROPOSAL OF ALGORITHM FOR ROUTE OPTIMIZATION
Robert Ramon de Carvalho Sousa; Abimael de Jesus Barros Costa; Eliezé Bulhões de Carvalho; Adriano de Carvalho Paranaíba; Daylyne Maerla Gomes Lima Sandoval
2016-01-01
This article uses “Six Sigma” methodology for the elaboration of an algorithm for routing problems which is able to obtain more efficient results than those from Clarke and Wright´s (CW) algorithm (1964) in situations of random increase of product delivery demands, facing the incapability of service level increase . In some situations, the algorithm proposed obtained more efficient results than the CW algorithm. The key factor was a reduction in the number of mistakes (on...
Path Diversity Improved Opportunistic Routing for Underwater Sensor Networks.
Bai, Weigang; Wang, Haiyan; He, Ke; Zhao, Ruiqin
2018-04-23
The packets carried along a pre-defined route in underwater sensor networks are very vulnerble. Node mobility or intermittent channel availability easily leads to unreachable routing. Opportunistic routing has been proven to be a promising paradigm to design routing protocols for underwater sensor networks. It takes advantage of the broadcast nature of the wireless medium to combat packet losses and selects potential paths on the fly. Finding an appropriate forwarding candidate set is a key issue in opportunistic routing. Many existing solutions ignore the impact of candidates location distribution on packet forwarding. In this paper, a path diversity improved candidate selection strategy is applied in opportunistic routing to improve packet forwarding efficiency. It not only maximizes the packet forwarding advancements but also takes the candidate’s location distribution into account. Based on this strategy, we propose two effective routing protocols: position improved candidates selection (PICS) and position random candidates selection (PRCS). PICS employs two-hop neighbor information to make routing decisions. PRCS only uses one-hop neighbor information. Simulation results show that both PICS and PRCS can significantly improve network performance when compared with the previous solutions, in terms of packet delivery ratio, average energy consumption and end-to-end delay.
Path Diversity Improved Opportunistic Routing for Underwater Sensor Networks
Directory of Open Access Journals (Sweden)
Weigang Bai
2018-04-01
Full Text Available The packets carried along a pre-defined route in underwater sensor networks are very vulnerble. Node mobility or intermittent channel availability easily leads to unreachable routing. Opportunistic routing has been proven to be a promising paradigm to design routing protocols for underwater sensor networks. It takes advantage of the broadcast nature of the wireless medium to combat packet losses and selects potential paths on the fly. Finding an appropriate forwarding candidate set is a key issue in opportunistic routing. Many existing solutions ignore the impact of candidates location distribution on packet forwarding. In this paper, a path diversity improved candidate selection strategy is applied in opportunistic routing to improve packet forwarding efficiency. It not only maximizes the packet forwarding advancements but also takes the candidate’s location distribution into account. Based on this strategy, we propose two effective routing protocols: position improved candidates selection (PICS and position random candidates selection (PRCS. PICS employs two-hop neighbor information to make routing decisions. PRCS only uses one-hop neighbor information. Simulation results show that both PICS and PRCS can significantly improve network performance when compared with the previous solutions, in terms of packet delivery ratio, average energy consumption and end-to-end delay.
Energy optimization based path selection algorithm for IEEE 802.11s wireless mesh networks
CSIR Research Space (South Africa)
Mhlanga, MM
2011-09-01
Full Text Available It is everyone’s dream to have network connectivity anywhere at all times. This dream can only be realized provided there are feasible solutions that are put in place for the next generation of wireless works. Wireless Mesh Networks (WMNs...
DiversePathsJ: diverse shortest paths for bioimage analysis.
Uhlmann, Virginie; Haubold, Carsten; Hamprecht, Fred A; Unser, Michael
2018-02-01
We introduce a formulation for the general task of finding diverse shortest paths between two end-points. Our approach is not linked to a specific biological problem and can be applied to a large variety of images thanks to its generic implementation as a user-friendly ImageJ/Fiji plugin. It relies on the introduction of additional layers in a Viterbi path graph, which requires slight modifications to the standard Viterbi algorithm rules. This layered graph construction allows for the specification of various constraints imposing diversity between solutions. The software allows obtaining a collection of diverse shortest paths under some user-defined constraints through a convenient and user-friendly interface. It can be used alone or be integrated into larger image analysis pipelines. http://bigwww.epfl.ch/algorithms/diversepathsj. michael.unser@epfl.ch or fred.hamprecht@iwr.uni-heidelberg.de. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.
Directory of Open Access Journals (Sweden)
Nguyen Thanh Long
2015-02-01
Full Text Available MANET (short for Mobile Ad-Hoc Network consists of a set of mobile network nodes, network configuration changes very fast. In content based routing, data is transferred from source node to request nodes is not based on destination addresses. Therefore, it is very flexible and reliable, because source node does not need to know destination nodes. If We can find multiple paths that satisfies bandwidth requirement, split the original message into multiple smaller messages to transmit concurrently on these paths. On destination nodes, combine separated messages into the original message. Hence it can utilize better network resources, causes data transfer rate to be higher, load balancing, failover. Service Oriented Routing is inherited from the model of content based routing (CBR, combined with several advanced techniques such as Multicast, multiple path routing, Genetic algorithm to increase the data rate, and data encryption to ensure information security. Fuzzy logic is a logical field study evaluating the accuracy of the results based on the approximation of the components involved, make decisions based on many factors relative accuracy based on experimental or mathematical proof. This article presents some techniques to support multiple path routing from one network node to a set of nodes with guaranteed quality of service. By using these techniques can decrease the network load, congestion, use network resources efficiently.
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
2017-01-01
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
An Energy Efficient Stable Election-Based Routing Algorithm for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Weiwei Yuan
2013-10-01
Full Text Available Sensor nodes usually have limited energy supply and they are impractical to recharge. How to balance traffic load in sensors in order to increase network lifetime is a very challenging research issue. Many clustering algorithms have been proposed recently for wireless sensor networks (WSNs. However, sensor networks with one fixed sink node often suffer from a hot spots problem since nodes near sinks have more traffic burden to forward during a multi-hop transmission process. The use of mobile sinks has been shown to be an effective technique to enhance network performance features such as latency, energy efficiency, network lifetime, etc. In this paper, a modified Stable Election Protocol (SEP, which employs a mobile sink, has been proposed for WSNs with non-uniform node distribution. The decision of selecting cluster heads by the sink is based on the minimization of the associated additional energy and residual energy at each node. Besides, the cluster head selects the shortest path to reach the sink between the direct approach and the indirect approach with the use of the nearest cluster head. Simulation results demonstrate that our algorithm has better performance than traditional routing algorithms, such as LEACH and SEP.
Gems of combinatorial optimization and graph algorithms
Skutella, Martin; Stiller, Sebastian; Wagner, Dorothea
2015-01-01
Are you looking for new lectures for your course on algorithms, combinatorial optimization, or algorithmic game theory? Maybe you need a convenient source of relevant, current topics for a graduate student or advanced undergraduate student seminar? Or perhaps you just want an enjoyable look at some beautiful mathematical and algorithmic results, ideas, proofs, concepts, and techniques in discrete mathematics and theoretical computer science? Gems of Combinatorial Optimization and Graph Algorithms is a handpicked collection of up-to-date articles, carefully prepared by a select group of international experts, who have contributed some of their most mathematically or algorithmically elegant ideas. Topics include longest tours and Steiner trees in geometric spaces, cartograms, resource buying games, congestion games, selfish routing, revenue equivalence and shortest paths, scheduling, linear structures in graphs, contraction hierarchies, budgeted matching problems, and motifs in networks. This ...
An Adaptive Multi-Objective Particle Swarm Optimization Algorithm for Multi-Robot Path Planning
Directory of Open Access Journals (Sweden)
Nizar Hadi Abbas
2016-07-01
Full Text Available This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In order to evaluate the proposed algorithm in term of finding the best solution, six benchmark test functions are used to make a comparison between AMOPSO and the standard MOPSO. The results show that the AMOPSO has a better ability to get away from local optimums with a quickest convergence than the MOPSO. The simulation results using Matlab 2014a, indicate that this methodology is extremely valuable for every robot in multi-robot framework to discover its own particular proper path from the start to the destination position with minimum distance and time.
Directory of Open Access Journals (Sweden)
Veena Anand
2017-01-01
Full Text Available Wireless Sensor Networks (WSN has the disadvantage of limited and non-rechargeable energy resource in WSN creates a challenge and led to development of various clustering and routing algorithms. The paper proposes an approach for improving network lifetime by using Particle swarm optimization based clustering and Harmony Search based routing in WSN. So in this paper, global optimal cluster head are selected and Gateway nodes are introduced to decrease the energy consumption of the CH while sending aggregated data to the Base station (BS. Next, the harmony search algorithm based Local Search strategy finds best routing path for gateway nodes to the Base Station. Finally, the proposed algorithm is presented.
Effective caching of shortest paths for location-based services
DEFF Research Database (Denmark)
Jensen, Christian S.; Thomsen, Jeppe Rishede; Yiu, Man Lung
2012-01-01
Web search is ubiquitous in our daily lives. Caching has been extensively used to reduce the computation time of the search engine and reduce the network traffic beyond a proxy server. Another form of web search, known as online shortest path search, is popular due to advances in geo...
2010-10-25
Real-time information is important for travelers' routing decisions in uncertain networks by enabling online adaptation to revealed traffic conditions. Usually there are spatial and/or temporal limitations in traveler information. In this research, a...
Bellman Ford algorithm - in Routing Information Protocol (RIP)
Krianto Sulaiman, Oris; Mahmud Siregar, Amir; Nasution, Khairuddin; Haramaini, Tasliyah
2018-04-01
In a large scale network need a routing that can handle a lot number of users, one of the solutions to cope with large scale network is by using a routing protocol, There are 2 types of routing protocol that is static and dynamic, Static routing is manually route input based on network admin, while dynamic routing is automatically route input formed based on existing network. Dynamic routing is efficient used to network extensively because of the input of route automatic formed, Routing Information Protocol (RIP) is one of dynamic routing that uses the bellman-ford algorithm where this algorithm will search for the best path that traversed the network by leveraging the value of each link, so with the bellman-ford algorithm owned by RIP can optimize existing networks.
Mao, Yuxin; Zhu, Ping
2013-01-01
The unreliability and dynamics of mobile wireless sensor networks make it hard to perform end-to-end communications. This paper presents a novel source-initiated on-demand routing mechanism for efficient data transmission in mobile wireless sensor networks. It explores the Thorup-Zwick theory to achieve source-initiated on-demand routing with time efficiency. It is able to find out shortest routing path between source and target in a network and transfer data in linear time. The algorithm is easy to be implemented and performed in resource-constrained mobile wireless sensor networks. We also evaluate the approach by analyzing its cost in detail. It can be seen that the approach is efficient to support data transmission in mobile wireless sensor networks.
Approximate Shortest Homotopic Paths in Weighted Regions
Cheng, Siu-Wing; Jin, Jiongxin; Vigneron, Antoine; Wang, Yajun
2010-01-01
Let P be a path between two points s and t in a polygonal subdivision T with obstacles and weighted regions. Given a relative error tolerance ε ∈(0,1), we present the first algorithm to compute a path between s and t that can be deformed to P
Approximate shortest homotopic paths in weighted regions
Cheng, Siuwing; Jin, Jiongxin; Vigneron, Antoine E.; Wang, Yajun
2012-01-01
A path P between two points s and t in a polygonal subdivision T with obstacles and weighted regions defines a class of paths that can be deformed to P without passing over any obstacle. We present the first algorithm that, given P and a relative
A Method of Forming the Optimal Set of Disjoint Path in Computer Networks
Directory of Open Access Journals (Sweden)
As'ad Mahmoud As'ad ALNASER
2017-04-01
Full Text Available This work provides a short analysis of algorithms of multipath routing. The modified algorithm of formation of the maximum set of not crossed paths taking into account their metrics is offered. Optimization of paths is carried out due to their reconfiguration with adjacent deadlock path. Reconfigurations are realized within the subgraphs including only peaks of the main and an adjacent deadlock path. It allows to reduce the field of formation of an optimum path and time complexity of its formation.
Combinatorial optimization networks and matroids
Lawler, Eugene
2011-01-01
Perceptively written text examines optimization problems that can be formulated in terms of networks and algebraic structures called matroids. Chapters cover shortest paths, network flows, bipartite matching, nonbipartite matching, matroids and the greedy algorithm, matroid intersections, and the matroid parity problems. A suitable text or reference for courses in combinatorial computing and concrete computational complexity in departments of computer science and mathematics.
OPTIMIZATION OF DISJOINTS FOR MINIMIZATION OF FAILURE IN WDM OPTICAL NETWORK
Directory of Open Access Journals (Sweden)
A. Renugadevi
2015-06-01
Full Text Available In an optical network, the fiber optic cable is used for communication between the nodes in a network by passing lights. The main problem in optical network is finding the link disjoints as well as optimal solution for the disjoints. To tolerate a single link failure in the network, the enhanced active path first algorithm is used which computes the re-routed back-up path. The multiple link failure in a network called fibre span disjoint path problem is solved using integer linear programming algorithm. The loop back recovery is used to provide pre-planned recovery of link or node failures in a network which allows dynamic choice of routes over pre-planned directions. Considering reliability in a mesh networks, the reliability algorithm helps to achieve the maximum reliability in two-path protection. It addresses the multiple disjoint failures that arise in a network and discusses the best solution between paths shared nodes or links. The unified algorithm is used to generate the optimal results with minimum cost for multiple link failures. The heuristic algorithm namely maximum arbitrary double-link protection algorithm helps to pre-compute the back-up path for double-link failures. In all the above approaches the shortest optimized path must be improved. To find the best shortest path, link-disjoint lightpath algorithm is designed to compute the disjoint occurred in a network and it also satisfies the wavelength continuity constraint in wavelength division multiplexing. A polynomial time algorithm Wavelength Division Multiplexing – Passive Optical Networking is used to compute the disjoint happen in the network. The overall time efficiency is analyzed and performance is evaluated through simulations.
An Application of Multi-Criteria Shortest Path to a Customizable Hex-Map Environment
2015-03-26
47 Appendix A. Shortest Path Code ( VBA ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Appendix B. Quad Chart...efficient shortest path algorithm into the modeling environment, namely Excel VBA . While various algorithms offer the potential for more efficiency in...graphical interface is extremely intuitive and easily accessible to a user with no prior knowledge of the system. Since the Metz model is based on the
Transmission Scheduling and Routing Algorithms for Delay Tolerant Networks
Dudukovich, Rachel; Raible, Daniel E.
2016-01-01
The challenges of data processing, transmission scheduling and routing within a space network present a multi-criteria optimization problem. Long delays, intermittent connectivity, asymmetric data rates and potentially high error rates make traditional networking approaches unsuitable. The delay tolerant networking architecture and protocols attempt to mitigate many of these issues, yet transmission scheduling is largely manually configured and routes are determined by a static contact routing graph. A high level of variability exists among the requirements and environmental characteristics of different missions, some of which may allow for the use of more opportunistic routing methods. In all cases, resource allocation and constraints must be balanced with the optimization of data throughput and quality of service. Much work has been done researching routing techniques for terrestrial-based challenged networks in an attempt to optimize contact opportunities and resource usage. This paper examines several popular methods to determine their potential applicability to space networks.
Adaptive Bio-Inspired Wireless Network Routing for Planetary Surface Exploration
Alena, Richard I.; Lee, Charles
2004-01-01
Wireless mobile networks suffer connectivity loss when used in a terrain that has hills, and valleys when line of sight is interrupted or range is exceeded. To resolve this problem and achieve acceptable network performance, we have designed an adaptive, configurable, hybrid system to automatically route network packets along the best path between multiple geographically dispersed modules. This is very useful in planetary surface exploration, especially for ad-hoc mobile networks, where computational devices take an active part in creating a network infrastructure, and can actually be used to route data dynamically and even store data for later transmission between networks. Using inspiration from biological systems, this research proposes to use ant trail algorithms with multi-layered information maps (topographic maps, RF coverage maps) to determine the best route through ad-hoc network at real time. The determination of best route is a complex one, and requires research into the appropriate metrics, best method to identify the best path, optimizing traffic capacity, network performance, reliability, processing capabilities and cost. Real ants are capable of finding the shortest path from their nest to a food source without visual sensing through the use of pheromones. They are also able to adapt to changes in the environment using subtle clues. To use ant trail algorithms, we need to define the probability function. The artificial ant is, in this case, a software agent that moves from node to node on a network graph. The function to calculate the fitness (evaluate the better path) includes: length of the network edge, the coverage index, topology graph index, and pheromone trail left behind by other ant agents. Each agent modifies the environment in two different ways: 1) Local trail updating: As the ant moves between nodes it updates the amount of pheromone on the edge; and 2) Global trail updating: When all ants have completed a tour the ant that found the
Eco-sustainable routing in optical networks
DEFF Research Database (Denmark)
Ricciardi, Sergio; Wang, Jiayuan; Palmieri, Francesco
2013-01-01
infrastructures are now widely recognized to play a fundamental role in the emission of green-house gases (GHG) in the at- mosphere, signicantly aecting the environmental sustainability of new evolutions in network architectures as well as technological developments in communication devices. In this paper......, a novel eco-sustainable Routing and Wavelength Assignment (RWA) algorithm, based on shortest-path routing with an adaptive link weighting function relying on an extension of the OSPF-TE protocol to convey carbon footprint information, has been proposed to decrease the network ecological impact while...
Optimal path planning for a mobile robot using cuckoo search algorithm
Mohanty, Prases K.; Parhi, Dayal R.
2016-03-01
The shortest/optimal path planning is essential for efficient operation of autonomous vehicles. In this article, a new nature-inspired meta-heuristic algorithm has been applied for mobile robot path planning in an unknown or partially known environment populated by a variety of static obstacles. This meta-heuristic algorithm is based on the levy flight behaviour and brood parasitic behaviour of cuckoos. A new objective function has been formulated between the robots and the target and obstacles, which satisfied the conditions of obstacle avoidance and target-seeking behaviour of robots present in the terrain. Depending upon the objective function value of each nest (cuckoo) in the swarm, the robot avoids obstacles and proceeds towards the target. The smooth optimal trajectory is framed with this algorithm when the robot reaches its goal. Some simulation and experimental results are presented at the end of the paper to show the effectiveness of the proposed navigational controller.
Structural parcellation of the thalamus using shortest-path tractography
DEFF Research Database (Denmark)
Kasenburg, Niklas; Darkner, Sune; Hahn, Ute
2016-01-01
that parcellation of the thalamus results in p-value maps that are spatially coherent across subjects. Comparing to the state-of-the-art parcellation of Behrens et al. [1], we observe some agreement, but the soft segmentation exhibits better stability for voxels connected to multiple target regions.......We demonstrate how structural parcellation can be implemented using shortest-path tractography, thereby addressing some of the shortcomings of the conventional approaches. In particular, our algorithm quantifies, via p-values, the confidence that a voxel in the parcellated region is connected...... to each cortical target region. Calculation of these statistical measures is derived from a rank-based test on the histogram of tract-based scores from all the shortest paths found between the source voxel and each voxel within the target region. Using data from the Human Connectome Project, we show...
Selective epidemic vaccination under the performant routing algorithms
Bamaarouf, O.; Alweimine, A. Ould Baba; Rachadi, A.; EZ-Zahraouy, H.
2018-04-01
Despite the extensive research on traffic dynamics and epidemic spreading, the effect of the routing algorithms strategies on the traffic-driven epidemic spreading has not received an adequate attention. It is well known that more performant routing algorithm strategies are used to overcome the congestion problem. However, our main result shows unexpectedly that these algorithms favor the virus spreading more than the case where the shortest path based algorithm is used. In this work, we studied the virus spreading in a complex network using the efficient path and the global dynamic routing algorithms as compared to shortest path strategy. Some previous studies have tried to modify the routing rules to limit the virus spreading, but at the expense of reducing the traffic transport efficiency. This work proposed a solution to overcome this drawback by using a selective vaccination procedure instead of a random vaccination used often in the literature. We found that the selective vaccination succeeded in eradicating the virus better than a pure random intervention for the performant routing algorithm strategies.
Kröger, Martin
2005-06-01
program has been tested: UNIX, Linux Program language used: USANSI Fortran 77 and Fortran 90 Memory required to execute with typical data: 1 MByte No. of lines in distributed program, including test data, etc.: 10 660 No. of bytes in distributed program, including test data, etc.: 119 551 Distribution formet:tar.gz Nature of physical problem: The problem is to obtain primitive paths substantiating a shortest multiple disconnected path (SP) for a given polymer configuration (chains of particles, with or without additional single particles as obstacles for the 2D case). Primitive paths are here defined as in [M. Rubinstein, E. Helfand, J. Chem. Phys. 82 (1985) 2477; R. Everaers, S.K. Sukumaran, G.S. Grest, C. Svaneborg, A. Sivasubramanian, K. Kremer, Science 303 (2004) 823] as the shortest line (path) respecting 'topological' constraints (from neighboring polymers or point obstacles) between ends of polymers. There is a unique solution for the 2D case. For the 3D case it is unique if we construct a primitive path of a single chain embedded within fixed line obstacles [J.S.B. Mitchell, Geometric shortest paths and network optimization, in: J.-R. Sack, J. Urrutia (Eds.), Handbook of Computational Geometry, Elsevier, Amsterdam, 2000, pp. 633-701]. For a large 3D configuration made of several chains, short is meant to be the Euclidean shortest multiple disconnected path (SP) where primitive paths are constructed for all chains simultaneously. While the latter problem, in general, does not possess a unique solution, the algorithm must return a locally optimal solution, robust against minor displacements of the disconnected path and chain re-labeling. The problem is solved if the number of kinks (or entanglements Z), explicitly deduced from the SP, is quite insensitive to the exact conformation of the SP which allows to estimate Z with a small error. Efficient method of solution: Primitive paths are constructed from the given polymer configuration (a non-shortest multiple
Approximate Shortest Homotopic Paths in Weighted Regions
Cheng, Siu-Wing
2010-01-01
Let P be a path between two points s and t in a polygonal subdivision T with obstacles and weighted regions. Given a relative error tolerance ε ∈(0,1), we present the first algorithm to compute a path between s and t that can be deformed to P without passing over any obstacle and the path cost is within a factor 1 + ε of the optimum. The running time is O(h 3/ε2 kn polylog(k, n, 1/ε)), where k is the number of segments in P and h and n are the numbers of obstacles and vertices in T, respectively. The constant in the running time of our algorithm depends on some geometric parameters and the ratio of the maximum region weight to the minimum region weight. © 2010 Springer-Verlag.
Approximate shortest homotopic paths in weighted regions
Cheng, Siuwing
2012-02-01
A path P between two points s and t in a polygonal subdivision T with obstacles and weighted regions defines a class of paths that can be deformed to P without passing over any obstacle. We present the first algorithm that, given P and a relative error tolerance ε (0, 1), computes a path from this class with cost at most 1 + ε times the optimum. The running time is O(h 3/ε 2kn polylog (k,n,1/ε)), where k is the number of segments in P and h and n are the numbers of obstacles and vertices in T, respectively. The constant in the running time of our algorithm depends on some geometric parameters and the ratio of the maximum region weight to the minimum region weight. © 2012 World Scientific Publishing Company.
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.
Directory of Open Access Journals (Sweden)
Juan Carlos Montoya M.
2008-06-01
Full Text Available Multicast juega un papel muy importante para soportar una nueva generación de aplicaciones. En la actualidad y por diferentes razones, técnicas y no técnicas, multicast IP no ha sido totalmente adoptado en Internet. Durante los últimos a˜nos, un área de investigación activa es la de implementar este tipo de tráfico desde la perspectiva del nivel de aplicación, donde la funcionalidad de multicast no es responsabilidad de los enrutadores sino de los hosts, a lo que se le conoce como Multicast Overlay Network (MON. En este artículo se plantea el enrutamiento en MON como un problema de Optimización Multiobjetivo (MOP donde se optimizan dos funciones: 1 el retardo total extremo a extremo del árbol multicast, y 2 la máxima utilización de los enlaces. La optimización simultánea de estas dos funciones es un problema NP completo y para resolverlo se propone utilizar Algoritmos Evolutivos Multiobjetivos (MOEA, específicamente NSGAIMulticast plays an important role in supporting a new generation of applications. At present and for different reasons, technical and non–technical, multicast IP hasn’t yet been totally adopted for Internet. During recent years, an active area of research is that of implementing this kind of traffic in the application layer where the multicast functionality isn´t a responsibility of the routers but that of the hosts, which we know as Multicast Overlay Networks (MON. In this article, routing in an MON is put forward as a multiobjective optimization problem (MOP where two functions are optimized: 1 the total end to end delay of the multicast tree and 2 the maximum link utilization. The simultaneous optimization of these two functions is an NP–Complete problem and to solve this we suggest using Multiobjective Evolutionary Algorithms (MOEA, specifically NSGA–II.
Energy-Aware Routing Optimization in Dynamic GMPLS Controlled Optical Networks
DEFF Research Database (Denmark)
Wang, Jiayuan; Ricciardi, Sergio; Fagertun, Anna Manolova
2012-01-01
In this paper, routing optimizations based on energy sources are proposed in dynamic GMPLS controlled optical networks. The influences of re-routing and load balancing factors on the algorithm are evaluated, with a focus on different re-routing thresholds. Results from dynamic network simulations...
Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming
2016-07-14
Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle's position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.
Chu, Xiaowen; Li, Bo; Chlamtac, Imrich
2002-07-01
Sparse wavelength conversion and appropriate routing and wavelength assignment (RWA) algorithms are the two key factors in improving the blocking performance in wavelength-routed all-optical networks. It has been shown that the optimal placement of a limited number of wavelength converters in an arbitrary mesh network is an NP complete problem. There have been various heuristic algorithms proposed in the literature, in which most of them assume that a static routing and random wavelength assignment RWA algorithm is employed. However, the existing work shows that fixed-alternate routing and dynamic routing RWA algorithms can achieve much better blocking performance. Our study in this paper further demonstrates that the wavelength converter placement and RWA algorithms are closely related in the sense that a well designed wavelength converter placement mechanism for a particular RWA algorithm might not work well with a different RWA algorithm. Therefore, the wavelength converter placement and the RWA have to be considered jointly. The objective of this paper is to investigate the wavelength converter placement problem under fixed-alternate routing algorithm and least-loaded routing algorithm. Under the fixed-alternate routing algorithm, we propose a heuristic algorithm called Minimum Blocking Probability First (MBPF) algorithm for wavelength converter placement. Under the least-loaded routing algorithm, we propose a heuristic converter placement algorithm called Weighted Maximum Segment Length (WMSL) algorithm. The objective of the converter placement algorithm is to minimize the overall blocking probability. Extensive simulation studies have been carried out over three typical mesh networks, including the 14-node NSFNET, 19-node EON and 38-node CTNET. We observe that the proposed algorithms not only outperform existing wavelength converter placement algorithms by a large margin, but they also can achieve almost the same performance comparing with full wavelength
International Nuclear Information System (INIS)
Cox, R.G.
1984-01-01
Much controversy surrounds government regulation of routing and scheduling of Hazardous Materials Transportation (HMT). Increases in operating costs must be balanced against expected benefits from local HMT bans and curfews when promulgating or preempting HMT regulations. Algorithmic approaches for evaluating HMT routing and scheduling regulatory policy are described. A review of current US HMT regulatory policy is presented to provide a context for the analysis. Next, a multiobjective shortest path algorithm to find the set of efficient routes under conflicting objectives is presented. This algorithm generates all efficient routes under any partial ordering in a single pass through the network. Also, scheduling algorithms are presented to estimate the travel time delay due to HMT curfews along a route. Algorithms are presented assuming either deterministic or stochastic travel times between curfew cities and also possible rerouting to avoid such cities. These algorithms are applied to the case study of US highway transport of spent nuclear fuel from reactors to permanent repositories. Two data sets were used. One data set included the US Interstate Highway System (IHS) network with reactor locations, possible repository sites, and 150 heavily populated areas (HPAs). The other data set contained estimates of the population residing with 0.5 miles of the IHS and the Eastern US. Curfew delay is dramatically reduced by optimally scheduling departure times unless inter-HPA travel times are highly uncertain. Rerouting shipments to avoid HPAs is a less efficient approach to reducing delay
Simulated Annealing Technique for Routing in a Rectangular Mesh Network
Directory of Open Access Journals (Sweden)
Noraziah Adzhar
2014-01-01
Full Text Available In the process of automatic design for printed circuit boards (PCBs, the phase following cell placement is routing. On the other hand, routing process is a notoriously difficult problem, and even the simplest routing problem which consists of a set of two-pin nets is known to be NP-complete. In this research, our routing region is first tessellated into a uniform Nx×Ny array of square cells. The ultimate goal for a routing problem is to achieve complete automatic routing with minimal need for any manual intervention. Therefore, shortest path for all connections needs to be established. While classical Dijkstra’s algorithm guarantees to find shortest path for a single net, each routed net will form obstacles for later paths. This will add complexities to route later nets and make its routing longer than the optimal path or sometimes impossible to complete. Today’s sequential routing often applies heuristic method to further refine the solution. Through this process, all nets will be rerouted in different order to improve the quality of routing. Because of this, we are motivated to apply simulated annealing, one of the metaheuristic methods to our routing model to produce better candidates of sequence.
Intelligent QoS routing algorithm based on improved AODV protocol for Ad Hoc networks
Huibin, Liu; Jun, Zhang
2016-04-01
Mobile Ad Hoc Networks were playing an increasingly important part in disaster reliefs, military battlefields and scientific explorations. However, networks routing difficulties are more and more outstanding due to inherent structures. This paper proposed an improved cuckoo searching-based Ad hoc On-Demand Distance Vector Routing protocol (CSAODV). It elaborately designs the calculation methods of optimal routing algorithm used by protocol and transmission mechanism of communication-package. In calculation of optimal routing algorithm by CS Algorithm, by increasing QoS constraint, the found optimal routing algorithm can conform to the requirements of specified bandwidth and time delay, and a certain balance can be obtained among computation spending, bandwidth and time delay. Take advantage of NS2 simulation software to take performance test on protocol in three circumstances and validate the feasibility and validity of CSAODV protocol. In results, CSAODV routing protocol is more adapt to the change of network topological structure than AODV protocol, which improves package delivery fraction of protocol effectively, reduce the transmission time delay of network, reduce the extra burden to network brought by controlling information, and improve the routing efficiency of network.
Multi-objective evacuation routing optimization for toxic cloud releases
International Nuclear Information System (INIS)
Gai, Wen-mei; Deng, Yun-feng; Jiang, Zhong-an; Li, Jing; Du, Yan
2017-01-01
This paper develops a model for assessing the risks associated with the evacuation process in response to potential chemical accidents, based on which a multi-objective evacuation routing model for toxic cloud releases is proposed taking into account that the travel speed on each arc will be affected by disaster extension. The objectives of the evacuation routing model are to minimize travel time and individual evacuation risk along a path respectively. Two heuristic algorithms are proposed to solve the multi-objective evacuation routing model. Simulation results show the effectiveness and feasibility of the model and algorithms presented in this paper. And, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency route selection in other cases (fires, nuclear accidents). - Highlights: • A model for assessing and visualizing the risks is developed. • A multi-objective evacuation routing model is proposed for toxic cloud releases. • A modified Dijkstra algorithm is designed to obtain an solution of the model. • Two heuristic algorithms have been developed as the optimization tool.
Directory of Open Access Journals (Sweden)
Jing Yang
2010-05-01
Full Text Available For monitoring burst events in a kind of reactive wireless sensor networks (WSNs, a multipath routing protocol (MRP based on dynamic clustering and ant colony optimization (ACO is proposed.. Such an approach can maximize the network lifetime and reduce the energy consumption. An important attribute of WSNs is their limited power supply, and therefore some metrics (such as energy consumption of communication among nodes, residual energy, path length were considered as very important criteria while designing routing in the MRP. Firstly, a cluster head (CH is selected among nodes located in the event area according to some parameters, such as residual energy. Secondly, an improved ACO algorithm is applied in the search for multiple paths between the CH and sink node. Finally, the CH dynamically chooses a route to transmit data with a probability that depends on many path metrics, such as energy consumption. The simulation results show that MRP can prolong the network lifetime, as well as balance of energy consumption among nodes and reduce the average energy consumption effectively.
High throughput route selection in multi-rate wireless mesh networks
Institute of Scientific and Technical Information of China (English)
WEI Yi-fei; GUO Xiang-li; SONG Mei; SONG Jun-de
2008-01-01
Most existing Ad-hoc routing protocols use the shortest path algorithm with a hop count metric to select paths. It is appropriate in single-rate wireless networks, but has a tendency to select paths containing long-distance links that have low data rates and reduced reliability in multi-rate networks. This article introduces a high throughput routing algorithm utilizing the multi-rate capability and some mesh characteristics in wireless fidelity (WiFi) mesh networks. It uses the medium access control (MAC) transmission time as the routing metric, which is estimated by the information passed up from the physical layer. When the proposed algorithm is adopted, the Ad-hoc on-demand distance vector (AODV) routing can be improved as high throughput AODV (HT-AODV). Simulation results show that HT-AODV is capable of establishing a route that has high data-rate, short end-to-end delay and great network throughput.
Secure Multicast Routing Algorithm for Wireless Mesh Networks
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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.
Wu, Shaobo; Chou, Wusheng; Niu, Jianwei; Guizani, Mohsen
2018-01-01
Wireless sensor networks (WSNs) involve more mobile elements with their widespread development in industries. Exploiting mobility present in WSNs for data collection can effectively improve the network performance. However, when the sink (i.e., data collector) path is fixed and the movement is uncontrollable, existing schemes fail to guarantee delay requirements while achieving high energy efficiency. This paper proposes a delay-aware energy-efficient routing algorithm for WSNs with a path-fixed mobile sink, named DERM, which can strike a desirable balance between the delivery latency and energy conservation. We characterize the object of DERM as realizing the energy-optimal anycast to time-varying destination regions, and introduce a location-based forwarding technique tailored for this problem. To reduce the control overhead, a lightweight sink location calibration method is devised, which cooperates with the rough estimation based on the mobility pattern to determine the sink location. We also design a fault-tolerant mechanism called track routing to tackle location errors for ensuring reliable and on-time data delivery. We comprehensively evaluate DERM by comparing it with two canonical routing schemes and a baseline solution presented in this work. Extensive evaluation results demonstrate that DERM can provide considerable energy savings while meeting the delay constraint and maintaining a high delivery ratio. PMID:29562628
Wu, Shaobo; Chou, Wusheng; Niu, Jianwei; Guizani, Mohsen
2018-03-18
Wireless sensor networks (WSNs) involve more mobile elements with their widespread development in industries. Exploiting mobility present in WSNs for data collection can effectively improve the network performance. However, when the sink (i.e., data collector) path is fixed and the movement is uncontrollable, existing schemes fail to guarantee delay requirements while achieving high energy efficiency. This paper proposes a delay-aware energy-efficient routing algorithm for WSNs with a path-fixed mobile sink, named DERM, which can strike a desirable balance between the delivery latency and energy conservation. We characterize the object of DERM as realizing the energy-optimal anycast to time-varying destination regions, and introduce a location-based forwarding technique tailored for this problem. To reduce the control overhead, a lightweight sink location calibration method is devised, which cooperates with the rough estimation based on the mobility pattern to determine the sink location. We also design a fault-tolerant mechanism called track routing to tackle location errors for ensuring reliable and on-time data delivery. We comprehensively evaluate DERM by comparing it with two canonical routing schemes and a baseline solution presented in this work. Extensive evaluation results demonstrate that DERM can provide considerable energy savings while meeting the delay constraint and maintaining a high delivery ratio.
Directory of Open Access Journals (Sweden)
Shaobo Wu
2018-03-01
Full Text Available Wireless sensor networks (WSNs involve more mobile elements with their widespread development in industries. Exploiting mobility present in WSNs for data collection can effectively improve the network performance. However, when the sink (i.e., data collector path is fixed and the movement is uncontrollable, existing schemes fail to guarantee delay requirements while achieving high energy efficiency. This paper proposes a delay-aware energy-efficient routing algorithm for WSNs with a path-fixed mobile sink, named DERM, which can strike a desirable balance between the delivery latency and energy conservation. We characterize the object of DERM as realizing the energy-optimal anycast to time-varying destination regions, and introduce a location-based forwarding technique tailored for this problem. To reduce the control overhead, a lightweight sink location calibration method is devised, which cooperates with the rough estimation based on the mobility pattern to determine the sink location. We also design a fault-tolerant mechanism called track routing to tackle location errors for ensuring reliable and on-time data delivery. We comprehensively evaluate DERM by comparing it with two canonical routing schemes and a baseline solution presented in this work. Extensive evaluation results demonstrate that DERM can provide considerable energy savings while meeting the delay constraint and maintaining a high delivery ratio.
Robinson, Y Harold; Rajaram, M
2015-01-01
Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique.
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.
Special cases of the quadratic shortest path problem
Sotirov, Renata; Hu, Hao
2017-01-01
The quadratic shortest path problem (QSPP) is the problem of finding a path with prespecified start vertex s and end vertex t in a digraph such that the sum of weights of arcs and the sum of interaction costs over all pairs of arcs on the path is minimized. We first consider a variant of the QSPP
Prograph Based Analysis of Single Source Shortest Path Problem with Few Distinct Positive Lengths
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B. Bhowmik
2011-08-01
Full Text Available In this paper we propose an experimental study model S3P2 of a fast fully dynamic programming algorithm design technique in finite directed graphs with few distinct nonnegative real edge weights. The Bellman-Ford’s approach for shortest path problems has come out in various implementations. In this paper the approach once again is re-investigated with adjacency matrix selection in associate least running time. The model tests proposed algorithm against arbitrarily but positive valued weighted digraphs introducing notion of Prograph that speeds up finding the shortest path over previous implementations. Our experiments have established abstract results with the intention that the proposed algorithm can consistently dominate other existing algorithms for Single Source Shortest Path Problems. A comparison study is also shown among Dijkstra’s algorithm, Bellman-Ford algorithm, and our algorithm.
Determination of Optimal Flow Paths for Safety Injection According to Accident Conditions
Energy Technology Data Exchange (ETDEWEB)
Yoo, Kwae Hwan; Kim, Ju Hyun; Kim, Dong Yeong; Na, Man Gyun [Chosun Univ., Gwangju (Korea, Republic of); Hur, Seop; Kim, Changhwoi [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2014-05-15
In case severe accidents happen, major safety parameters of nuclear reactors are rapidly changed. Therefore, operators are unable to respond appropriately. This situation causes the human error of operators that led to serious accidents at Chernobyl. In this study, we aimed to develop an algorithm that can be used to select the optimal flow path for cold shutdown in serious accidents, and to recover an NPP quickly and efficiently from the severe accidents. In order to select the optimal flow path, we applied a Dijkstra algorithm. The Dijkstra algorithm is used to find the path of minimum total length between two given nodes and needs a weight (or length) matrix. In this study, the weight between nodes was calculated from frictional and minor losses inside pipes. That is, the optimal flow path is found so that the pressure drop between a starting node (water source) and a destination node (position that cooling water is injected) is minimized. In case a severe accident has happened, if we inject cooling water through the optimized flow path, then the nuclear reactor will be safely and effectively returned into the cold shutdown state. In this study, we have analyzed the optimal flow paths for safety injection as a preliminary study for developing an accident recovery system. After analyzing the optimal flow path using the Dijkstra algorithm, and the optimal flow paths were selected by calculating the head loss according to path conditions.
Jing Chen
2015-01-01
This study takes the concept of food logistics distribution as the breakthrough point, by means of the aim of optimization of food logistics distribution routes and analysis of the optimization model of food logistics route, as well as the interpretation of the genetic algorithm, it discusses the optimization of food logistics distribution route based on genetic and cluster scheme algorithm.
Directory of Open Access Journals (Sweden)
Jean Chamberlain Chedjou
2015-01-01
Full Text Available This paper develops a flexible analytical concept for robust shortest path detection in dynamically reconfigurable graphs. The concept is expressed by a mathematical model representing the shortest path problem solver. The proposed mathematical model is characterized by three fundamental parameters expressing (a the graph topology (through the “incidence matrix”, (b the edge weights (with dynamic external weights’ setting capability, and (c the dynamic reconfigurability through external input(s of the source-destination nodes pair. In order to demonstrate the universality of the developed concept, a general algorithm is proposed to determine the three fundamental parameters (of the mathematical model developed for all types of graphs regardless of their topology, magnitude, and size. It is demonstrated that the main advantage of the developed concept is that arc costs, the origin-destination pair setting, and the graph topology are dynamically provided by external commands, which are inputs of the shortest path solver model. This enables high flexibility and full reconfigurability of the developed concept, without any retraining need. To validate the concept developed, benchmarking is performed leading to a comparison of its performance with the performances of two well-known concepts based on neural networks.
Smith, K; Abasolo, Daniel Emilio; Escudero, J
2016-01-01
The Cluster-Span Threshold (CST) is a recently introduced unbiased threshold for functional connectivity networks. This binarisation technique offers a natural trade-off of sparsity and density of information by balancing the ratio of closed to open triples in the network topology. Here we present findings comparing it with the Union of Shortest Paths (USP), another recently proposed objective method. We analyse standard network metrics of binarised networks for sensitivity to clinical Alzhei...
Yu, W.; Ai, T.
2014-11-01
Accessibility analysis usually requires special models of spatial location analysis based on some geometric constructions, such as Voronoi diagram (abbreviated to VD). There are many achievements in classic Voronoi model research, however suffering from the following limitations for location-based services (LBS) applications. (1) It is difficult to objectively reflect the actual service areas of facilities by using traditional planar VDs, because human activities in LBS are usually constrained only to the network portion of the planar space. (2) Although some researchers have adopted network distance to construct VDs, their approaches are used in a static environment, where unrealistic measures of shortest path distance based on assumptions about constant travel speeds through the network were often used. (3) Due to the computational complexity of the shortest-path distance calculating, previous researches tend to be very time consuming, especially for large datasets and if multiple runs are required. To solve the above problems, a novel algorithm is developed in this paper. We apply network-based quadrat system and 1-D sequential expansion to find the corresponding subnetwork for each focus. The idea is inspired by the natural phenomenon that water flow extends along certain linear channels until meets others or arrives at the end of route. In order to accommodate the changes in traffic conditions, the length of network-quadrat is set upon the traffic condition of the corresponding street. The method has the advantage over Dijkstra's algorithm in that the time cost is avoided, and replaced with a linear time operation.
The Resource constrained shortest path problem implemented in a lazy functional language
Hartel, Pieter H.; Glaser, Hugh
The resource constrained shortest path problem is an NP-hard problem for which many ingenious algorithms have been developed. These algorithms are usually implemented in Fortran or another imperative programming language. We have implemented some of the simpler algorithms in a lazy functional
An Optical Multicast Routing with Minimal Network Coding Operations in WDM Networks
Directory of Open Access Journals (Sweden)
Huanlin Liu
2014-01-01
Full Text Available Network coding can improve the optical multicast routing performance in terms of network throughput, bandwidth utilization, and traffic load balance. But network coding needs high encoding operations costs in all-optical WDM networks due to shortage of optical RAM. In the paper, the network coding operation is defined to evaluate the number of network coding operation cost in the paper. An optical multicast routing algorithm based on minimal number of network coding operations is proposed to improve the multicast capacity. Two heuristic criteria are designed to establish the multicast routing with low network coding cost and high multicast capacity. One is to select one path from the former K shortest paths with the least probability of dropping the multicast maximal capacity. The other is to select the path with lowest potential coding operations with the highest link shared degree among the multiple wavelength disjoint paths cluster from source to each destination. Comparing with the other multicast routing based on network coding, simulation results show that the proposed multicast routing algorithm can effectively reduce the times of network coding operations, can improve the probability of reaching multicast maximal capacity, and can keep the less multicast routing link cost for optical WDM networks.
External Data Structures for Shortest Path Queries on Planar Digraphs
DEFF Research Database (Denmark)
Arge, Lars; Toma, Laura
2005-01-01
In this paper we present space-query trade-offs for external memory data structures that answer shortest path queries on planar directed graphs. For any S = Ω(N 1 + ε) and S = O(N2/B), our main result is a family of structures that use S space and answer queries in O(N2/ S B) I/Os, thus obtaining...... optimal space-query product O(N2/B). An S space structure can be constructed in O(√S · sort(N)) I/Os, where sort(N) is the number of I/Os needed to sort N elements, B is the disk block size, and N is the size of the graph....
A dynamic routing strategy with limited buffer on scale-free network
Wang, Yufei; Liu, Feng
2016-04-01
In this paper, we propose an integrated routing strategy based on global static topology information and local dynamic data packet queue lengths to improve the transmission efficiency of scale-free networks. The proposed routing strategy is a combination of a global static routing strategy (based on the shortest path algorithm) and local dynamic queue length management, in which, instead of using an infinite buffer, the queue length of each node i in the proposed routing strategy is limited by a critical queue length Qic. When the network traffic is lower and the queue length of each node i is shorter than its critical queue length Qic, it forwards packets according to the global routing table. With increasing network traffic, when the buffers of the nodes with higher degree are full, they do not receive packets due to their limited buffers and the packets have to be delivered to the nodes with lower degree. The global static routing strategy can shorten the transmission time that it takes a packet to reach its destination, and the local limited queue length can balance the network traffic. The optimal critical queue lengths of nodes have been analysed. Simulation results show that the proposed routing strategy can get better performance than that of the global static strategy based on topology, and almost the same performance as that of the global dynamic routing strategy with less complexity.
A study of routing algorithms for SCI-Based multistage networks
International Nuclear Information System (INIS)
Wu Bin; Kristiansen, E.; Skaali, B.; Bogaerts, A.; )
1994-03-01
The report deals with a particular class of multistage Scalable Coherent Interface (SCI) network systems and two important routing algorithms, namely self-routing and table-look up routing. The effect of routing delay on system performance is investigated by simulations. Adaptive routing and deadlock-free routing are studied. 8 refs., 11 figs., 1 tab
Dynamic route guidance algorithm based algorithm based on artificial immune system
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
To improve the performance of the K-shortest paths search in intelligent traffic guidance systems,this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the memphor mechanism of vertebrate immune systems.This algorithm,applied to the urban traffic network model established by the node-expanding method,can expediently realize K-shortest paths search in the urban traffic guidance systems.Because of the immune memory and global parallel search ability from artificial immune systems,K shortest paths can be found without any repeat,which indicates evidently the superiority of the algorithm to the conventional ones.Not only does it perform a better parallelism,the algorithm also prevents premature phenomenon that often occurs in genetic algorithms.Thus,it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications.A case study verifies the efficiency and the practicability of the algorithm aforementioned.
Tien, Nguyen Xuan; Kim, Semog; Rhee, Jong Myung; Park, Sang Yoon
2017-07-25
Fault tolerance has long been a major concern for sensor communications in fault-tolerant cyber physical systems (CPSs). Network failure problems often occur in wireless sensor networks (WSNs) due to various factors such as the insufficient power of sensor nodes, the dislocation of sensor nodes, the unstable state of wireless links, and unpredictable environmental interference. Fault tolerance is thus one of the key requirements for data communications in WSN applications. This paper proposes a novel path redundancy-based algorithm, called dual separate paths (DSP), that provides fault-tolerant communication with the improvement of the network traffic performance for WSN applications, such as fault-tolerant CPSs. The proposed DSP algorithm establishes two separate paths between a source and a destination in a network based on the network topology information. These paths are node-disjoint paths and have optimal path distances. Unicast frames are delivered from the source to the destination in the network through the dual paths, providing fault-tolerant communication and reducing redundant unicast traffic for the network. The DSP algorithm can be applied to wired and wireless networks, such as WSNs, to provide seamless fault-tolerant communication for mission-critical and life-critical applications such as fault-tolerant CPSs. The analyzed and simulated results show that the DSP-based approach not only provides fault-tolerant communication, but also improves network traffic performance. For the case study in this paper, when the DSP algorithm was applied to high-availability seamless redundancy (HSR) networks, the proposed DSP-based approach reduced the network traffic by 80% to 88% compared with the standard HSR protocol, thus improving network traffic performance.
An Optimal Path Computation Architecture for the Cloud-Network on Software-Defined Networking
Directory of Open Access Journals (Sweden)
Hyunhun Cho
2015-05-01
Full Text Available Legacy networks do not open the precise information of the network domain because of scalability, management and commercial reasons, and it is very hard to compute an optimal path to the destination. According to today’s ICT environment change, in order to meet the new network requirements, the concept of software-defined networking (SDN has been developed as a technological alternative to overcome the limitations of the legacy network structure and to introduce innovative concepts. The purpose of this paper is to propose the application that calculates the optimal paths for general data transmission and real-time audio/video transmission, which consist of the major services of the National Research & Education Network (NREN in the SDN environment. The proposed SDN routing computation (SRC application is designed and applied in a multi-domain network for the efficient use of resources, selection of the optimal path between the multi-domains and optimal establishment of end-to-end connections.
Path searching in switching networks using cellular algorithm
Energy Technology Data Exchange (ETDEWEB)
Koczy, L T; Langer, J; Legendi, T
1981-01-01
After a survey of the important statements in the paper A Mathematical Model of Path Searching in General Type Switching Networks (see IBID., vol.25, no.1, p.31-43, 1981) the authors consider the possible implementation for cellular automata of the algorithm introduced there. The cellular field used consists of 5 neighbour 8 state cells. Running times required by a traditional serial processor and by the cellular field, respectively, are compared. By parallel processing this running time can be reduced. 5 references.
Totally opportunistic routing algorithm (TORA) for underwater wireless sensor network.
Rahman, Ziaur; Hashim, Fazirulhisyam; Rasid, Mohd Fadlee A; Othman, Mohamed
2018-01-01
Underwater Wireless Sensor Network (UWSN) has emerged as promising networking techniques to monitor and explore oceans. Research on acoustic communication has been conducted for decades, but had focused mostly on issues related to physical layer such as high latency, low bandwidth, and high bit error. However, data gathering process is still severely limited in UWSN due to channel impairment. One way to improve data collection in UWSN is the design of routing protocol. Opportunistic Routing (OR) is an emerging technique that has the ability to improve the performance of wireless network, notably acoustic network. In this paper, we propose an anycast, geographical and totally opportunistic routing algorithm for UWSN, called TORA. Our proposed scheme is designed to avoid horizontal transmission, reduce end to end delay, overcome the problem of void nodes and maximize throughput and energy efficiency. We use TOA (Time of Arrival) and range based equation to localize nodes recursively within a network. Once nodes are localized, their location coordinates and residual energy are used as a matrix to select the best available forwarder. All data packets may or may not be acknowledged based on the status of sender and receiver. Thus, the number of acknowledgments for a particular data packet may vary from zero to 2-hop. Extensive simulations were performed to evaluate the performance of the proposed scheme for high network traffic load under very sparse and very dense network scenarios. Simulation results show that TORA significantly improves the network performance when compared to some relevant existing routing protocols, such as VBF, HHVBF, VAPR, and H2DAB, for energy consumption, packet delivery ratio, average end-to-end delay, average hop-count and propagation deviation factor. TORA reduces energy consumption by an average of 35% of VBF, 40% of HH-VBF, 15% of VAPR, and 29% of H2DAB, whereas the packet delivery ratio has been improved by an average of 43% of VBF, 26
Totally opportunistic routing algorithm (TORA) for underwater wireless sensor network
Hashim, Fazirulhisyam; Rasid, Mohd Fadlee A.; Othman, Mohamed
2018-01-01
Underwater Wireless Sensor Network (UWSN) has emerged as promising networking techniques to monitor and explore oceans. Research on acoustic communication has been conducted for decades, but had focused mostly on issues related to physical layer such as high latency, low bandwidth, and high bit error. However, data gathering process is still severely limited in UWSN due to channel impairment. One way to improve data collection in UWSN is the design of routing protocol. Opportunistic Routing (OR) is an emerging technique that has the ability to improve the performance of wireless network, notably acoustic network. In this paper, we propose an anycast, geographical and totally opportunistic routing algorithm for UWSN, called TORA. Our proposed scheme is designed to avoid horizontal transmission, reduce end to end delay, overcome the problem of void nodes and maximize throughput and energy efficiency. We use TOA (Time of Arrival) and range based equation to localize nodes recursively within a network. Once nodes are localized, their location coordinates and residual energy are used as a matrix to select the best available forwarder. All data packets may or may not be acknowledged based on the status of sender and receiver. Thus, the number of acknowledgments for a particular data packet may vary from zero to 2-hop. Extensive simulations were performed to evaluate the performance of the proposed scheme for high network traffic load under very sparse and very dense network scenarios. Simulation results show that TORA significantly improves the network performance when compared to some relevant existing routing protocols, such as VBF, HHVBF, VAPR, and H2DAB, for energy consumption, packet delivery ratio, average end-to-end delay, average hop-count and propagation deviation factor. TORA reduces energy consumption by an average of 35% of VBF, 40% of HH-VBF, 15% of VAPR, and 29% of H2DAB, whereas the packet delivery ratio has been improved by an average of 43% of VBF, 26
Welding Robot Collision-Free Path Optimization
Directory of Open Access Journals (Sweden)
Xuewu Wang
2017-02-01
Full Text Available Reasonable welding path has a significant impact on welding efficiency, and a collision-free path should be considered first in the process of welding robot path planning. The shortest path length is considered as an optimization objective, and obstacle avoidance is considered as the constraint condition in this paper. First, a grid method is used as a modeling method after the optimization objective is analyzed. For local collision-free path planning, an ant colony algorithm is selected as the search strategy. Then, to overcome the shortcomings of the ant colony algorithm, a secondary optimization is presented to improve the optimization performance. Finally, the particle swarm optimization algorithm is used to realize global path planning. Simulation results show that the desired welding path can be obtained based on the optimization strategy.
Evacuation route planning during nuclear emergency using genetic algorithm
International Nuclear Information System (INIS)
Suman, Vitisha; Sarkar, P.K.
2012-01-01
In nuclear industry the routing in case of any emergency is a cause of concern and of great importance. Even the smallest of time saved in the affected region saves a huge amount of otherwise received dose. Genetic algorithm an optimization technique has great ability to search for the optimal path from the affected region to a destination station in a spatially addressed problem. Usually heuristic algorithms are used to carry out these types of search strategy, but due to the lack of global sampling in the feasible solution space, these algorithms have considerable possibility of being trapped into local optima. Routing problems mainly are search problems for finding the shortest distance within a time limit to cover the required number of stations taking care of the traffics, road quality, population size etc. Lack of any formal mechanisms to help decision-makers explore the solution space of their problem and thereby challenges their assumptions about the number and range of options available. The Genetic Algorithm provides a way to optimize a multi-parameter constrained problem with an ease. Here use of Genetic Algorithm to generate a range of options available and to search a solution space and selectively focus on promising combinations of criteria makes them ideally suited to such complex spatial decision problems. The emergency response and routing can be made efficient, in accessing the closest facilities and determining the shortest route using genetic algorithm. The accuracy and care in creating database can be used to improve the result of the final output. The Genetic algorithm can be used to improve the accuracy of result on the basis of distance where other algorithm cannot be obtained. The search space can be utilized to its great extend
An optimization algorithm for a capacitated vehicle routing problem ...
Indian Academy of Sciences (India)
In this paper, vehicle routing problem (VRP) with time windows and real world constraints are considered as a real-world application on google maps. Also, tabu search is used and Hopfield neural networks is utilized. Basic constraints consist of customer demands, time windows, vehicle speed, vehicle capacity andworking ...
Guex, Guillaume
2016-05-01
In recent articles about graphs, different models proposed a formalism to find a type of path between two nodes, the source and the target, at crossroads between the shortest-path and the random-walk path. These models include a freely adjustable parameter, allowing to tune the behavior of the path toward randomized movements or direct routes. This article presents a natural generalization of these models, namely a model with multiple sources and targets. In this context, source nodes can be viewed as locations with a supply of a certain good (e.g. people, money, information) and target nodes as locations with a demand of the same good. An algorithm is constructed to display the flow of goods in the network between sources and targets. With again a freely adjustable parameter, this flow can be tuned to follow routes of minimum cost, thus displaying the flow in the context of the optimal transportation problem or, by contrast, a random flow, known to be similar to the electrical current flow if the random-walk is reversible. Moreover, a source-targetcoupling can be retrieved from this flow, offering an optimal assignment to the transportation problem. This algorithm is described in the first part of this article and then illustrated with case studies.
Directory of Open Access Journals (Sweden)
P. Chitra
2017-04-01
Full Text Available Recently, wireless network technologies were designed for most of the applications. Congestion raised in the wireless network degrades the performance and reduces the throughput. Congestion-free network is quit essen- tial in the transport layer to prevent performance degradation in a wireless network. Game theory is a branch of applied mathematics and applied sciences that used in wireless network, political science, biology, computer science, philosophy and economics. e great challenges of wireless network are their congestion by various factors. E ective congestion-free alternate path routing is pretty essential to increase network performance. Stackelberg game theory model is currently employed as an e ective tool to design and formulate conges- tion issues in wireless networks. is work uses a Stackelberg game to design alternate path model to avoid congestion. In this game, leaders and followers are selected to select an alternate routing path. e correlated equilibrium is used in Stackelberg game for making better decision between non-cooperation and cooperation. Congestion was continuously monitored to increase the throughput in the network. Simulation results show that the proposed scheme could extensively improve the network performance by reducing congestion with the help of Stackelberg game and thereby enhance throughput.
Villarubia, Gabriel; De Paz, Juan F.; Bajo, Javier
2017-01-01
The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route. PMID:29088087
Directory of Open Access Journals (Sweden)
Daniel H. De La Iglesia
2017-10-01
Full Text Available The use of electric bikes (e-bikes has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.
De La Iglesia, Daniel H; Villarrubia, Gabriel; De Paz, Juan F; Bajo, Javier
2017-10-31
The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.
Directory of Open Access Journals (Sweden)
Adamu Murtala Zungeru
2013-01-01
Full Text Available The main problem for event gathering in wireless sensor networks (WSNs is the restricted communication range for each node. Due to the restricted communication range and high network density, event forwarding in WSNs is very challenging and requires multihop data forwarding. Currently, the energy-efficient ant based routing (EEABR algorithm, based on the ant colony optimization (ACO metaheuristic, is one of the state-of-the-art energy-aware routing protocols. In this paper, we propose three improvements to the EEABR algorithm to further improve its energy efficiency. The improvements to the original EEABR are based on the following: (1 a new scheme to intelligently initialize the routing tables giving priority to neighboring nodes that simultaneously could be the destination, (2 intelligent update of routing tables in case of a node or link failure, and (3 reducing the flooding ability of ants for congestion control. The energy efficiency improvements are significant particularly for dynamic routing environments. Experimental results using the RMASE simulation environment show that the proposed method increases the energy efficiency by up to 9% and 64% in converge-cast and target-tracking scenarios, respectively, over the original EEABR without incurring a significant increase in complexity. The method is also compared and found to also outperform other swarm-based routing protocols such as sensor-driven and cost-aware ant routing (SC and Beesensor.
Application of Fuzzy Sets for the Improvement of Routing Optimization Heuristic Algorithms
Directory of Open Access Journals (Sweden)
Mattas Konstantinos
2016-12-01
Full Text Available The determination of the optimal circular path has become widely known for its difficulty in producing a solution and for the numerous applications in the scope of organization and management of passenger and freight transport. It is a mathematical combinatorial optimization problem for which several deterministic and heuristic models have been developed in recent years, applicable to route organization issues, passenger and freight transport, storage and distribution of goods, waste collection, supply and control of terminals, as well as human resource management. Scope of the present paper is the development, with the use of fuzzy sets, of a practical, comprehensible and speedy heuristic algorithm for the improvement of the ability of the classical deterministic algorithms to identify optimum, symmetrical or non-symmetrical, circular route. The proposed fuzzy heuristic algorithm is compared to the corresponding deterministic ones, with regard to the deviation of the proposed solution from the best known solution and the complexity of the calculations needed to obtain this solution. It is shown that the use of fuzzy sets reduced up to 35% the deviation of the solution identified by the classical deterministic algorithms from the best known solution.
Exact and Heuristic Algorithms for Routing AGV on Path with Precedence Constraints
Directory of Open Access Journals (Sweden)
Liang Xu
2016-01-01
Full Text Available A new problem arises when an automated guided vehicle (AGV is dispatched to visit a set of customers, which are usually located along a fixed wire transmitting signal to navigate the AGV. An optimal visiting sequence is desired with the objective of minimizing the total travelling distance (or time. When precedence constraints are restricted on customers, the problem is referred to as traveling salesman problem on path with precedence constraints (TSPP-PC. Whether or not it is NP-complete has no answer in the literature. In this paper, we design dynamic programming for the TSPP-PC, which is the first polynomial-time exact algorithm when the number of precedence constraints is a constant. For the problem with number of precedence constraints, part of the input can be arbitrarily large, so we provide an efficient heuristic based on the exact algorithm.
Optimization of the Critical Diameter and Average Path Length of Social Networks
Directory of Open Access Journals (Sweden)
Haifeng Du
2017-01-01
Full Text Available Optimizing average path length (APL by adding shortcut edges has been widely discussed in connection with social networks, but the relationship between network diameter and APL is generally ignored in the dynamic optimization of APL. In this paper, we analyze this relationship and transform the problem of optimizing APL into the problem of decreasing diameter to 2. We propose a mathematic model based on a memetic algorithm. Experimental results show that our algorithm can efficiently solve this problem as well as optimize APL.
Load Balancing Routing with Bounded Stretch
Directory of Open Access Journals (Sweden)
Chen Siyuan
2010-01-01
Full Text Available Routing in wireless networks has been heavily studied in the last decade. Many routing protocols are based on classic shortest path algorithms. However, shortest path-based routing protocols suffer from uneven load distribution in the network, such as crowed center effect where the center nodes have more load than the nodes in the periphery. Aiming to balance the load, we propose a novel routing method, called Circular Sailing Routing (CSR, which can distribute the traffic more evenly in the network. The proposed method first maps the network onto a sphere via a simple stereographic projection, and then the route decision is made by a newly defined "circular distance" on the sphere instead of the Euclidean distance in the plane. We theoretically prove that for a network, the distance traveled by the packets using CSR is no more than a small constant factor of the minimum (the distance of the shortest path. We also extend CSR to a localized version, Localized CSR, by modifying greedy routing without any additional communication overhead. In addition, we investigate how to design CSR routing for 3D networks. For all proposed methods, we conduct extensive simulations to study their performances and compare them with global shortest path routing or greedy routing in 2D and 3D wireless networks.
Location-Based Self-Adaptive Routing Algorithm for Wireless Sensor Networks in Home Automation
Directory of Open Access Journals (Sweden)
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.
Entanglement-Gradient Routing for Quantum Networks.
Gyongyosi, Laszlo; Imre, Sandor
2017-10-27
We define the entanglement-gradient routing scheme for quantum repeater networks. The routing framework fuses the fundamentals of swarm intelligence and quantum Shannon theory. Swarm intelligence provides nature-inspired solutions for problem solving. Motivated by models of social insect behavior, the routing is performed using parallel threads to determine the shortest path via the entanglement gradient coefficient, which describes the feasibility of the entangled links and paths of the network. The routing metrics are derived from the characteristics of entanglement transmission and relevant measures of entanglement distribution in quantum networks. The method allows a moderate complexity decentralized routing in quantum repeater networks. The results can be applied in experimental quantum networking, future quantum Internet, and long-distance quantum communications.
Xu, Quan-Li; Cao, Yu-Wei; Yang, Kun
2018-03-01
Ant Colony Optimization (ACO) is the most widely used artificial intelligence algorithm at present. This study introduced the principle and mathematical model of ACO algorithm in solving Vehicle Routing Problem (VRP), and designed a vehicle routing optimization model based on ACO, then the vehicle routing optimization simulation system was developed by using c ++ programming language, and the sensitivity analyses, estimations and improvements of the three key parameters of ACO were carried out. The results indicated that the ACO algorithm designed in this paper can efficiently solve rational planning and optimization of VRP, and the different values of the key parameters have significant influence on the performance and optimization effects of the algorithm, and the improved algorithm is not easy to locally converge prematurely and has good robustness.
Training shortest-path tractography: Automatic learning of spatial priors
DEFF Research Database (Denmark)
Kasenburg, Niklas; Liptrot, Matthew George; Reislev, Nina Linde
2016-01-01
Tractography is the standard tool for automatic delineation of white matter tracts from diffusion weighted images. However, the output of tractography often requires post-processing to remove false positives and ensure a robust delineation of the studied tract, and this demands expert prior...... knowledge. Here we demonstrate how such prior knowledge, or indeed any prior spatial information, can be automatically incorporated into a shortest-path tractography approach to produce more robust results. We describe how such a prior can be automatically generated (learned) from a population, and we...
A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks
Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Loureiro, Antonio A. F.
2009-01-01
Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207
A hybrid GA-TS algorithm for open vehicle routing optimization of coal mines material
Energy Technology Data Exchange (ETDEWEB)
Yu, S.W.; Ding, C.; Zhu, K.J. [China University of Geoscience, Wuhan (China)
2011-08-15
In the open vehicle routing problem (OVRP), the objective is to minimize the number of vehicles and the total distance (or time) traveled. This study primarily focuses on solving an open vehicle routing problem (OVRP) by applying a novel hybrid genetic algorithm and the Tabu search (GA-TS), which combines the GA's parallel computing and global optimization with TS's Tabu search skill and fast local search. Firstly, the proposed algorithm uses natural number coding according to the customer demands and the captivity of the vehicle for globe optimization. Secondly, individuals of population do TS local search with a certain degree of probability, namely, do the local routing optimization of all customer sites belong to one vehicle. The mechanism not only improves the ability of global optimization, but also ensures the speed of operation. The algorithm was used in Zhengzhou Coal Mine and Power Supply Co., Ltd.'s transport vehicle routing optimization.
k-Shortest routing of trains on shunting yards
Riezebos, Jan; van Wezel, Wout
2009-01-01
We consider the problem of designing algorithmic support for k-best routing decisions in train shunting scheduling. A study at the Netherlands Railways revealed that planners like to interact with the solution process of finding suitable routes. Two types of interaction were required: the
Improved AODV route recovery in mobile ad-hoc networks using a genetic algorithm
Directory of Open Access Journals (Sweden)
Ahmad Maleki
2014-09-01
Full Text Available An important issue in ad-hoc on-demand distance vector (AODV routing protocols is route failure caused by node mobility in the MANETs. The AODV requires a new route discovery procedure whenever a route breaks and these frequent route discoveries increase transmission delays and routing overhead. The present study proposes a new method for AODVs using a genetic algorithm to improve the route recovery mechanism. When failure occurs in a route, the proposed method (GAAODV makes decisions regarding the QOS parameter to select source or local repair. The task of the genetic algorithm is to find an appropriate combination of weights to optimize end-to-end delay. This paper evaluates the metrics of routing overhead, average end-to-end delay, and packet delivery ratio. Comparison of the new algorithm and AODV (RFC 3561 using a NS-2 simulator shows that GAAODV obtains better results for the QOS parameters.
Le Viet Duc, L Duc; Scholten, Johan; Havinga, Paul J.M.
2012-01-01
Recently the increasing number of sensors integrated in smartphones, especially the iPhone and Android phones, has motivated the development of routing algorithms for Opportunistic Mobile Sensor Networks (OppMSNs). Although there are many existing opportunistic routing algorithms, researchers still
A multicast tree aggregation algorithm in wavelength-routed WDM networks
Cheng, Hsu-Chen; Kuo, Chin-Chun; Lin, Frank Y.
2005-02-01
Wavelength division multiplexing (WDM) has been considered a promising transmission technology in optical communication networks. With the continuous advance in optical technology, WDM network will play an important role in wide area backbone networks. Optical wavelength switching, compared with optical packet switching, is a more mature and more cost-effective choice for optical switching technologies. Besides, the technology of time division multiplexing in optical communication networks has been working smoothly for a long time. In the proposed research, the problem of multicast groups aggregation and multicast routing and wavelength assignment in wavelength-routed WDM network is studied. The optical cross connect switches in the problem are assumed to have limited optical multicast/splitting and TDM functionalities. Given the physical network topology and capacity, the objective is to maximize the total revenue by means of utmost merging multicast groups into larger macro-groups. The groups in the same macro-group will share a multicast tree to conduct data transmission. The problem is formulated as an optimization problem, where the objective function is to maximize the total revenue subject to capacity constraints of components in the optical network, wavelength continuity constraints, and tree topology constraints. The decision variables in the formulations include the merging results between groups, multicast tree routing assignment and wavelength assignment. The basic approach to the algorithm development for this model is Lagrangean relaxation in conjunction with a number of optimization techniques. In computational experiments, the proposed algorithms are evaluated on different network topologies and perform efficiently and effectively according to the experiment results.
Hannan, M A; Akhtar, Mahmuda; Begum, R A; Basri, H; Hussain, A; Scavino, Edgar
2018-01-01
Waste collection widely depends on the route optimization problem that involves a large amount of expenditure in terms of capital, labor, and variable operational costs. Thus, the more waste collection route is optimized, the more reduction in different costs and environmental effect will be. This study proposes a modified particle swarm optimization (PSO) algorithm in a capacitated vehicle-routing problem (CVRP) model to determine the best waste collection and route optimization solutions. In this study, threshold waste level (TWL) and scheduling concepts are applied in the PSO-based CVRP model under different datasets. The obtained results from different datasets show that the proposed algorithmic CVRP model provides the best waste collection and route optimization in terms of travel distance, total waste, waste collection efficiency, and tightness at 70-75% of TWL. The obtained results for 1 week scheduling show that 70% of TWL performs better than all node consideration in terms of collected waste, distance, tightness, efficiency, fuel consumption, and cost. The proposed optimized model can serve as a valuable tool for waste collection and route optimization toward reducing socioeconomic and environmental impacts. Copyright © 2017 Elsevier Ltd. All rights reserved.
Optimization model of conventional missile maneuvering route based on improved Floyd algorithm
Wu, Runping; Liu, Weidong
2018-04-01
Missile combat plays a crucial role in the victory of war under high-tech conditions. According to the characteristics of maneuver tasks of conventional missile units in combat operations, the factors influencing road maneuvering are analyzed. Based on road distance, road conflicts, launching device speed, position requirements, launch device deployment, Concealment and so on. The shortest time optimization model was built to discuss the situation of road conflict and the strategy of conflict resolution. The results suggest that in the process of solving road conflict, the effect of node waiting is better than detour to another way. In this study, we analyzed the deficiency of the traditional Floyd algorithm which may limit the optimal way of solving road conflict, and put forward the improved Floyd algorithm, meanwhile, we designed the algorithm flow which would be better than traditional Floyd algorithm. Finally, throgh a numerical example, the model and the algorithm were proved to be reliable and effective.
Dessing, D.; Vries, S.I. de; Hegeman, G.; Verhagen, E.; Mechelen, W. van; Pierik, F.H.
2016-01-01
Background: The purpose of this study is to increase our understanding of environmental correlates that are associated with route choice during active transportation to school (ATS) by comparing characteristics of actual walking and cycling routes between home and school with the shortest possible
A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.
Directory of Open Access Journals (Sweden)
Huan Chen
Full Text Available This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN. Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.
A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.
Chen, Huan; Li, Lemin; Ren, Jing; Wang, Yang; Zhao, Yangming; Wang, Xiong; Wang, Sheng; Xu, Shizhong
2015-01-01
This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.
A novel communication mechanism based on node potential multi-path routing
Bu, Youjun; Zhang, Chuanhao; Jiang, YiMing; Zhang, Zhen
2016-10-01
With the network scales rapidly and new network applications emerge frequently, bandwidth supply for today's Internet could not catch up with the rapid increasing requirements. Unfortunately, irrational using of network sources makes things worse. Actual network deploys single-next-hop optimization paths for data transmission, but such "best effort" model leads to the imbalance use of network resources and usually leads to local congestion. On the other hand Multi-path routing can use the aggregation bandwidth of multi paths efficiently and improve the robustness of network, security, load balancing and quality of service. As a result, multi-path has attracted much attention in the routing and switching research fields and many important ideas and solutions have been proposed. This paper focuses on implementing the parallel transmission of multi next-hop data, balancing the network traffic and reducing the congestion. It aimed at exploring the key technologies of the multi-path communication network, which could provide a feasible academic support for subsequent applications of multi-path communication networking. It proposed a novel multi-path algorithm based on node potential in the network. And the algorithm can fully use of the network link resource and effectively balance network link resource utilization.
Optimal Path Choice in Railway Passenger Travel Network Based on Residual Train Capacity
Directory of Open Access Journals (Sweden)
Fei Dou
2014-01-01
Full Text Available Passenger’s optimal path choice is one of the prominent research topics in the field of railway passenger transport organization. More and more different train types are available, increasing path choices from departure to destination for travelers are unstoppable. However, travelers cannot avoid being confused when they hope to choose a perfect travel plan based on various travel time and cost constraints before departure. In this study, railway passenger travel network is constructed based on train timetable. Both the generalized cost function we developed and the residual train capacity are considered to be the foundation of path searching procedure. The railway passenger travel network topology is analyzed based on residual train capacity. Considering the total travel time, the total travel cost, and the total number of passengers, we propose an optimal path searching algorithm based on residual train capacity in railway passenger travel network. Finally, the rationale of the railway passenger travel network and the optimal path generation algorithm are verified positively by case study.
Low Carbon Footprint Routes for Bird Watching
Directory of Open Access Journals (Sweden)
Wei-Ta Fang
2015-03-01
Full Text Available Bird watching is one of many recreational activities popular in ecotourism. Its popularity, therefore, prompts the need for studies on energy conservation. One such environmentally friendly approach toward minimizing bird watching’s ecological impact is ensuring a reduced carbon footprint by using an economic travel itinerary comprising a series of connected routes between tourist attractions that minimizes transit time. This study used a travel-route planning approach using geographic information systems to detect the shortest path, thereby solving the problems associated with time-consuming transport. Based on the results of road network analyses, optimal travel-route planning can be determined. These methods include simulated annealing (SA and genetic algorithms (GA. We applied two algorithms in our simulation research to detect which one is an appropriate algorithm for running carbon-routing algorithms at the regional scale. SA, which is superior to GA, is considered an excellent approach to search for the optimal path to reduce carbon dioxide and high gasoline fees, thereby controlling travel time by using the shortest travel routes.
Directory of Open Access Journals (Sweden)
Longxiang Li
Full Text Available Effective assessments of air-pollution exposure depend on the ability to accurately predict pollutant concentrations at unmonitored locations, which can be achieved through spatial interpolation. However, most interpolation approaches currently in use are based on the Euclidean distance, which cannot account for the complex nonlinear features displayed by air-pollution distributions in the wind-field. In this study, an interpolation method based on the shortest path distance is developed to characterize the impact of complex urban wind-field on the distribution of the particulate matter concentration. In this method, the wind-field is incorporated by first interpolating the observed wind-field from a meteorological-station network, then using this continuous wind-field to construct a cost surface based on Gaussian dispersion model and calculating the shortest wind-field path distances between locations, and finally replacing the Euclidean distances typically used in Inverse Distance Weighting (IDW with the shortest wind-field path distances. This proposed methodology is used to generate daily and hourly estimation surfaces for the particulate matter concentration in the urban area of Beijing in May 2013. This study demonstrates that wind-fields can be incorporated into an interpolation framework using the shortest wind-field path distance, which leads to a remarkable improvement in both the prediction accuracy and the visual reproduction of the wind-flow effect, both of which are of great importance for the assessment of the effects of pollutants on human health.
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....
Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
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.
Particle swarm optimization for determining shortest distance to voltage collapse
Energy Technology Data Exchange (ETDEWEB)
Arya, L.D.; Choube, S.C. [Electrical Engineering Department, S.G.S.I.T.S. Indore, MP 452 003 (India); Shrivastava, M. [Electrical Engineering Department, Government Engineering College Ujjain, MP 456 010 (India); Kothari, D.P. [Centre for Energy Studies, Indian Institute of Technology, Delhi (India)
2007-12-15
This paper describes an algorithm for computing shortest distance to voltage collapse or determination of CSNBP using PSO technique. A direction along CSNBP gives conservative results from voltage security view point. This information is useful to the operator to steer the system away from this point by taking corrective actions. The distance to a closest bifurcation is a minimum of the loadability given a slack bus or participation factors for increasing generation as the load increases. CSNBP determination has been formulated as an optimization problem to be used in PSO technique. PSO is a new evolutionary algorithm (EA) which is population based inspired by the social behavior of animals such as fish schooling and birds flocking. It can handle optimization problems with any complexity since mechanization is simple with few parameters to be tuned. The developed algorithm has been implemented on two standard test systems. (author)
Identifying vital edges in Chinese air route network via memetic algorithm
Directory of Open Access Journals (Sweden)
Wenbo Du
2017-02-01
Full Text Available Due to rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system. Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges, and hence the solution of this model is the set of vital edges. Counterintuitively, our results show that the most vital edges are not necessarily the edges of the highest topological importance, for which we provide an extensive explanation from the microscope view. Our findings also offer new insights to understanding and optimizing other real-world network systems.
An Efficient Data-Gathering Routing Protocol for Underwater Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Nadeem Javaid
2015-11-01
Full Text Available Most applications of underwater wireless sensor networks (UWSNs demand reliable data delivery over a longer period in an efficient and timely manner. However, the harsh and unpredictable underwater environment makes routing more challenging as compared to terrestrial WSNs. Most of the existing schemes deploy mobile sensors or a mobile sink (MS to maximize data gathering. However, the relatively high deployment cost prevents their usage in most applications. Thus, this paper presents an autonomous underwater vehicle (AUV-aided efficient data-gathering (AEDG routing protocol for reliable data delivery in UWSNs. To prolong the network lifetime, AEDG employs an AUV for data collection from gateways and uses a shortest path tree (SPT algorithm while associating sensor nodes with the gateways. The AEDG protocol also limits the number of associated nodes with the gateway nodes to minimize the network energy consumption and to prevent the gateways from overloading. Moreover, gateways are rotated with the passage of time to balance the energy consumption of the network. To prevent data loss, AEDG allows dynamic data collection at the AUV depending on the limited number of member nodes that are associated with each gateway. We also develop a sub-optimal elliptical trajectory of AUV by using a connected dominating set (CDS to further facilitate network throughput maximization. The performance of the AEDG is validated via simulations, which demonstrate the effectiveness of AEDG in comparison to two existing UWSN routing protocols in terms of the selected performance metrics.
An Efficient Data-Gathering Routing Protocol for Underwater Wireless Sensor Networks.
Javaid, Nadeem; Ilyas, Naveed; Ahmad, Ashfaq; Alrajeh, Nabil; Qasim, Umar; Khan, Zahoor Ali; Liaqat, Tayyaba; Khan, Majid Iqbal
2015-11-17
Most applications of underwater wireless sensor networks (UWSNs) demand reliable data delivery over a longer period in an efficient and timely manner. However, the harsh and unpredictable underwater environment makes routing more challenging as compared to terrestrial WSNs. Most of the existing schemes deploy mobile sensors or a mobile sink (MS) to maximize data gathering. However, the relatively high deployment cost prevents their usage in most applications. Thus, this paper presents an autonomous underwater vehicle (AUV)-aided efficient data-gathering (AEDG) routing protocol for reliable data delivery in UWSNs. To prolong the network lifetime, AEDG employs an AUV for data collection from gateways and uses a shortest path tree (SPT) algorithm while associating sensor nodes with the gateways. The AEDG protocol also limits the number of associated nodes with the gateway nodes to minimize the network energy consumption and to prevent the gateways from overloading. Moreover, gateways are rotated with the passage of time to balance the energy consumption of the network. To prevent data loss, AEDG allows dynamic data collection at the AUV depending on the limited number of member nodes that are associated with each gateway. We also develop a sub-optimal elliptical trajectory of AUV by using a connected dominating set (CDS) to further facilitate network throughput maximization. The performance of the AEDG is validated via simulations, which demonstrate the effectiveness of AEDG in comparison to two existing UWSN routing protocols in terms of the selected performance metrics.
PlayNCool: Opportunistic Network Coding for Local Optimization of Routing in Wireless Mesh Networks
DEFF Research Database (Denmark)
Pahlevani, Peyman; Roetter, Daniel Enrique Lucani; Pedersen, Morten Videbæk
2013-01-01
This paper introduces PlayNCool, an opportunistic protocol with local optimization based on network coding to increase the throughput of a wireless mesh network (WMN). PlayNCool aims to enhance current routing protocols by (i) allowing random linear network coding transmissions end-to-end, (ii) r...
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«.
DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Francisco José Estévez
2016-06-01
Full Text Available The evolution of Smart City projects is pushing researchers and companies to develop more efficient embedded hardware and also more efficient communication technologies. These communication technologies are the focus of this work, presenting a new routing algorithm based on dynamically-allocated sub-networks and node roles. Among these features, our algorithm presents a fast set-up time, a reduced overhead and a hierarchical organization, which allows for the application of complex management techniques. This work presents a routing algorithm based on a dynamically-allocated hierarchical clustering, which uses the link quality indicator as a reference parameter, maximizing the network coverage and minimizing the control message overhead and the convergence time. The present work based its test scenario and analysis in the density measure, considered as a node degree. The routing algorithm is compared with some of the most well known routing algorithms for different scenario densities.
An optimization method of VON mapping for energy efficiency and routing in elastic optical networks
Liu, Huanlin; Xiong, Cuilian; Chen, Yong; Li, Changping; Chen, Derun
2018-03-01
To improve resources utilization efficiency, network virtualization in elastic optical networks has been developed by sharing the same physical network for difference users and applications. In the process of virtual nodes mapping, longer paths between physical nodes will consume more spectrum resources and energy. To address the problem, we propose a virtual optical network mapping algorithm called genetic multi-objective optimize virtual optical network mapping algorithm (GM-OVONM-AL), which jointly optimizes the energy consumption and spectrum resources consumption in the process of virtual optical network mapping. Firstly, a vector function is proposed to balance the energy consumption and spectrum resources by optimizing population classification and crowding distance sorting. Then, an adaptive crossover operator based on hierarchical comparison is proposed to improve search ability and convergence speed. In addition, the principle of the survival of the fittest is introduced to select better individual according to the relationship of domination rank. Compared with the spectrum consecutiveness-opaque virtual optical network mapping-algorithm and baseline-opaque virtual optical network mapping algorithm, simulation results show the proposed GM-OVONM-AL can achieve the lowest bandwidth blocking probability and save the energy consumption.
Enhanced Contact Graph Routing (ECGR) MACHETE Simulation Model
Segui, John S.; Jennings, Esther H.; Clare, Loren P.
2013-01-01
Contact Graph Routing (CGR) for Delay/Disruption Tolerant Networking (DTN) space-based networks makes use of the predictable nature of node contacts to make real-time routing decisions given unpredictable traffic patterns. The contact graph will have been disseminated to all nodes before the start of route computation. CGR was designed for space-based networking environments where future contact plans are known or are independently computable (e.g., using known orbital dynamics). For each data item (known as a bundle in DTN), a node independently performs route selection by examining possible paths to the destination. Route computation could conceivably run thousands of times a second, so computational load is important. This work refers to the simulation software model of Enhanced Contact Graph Routing (ECGR) for DTN Bundle Protocol in JPL's MACHETE simulation tool. The simulation model was used for performance analysis of CGR and led to several performance enhancements. The simulation model was used to demonstrate the improvements of ECGR over CGR as well as other routing methods in space network scenarios. ECGR moved to using earliest arrival time because it is a global monotonically increasing metric that guarantees the safety properties needed for the solution's correctness since route re-computation occurs at each node to accommodate unpredicted changes (e.g., traffic pattern, link quality). Furthermore, using earliest arrival time enabled the use of the standard Dijkstra algorithm for path selection. The Dijkstra algorithm for path selection has a well-known inexpensive computational cost. These enhancements have been integrated into the open source CGR implementation. The ECGR model is also useful for route metric experimentation and comparisons with other DTN routing protocols particularly when combined with MACHETE's space networking models and Delay Tolerant Link State Routing (DTLSR) model.
Network analysis of PTSD symptoms following mass violence.
Sullivan, Connor P; Smith, Andrew J; Lewis, Michael; Jones, Russell T
2018-01-01
Network analysis is a useful tool for understanding how symptoms interact with one another to influence psychopathology. However, this analytic strategy has not been fully utilized in the PTSD field. The current study utilized network analysis to examine connectedness and strength among PTSD symptoms (employing both partial correlation and regression network analyses) among a community sample of students exposed to the 2007 Virginia Tech shootings. Respondents (N = 4,639) completed online surveys 3-4 months postshootings, with PTSD symptom severity measured via the Trauma Symptom Questionnaire. Data were analyzed via adaptive least absolute shrinkage and selection operator (LASSO) and relative importance networks, as well as Dijkstra's algorithm to identify the shortest path from each symptom to all other symptoms. Relative importance network analysis revealed that intrusive thoughts had the strongest influence on other symptoms (i.e., had many strong connections [highest outdegree]) while computing Dijkstra's algorithm indicated that anger produced the shortest path to all other symptoms (i.e., the strongest connections to all other symptoms). Findings suggest that anger or intrusion likely play a crucial role in the development and maintenance of PTSD (i.e., are more influential within the network than are other symptoms). (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Comparison of some evolutionary algorithms for optimization of the path synthesis problem
Grabski, Jakub Krzysztof; Walczak, Tomasz; Buśkiewicz, Jacek; Michałowska, Martyna
2018-01-01
The paper presents comparison of the results obtained in a mechanism synthesis by means of some selected evolutionary algorithms. The optimization problem considered in the paper as an example is the dimensional synthesis of the path generating four-bar mechanism. In order to solve this problem, three different artificial intelligence algorithms are employed in this study.
Routing Optimization of AVB Streams in TSN Networks
DEFF Research Database (Denmark)
Laursen, Sune Mølgaard; Pop, Paul; Steiner, Wilfried
2016-01-01
In this paper we are interested in safety-critical real-time applications implemented on distributed architectures using the Time-Sensitive Networking (TSN) standard. The ongoing standardization of TSN is an IEEE effort to bring deterministic real-time capabilities into the IEEE 802.1 Ethernet...... standard supporting safety-critical systems and guaranteed Quality-of-Service. TSN will support Time-Triggered (TT) communication based on schedule tables, Audio-Video-Bridging (AVB) streams with bounded end-to-end latency as well as Best-Effort messages. We consider that we know the topology...... Procedure (GRASP)-based heuristic for this routing optimization problem. The proposed approaches has been evaluated using several test cases....
Aircraft Route Optimization using the A-Star Algorithm
2014-03-27
Map Cost array allows a search for a route that not only seeks to minimize the distance travelled, but also considers other factors that may impact ...Rules (VFR) flight profile requires aviators to plan a 20-minute fuel reserve into the flight while an Instrument Flight Rules ( IFR ) flight profile
Directory of Open Access Journals (Sweden)
Qi Hu
2013-04-01
Full Text Available State-of-the-art heuristic algorithms to solve the vehicle routing problem with time windows (VRPTW usually present slow speeds during the early iterations and easily fall into local optimal solutions. Focusing on solving the above problems, this paper analyzes the particle encoding and decoding strategy of the particle swarm optimization algorithm, the construction of the vehicle route and the judgment of the local optimal solution. Based on these, a hybrid chaos-particle swarm optimization algorithm (HPSO is proposed to solve VRPTW. The chaos algorithm is employed to re-initialize the particle swarm. An efficient insertion heuristic algorithm is also proposed to build the valid vehicle route in the particle decoding process. A particle swarm premature convergence judgment mechanism is formulated and combined with the chaos algorithm and Gaussian mutation into HPSO when the particle swarm falls into the local convergence. Extensive experiments are carried out to test the parameter settings in the insertion heuristic algorithm and to evaluate that they are corresponding to the data’s real-distribution in the concrete problem. It is also revealed that the HPSO achieves a better performance than the other state-of-the-art algorithms on solving VRPTW.
Improved Efficient Routing Strategy on Scale-Free Networks
Jiang, Zhong-Yuan; Liang, Man-Gui
Since the betweenness of nodes in complex networks can theoretically represent the traffic load of nodes under the currently used routing strategy, we propose an improved efficient (IE) routing strategy to enhance to the network traffic capacity based on the betweenness centrality. Any node with the highest betweenness is susceptible to traffic congestion. An efficient way to improve the network traffic capacity is to redistribute the heavy traffic load from these central nodes to non-central nodes, so in this paper, we firstly give a path cost function by considering the sum of node betweenness with a tunable parameter β along the actual path. Then, by minimizing the path cost, our IE routing strategy achieved obvious improvement on the network transport efficiency. Simulations on scale-free Barabási-Albert (BA) networks confirmed the effectiveness of our strategy, when compared with the efficient routing (ER) and the shortest path (SP) routing.
Directory of Open Access Journals (Sweden)
Lavrenov Roman
2017-01-01
Full Text Available Our research focuses on operation of a heterogeneous robotic group that carries out point-to point navigation in GPS-denied dynamic environment, applying a combined local and global planning approach. In this paper, we introduce a homotopy-based high-level planner, which uses a modified splinebased path-planning algorithm. The algorithm utilizes Voronoi graph for global planning and a set of optimization criteria for local improvements of selected paths. The simulation was implemented in Matlab environment.
An optimization algorithm for a capacitated vehicle routing problem ...
Indian Academy of Sciences (India)
Pinar Kirci
PINAR KIRCI. Engineering Sciences Department, Istanbul University, Istanbul, Turkey .... In VRP solution methods, tabu search algorithm belongs to ..... systems which are considered in statistical mechanics is ..... Procedia-Social Behav. Sci.
Wang, Fu; Liu, Bo; Zhang, Lijia; Zhang, Qi; Tian, Qinghua; Tian, Feng; Rao, Lan; Xin, Xiangjun
2017-07-01
Elastic software-defined optical networks greatly improve the flexibility of the optical switching network while it has brought challenges to the routing and spectrum assignment (RSA). A multilayer virtual topology model is proposed to solve RSA problems. Two RSA algorithms based on the virtual topology are proposed, which are the ant colony optimization (ACO) algorithm of minimum consecutiveness loss and the ACO algorithm of maximum spectrum consecutiveness. Due to the computing power of the control layer in the software-defined network, the routing algorithm avoids the frequent link-state information between routers. Based on the effect of the spectrum consecutiveness loss on the pheromone in the ACO, the path and spectrum of the minimal impact on the network are selected for the service request. The proposed algorithms have been compared with other algorithms. The results show that the proposed algorithms can reduce the blocking rate by at least 5% and perform better in spectrum efficiency. Moreover, the proposed algorithms can effectively decrease spectrum fragmentation and enhance available spectrum consecutiveness.
Iswari, T.; Asih, A. M. S.
2018-04-01
In the logistics system, transportation plays an important role to connect every element in the supply chain, but it can produces the greatest cost. Therefore, it is important to make the transportation costs as minimum as possible. Reducing the transportation cost can be done in several ways. One of the ways to minimizing the transportation cost is by optimizing the routing of its vehicles. It refers to Vehicle Routing Problem (VRP). The most common type of VRP is Capacitated Vehicle Routing Problem (CVRP). In CVRP, the vehicles have their own capacity and the total demands from the customer should not exceed the capacity of the vehicle. CVRP belongs to the class of NP-hard problems. These NP-hard problems make it more complex to solve such that exact algorithms become highly time-consuming with the increases in problem sizes. Thus, for large-scale problem instances, as typically found in industrial applications, finding an optimal solution is not practicable. Therefore, this paper uses two kinds of metaheuristics approach to solving CVRP. Those are Genetic Algorithm and Particle Swarm Optimization. This paper compares the results of both algorithms and see the performance of each algorithm. The results show that both algorithms perform well in solving CVRP but still needs to be improved. From algorithm testing and numerical example, Genetic Algorithm yields a better solution than Particle Swarm Optimization in total distance travelled.
DEFF Research Database (Denmark)
Amini, M. Hadi; Broojeni, Kianoosh G.; Dragicevic, Tomislav
2017-01-01
of microgrid while preventing congestion as well as minimizing the power loss. Then, we present a two-layer simulation platform which considers both communication layer and physical layer of the microgrids' cluster. In order to improve the security of communication network, we perform the computations...... regarding the oblivious power routing via a cloud-based network. The proposed framework can be used for further studies that deal with the real-time simulation of the clusters of microgrids. In order to validate the effectiveness of the proposed framework, we implement our proposed oblivious routing scheme...
Directory of Open Access Journals (Sweden)
Sunil Kumar
2013-01-01
Full Text Available Node mobility in mobile ad hoc networks (MANETs causes frequent route breakages and intermittent link stability. In this paper, we introduce a robust routing scheme, known as ad hoc on-demand multipath distance vector with dynamic path update (AOMDV-DPU, for delay-sensitive data transmission over MANET. The proposed scheme improves the AOMDV scheme by incorporating the following features: (i a routing metric based on the combination of minimum hops and received signal strength indicator (RSSI for discovery of reliable routes; (ii a local path update mechanism which strengthens the route, reduces the route breakage frequency, and increases the route longevity; (iii a keep alive mechanism for secondary route maintenance which enables smooth switching between routes and reduces the route discovery frequency; (iv a packet salvaging scheme to improve packet delivery in the event of a route breakage; and (v low HELLO packet overhead. The simulations are carried out in ns-2 for varying node speeds, number of sources, and traffic load conditions. Our AOMDV-DPU scheme achieves significantly higher throughput, lower delay, routing overhead, and route discovery frequency and latency compared to AOMDV. For H.264 compressed video traffic, AOMDV-DPU scheme achieves 3 dB or higher PSNR gain over AOMDV at both low and high node speeds.
Yuniarto, Budi; Kurniawan, Robert
2017-03-01
PLS Path Modeling (PLS-PM) is different from covariance based SEM, where PLS-PM use an approach based on variance or component, therefore, PLS-PM is also known as a component based SEM. Multiblock Partial Least Squares (MBPLS) is a method in PLS regression which can be used in PLS Path Modeling which known as Multiblock PLS Path Modeling (MBPLS-PM). This method uses an iterative procedure in its algorithm. This research aims to modify MBPLS-PM with Back Propagation Neural Network approach. The result is MBPLS-PM algorithm can be modified using the Back Propagation Neural Network approach to replace the iterative process in backward and forward step to get the matrix t and the matrix u in the algorithm. By modifying the MBPLS-PM algorithm using Back Propagation Neural Network approach, the model parameters obtained are relatively not significantly different compared to model parameters obtained by original MBPLS-PM algorithm.
Genetic based optimization for multicast routing algorithm for MANET
Indian Academy of Sciences (India)
three main principles of natural evolution: reproduction, natural selection and species .... For example, figure 3 shows a six-node network with node reachability shown by ... sion range of each node is 220 m using an Omni directional antenna.
Assaf, Ramiz; Saleh, Yahya
2017-09-01
Municipalities are responsible for solid waste collectiont for environmental, social and economic purposes. Practices of municipalities should be effective and efficient, with the objectives of reducing the total incurred costs in the solid waste collection network concurrently achieving the highest service level. This study aims at finding the best routes of solid waste collection network in Nablus city-Palestine. More specifically, the study seeks the optimal route that minimizes the total travelled distance by the trucks and hence the resulted costs. The current situation is evaluated and the problem is modelled as a Vehicle Routing Problem (VRP). The VRP is then optimized via a genetic algorithm. Specifically, compared to the current situation, the trucks total travelled distance was reduced by 66%, whereas the collection time was reduced from 7 hours per truck-trip to 2.3 hours. The findings of this study is useful for all municipality policy makers that are responsible for solid waste collection.
An improved algorithm for finding all minimal paths in a network
International Nuclear Information System (INIS)
Bai, Guanghan; Tian, Zhigang; Zuo, Ming J.
2016-01-01
Minimal paths (MPs) play an important role in network reliability evaluation. In this paper, we report an efficient recursive algorithm for finding all MPs in two-terminal networks, which consist of a source node and a sink node. A linked path structure indexed by nodes is introduced, which accepts both directed and undirected form of networks. The distance between each node and the sink node is defined, and a simple recursive algorithm is presented for labeling the distance for each node. Based on the distance between each node and the sink node, additional conditions for backtracking are incorporated to reduce the number of search branches. With the newly introduced linked node structure, the distances between each node and the sink node, and the additional backtracking conditions, an improved backtracking algorithm for searching for all MPs is developed. In addition, the proposed algorithm can be adapted to search for all minimal paths for each source–sink pair in networks consisting of multiple source nodes and/or multiple sink nodes. Through computational experiments, it is demonstrated that the proposed algorithm is more efficient than existing algorithms when the network size is not too small. The proposed algorithm becomes more advantageous as the size of the network grows. - Highlights: • A linked path structure indexed by nodes is introduced to represent networks. • Additional conditions for backtracking are proposed based on the distance of each node. • An efficient algorithm is developed to find all MPs for two-terminal networks. • The computational efficiency of the algorithm for two-terminal networks is investigated. • The computational efficiency of the algorithm for multi-terminal networks is investigated.
Joint optimization of physical layer parameters and routing in wireless mesh networks
Tobagi, Fouad A.
2010-06-01
Achieving the best performance in a wireless mesh network requires striking the right balance between the performance of links carrying traffic and the extent of spatial reuse of the wireless medium. The performance of a link depends on its transmit power and data rate as well as the level of interference caused by concurrent transmissions in the network; the latter is function of the Energy Detect (ED) threshold that determines when a node may access the medium. Which links in the network carry traffic is determined by the routing function; routing selects paths according to a link metric that reflects the relative performance of links (e.g., the expected transmission time of a packet on the link). In this paper, we seek to maximize end-to-end network throughput by jointly optimizing physical layer parameters and routing. We consider a random topology with a uniform node density. We consider that the signal attenuation between a pair of nodes is determined by a power law path loss model with an exponent equal to 3. Our findings are as follows. Consider first that the same transmit power and same data rate are used on all links. For any transmit power, data rate and ED threshold setting, the highest feasible load is obtained when the level of interference experienced by links used by routing is the highest possible. For a given transmit power and data rate setting, there is an optimum ED threshold that maximizes network performance. At the optimum ED threshold and maximum load, the range of link lengths used by routing is the lowest possible given the topology and routing metric used. With an ED threshold higher than the optimum, the same range of links is used by routing; however, the highest feasible load in this case is lower due to the fact that concurrent transmitters are allowed to be closer. With a lower ED threshold, concurrent transmitters are forced to be farther apart, and thus longer links become more attractive; as a result, the range of link lengths
Joint optimization of physical layer parameters and routing in wireless mesh networks
Tobagi, Fouad A.; Hira, Mukesh M.
2010-01-01
Achieving the best performance in a wireless mesh network requires striking the right balance between the performance of links carrying traffic and the extent of spatial reuse of the wireless medium. The performance of a link depends on its transmit power and data rate as well as the level of interference caused by concurrent transmissions in the network; the latter is function of the Energy Detect (ED) threshold that determines when a node may access the medium. Which links in the network carry traffic is determined by the routing function; routing selects paths according to a link metric that reflects the relative performance of links (e.g., the expected transmission time of a packet on the link). In this paper, we seek to maximize end-to-end network throughput by jointly optimizing physical layer parameters and routing. We consider a random topology with a uniform node density. We consider that the signal attenuation between a pair of nodes is determined by a power law path loss model with an exponent equal to 3. Our findings are as follows. Consider first that the same transmit power and same data rate are used on all links. For any transmit power, data rate and ED threshold setting, the highest feasible load is obtained when the level of interference experienced by links used by routing is the highest possible. For a given transmit power and data rate setting, there is an optimum ED threshold that maximizes network performance. At the optimum ED threshold and maximum load, the range of link lengths used by routing is the lowest possible given the topology and routing metric used. With an ED threshold higher than the optimum, the same range of links is used by routing; however, the highest feasible load in this case is lower due to the fact that concurrent transmitters are allowed to be closer. With a lower ED threshold, concurrent transmitters are forced to be farther apart, and thus longer links become more attractive; as a result, the range of link lengths
A low complexity method for the optimization of network path length in spatially embedded networks
International Nuclear Information System (INIS)
Chen, Guang; Yang, Xu-Hua; Xu, Xin-Li; Ming, Yong; Chen, Sheng-Yong; Wang, Wan-Liang
2014-01-01
The average path length of a network is an important index reflecting the network transmission efficiency. In this paper, we propose a new method of decreasing the average path length by adding edges. A new indicator is presented, incorporating traffic flow demand, to assess the decrease in the average path length when a new edge is added during the optimization process. With the help of the indicator, edges are selected and added into the network one by one. The new method has a relatively small time computational complexity in comparison with some traditional methods. In numerical simulations, the new method is applied to some synthetic spatially embedded networks. The result shows that the method can perform competitively in decreasing the average path length. Then, as an example of an application of this new method, it is applied to the road network of Hangzhou, China. (paper)
Directory of Open Access Journals (Sweden)
Bogna MRÓWCZYŃSKA
2011-01-01
Full Text Available This paper describes an application of an evolutionary algorithm and an artificial immune systems to solve a problem of scheduling an optimal route for waste disposal garbage trucks in its daily operation. Problem of an optimisation is formulated and solved using both methods. The results are presented for an area in one of the Polish cities.
Xu, Sheng-Hua; Liu, Ji-Ping; Zhang, Fu-Hao; Wang, Liang; Sun, Li-Jian
2015-08-27
A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following.
Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem.
Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing
2015-01-01
Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA.
Optimal routing of hazardous substances in time-varying, stochastic transportation networks
International Nuclear Information System (INIS)
Woods, A.L.; Miller-Hooks, E.; Mahmassani, H.S.
1998-07-01
This report is concerned with the selection of routes in a network along which to transport hazardous substances, taking into consideration several key factors pertaining to the cost of transport and the risk of population exposure in the event of an accident. Furthermore, the fact that travel time and the risk measures are not constant over time is explicitly recognized in the routing decisions. Existing approaches typically assume static conditions, possibly resulting in inefficient route selection and unnecessary risk exposure. The report described the application of recent advances in network analysis methodologies to the problem of routing hazardous substances. Several specific problem formulations are presented, reflecting different degrees of risk aversion on the part of the decision-maker, as well as different possible operational scenarios. All procedures explicitly consider travel times and travel costs (including risk measures) to be stochastic time-varying quantities. The procedures include both exact algorithms, which may require extensive computational effort in some situations, as well as more efficient heuristics that may not guarantee a Pareto-optimal solution. All procedures are systematically illustrated for an example application using the Texas highway network, for both normal and incident condition scenarios. The application illustrates the trade-offs between the information obtained in the solution and computational efficiency, and highlights the benefits of incorporating these procedures in a decision-support system for hazardous substance shipment routing decisions
Multi-AGV path planning with double-path constraints by using an improved genetic algorithm.
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Zengliang Han
Full Text Available This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, double-path constraints of both minimizing the total path distance of all AGVs and minimizing single path distances of each AGV are exerted, gaining the optimal shortest total path distance. The simulation results show that the total path distance of all AGVs and the longest single AGV path distance are shortened by using the improved genetic algorithm.
Energy Technology Data Exchange (ETDEWEB)
Fernandes, D.H.; Medeiros, A.R. [Subsea7, Niteroi, RJ (Brazil); Jacob, B.P.; Lima, B.S.L.P.; Albrecht, C.H. [Universidade Federaldo Rio de Janeiro (COPPE/UFRJ), RJ (Brazil). Coordenacao de Programas de Pos-graduacao em Engenharia
2009-07-01
This work presents studies regarding the determination of optimal pipeline routes for offshore applications. The assembly of an objective function is presented; this function can be later associated with Evolutionary Algorithm to implement a computational tool for the automatic determination of the most advantageous pipeline route for a given scenario. This tool may reduce computational overheads, avoid mistakes with route interpretation, and minimize costs with respect to submarine pipeline design and installation. The following aspects can be considered in the assembly of the objective function: Geophysical and geotechnical data obtained from the bathymetry and sonography; the influence of the installation method, total pipeline length and number of free spans to be mitigated along the routes as well as vessel time for both cases. Case studies are presented to illustrate the use of the proposed objective function, including a sensitivity analysis intended to identify the relative influence of selected parameters in the evaluation of different routes. (author)
The time-varying shortest path problem with fuzzy transit costs and speedup
Directory of Open Access Journals (Sweden)
Rezapour Hassan
2016-08-01
Full Text Available In this paper, we focus on the time-varying shortest path problem, where the transit costs are fuzzy numbers. Moreover, we consider this problem in which the transit time can be shortened at a fuzzy speedup cost. Speedup may also be a better decision to find the shortest path from a source vertex to a specified vertex.
Interactive Learning Environment for Bio-Inspired Optimization Algorithms for UAV Path Planning
Duan, Haibin; Li, Pei; Shi, Yuhui; Zhang, Xiangyin; Sun, Changhao
2015-01-01
This paper describes the development of BOLE, a MATLAB-based interactive learning environment, that facilitates the process of learning bio-inspired optimization algorithms, and that is dedicated exclusively to unmanned aerial vehicle path planning. As a complement to conventional teaching methods, BOLE is designed to help students consolidate the…
Directory of Open Access Journals (Sweden)
Yong Wang
2018-04-01
Full Text Available The formation of a cooperative alliance is an effective means of approaching the vehicle routing optimization in two-echelon reverse logistics networks. Cooperative mechanisms can contribute to avoiding the inefficient assignment of resources for the recycling logistics operations and reducing long distance transportation. With regard to the relatively low performance of waste collection, this paper proposes a three-phase methodology to properly address the corresponding vehicle routing problem on two echelons. First, a bi-objective programming model is established to minimize the total cost and the number of vehicles considering semitrailers and vehicles sharing. Furthermore, the Clarke–Wright (CW savings method and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II are combined to design a hybrid routing optimization heuristic, which is denoted CW_NSGA-II. Routes on the first and second echelons are obtained on the basis of sub-optimal solutions provided by CW algorithm. Compared to other intelligent algorithms, CW_NSGA-II reduces the complexity of the multi-objective solutions search and mostly converges to optimality. The profit generated by cooperation among retail stores and the recycling hub in the reverse logistics network is fairly and reasonably distributed to the participants by applying the Minimum Costs-Remaining Savings (MCRS method. Finally, an empirical study in Chengdu City, China, reveals the superiority of CW_NSGA over the multi-objective particle swarm optimization and the multi objective genetic algorithms in terms of solutions quality and convergence. Meanwhile, the comparison of MCRS method with the Shapley value model, equal profit method and cost gap allocation proves that MCRS method is more conducive to the stability of the cooperative alliance. In general, the implementation of cooperation in the optimization of the reverse logistics network effectively leads to the sustainable development of urban and sub
DRO: domain-based route optimization scheme for nested mobile networks
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Chuang Ming-Chin
2011-01-01
Full Text Available Abstract The network mobility (NEMO basic support protocol is designed to support NEMO management, and to ensure communication continuity between nodes in mobile networks. However, in nested mobile networks, NEMO suffers from the pinball routing problem, which results in long packet transmission delays. To solve the problem, we propose a domain-based route optimization (DRO scheme that incorporates a domain-based network architecture and ad hoc routing protocols for route optimization. DRO also improves the intra-domain handoff performance, reduces the convergence time during route optimization, and avoids the out-of-sequence packet problem. A detailed performance analysis and simulations were conducted to evaluate the scheme. The results demonstrate that DRO outperforms existing mechanisms in terms of packet transmission delay (i.e., better route-optimization, intra-domain handoff latency, convergence time, and packet tunneling overhead.
Dharmaseelan, Anoop; Adistambha, Keyne D.
2015-05-01
Fuel cost accounts for 40 percent of the operating cost of an airline. Fuel cost can be minimized by planning a flight on optimized routes. The routes can be optimized by searching best connections based on the cost function defined by the airline. The most common algorithm that used to optimize route search is Dijkstra's. Dijkstra's algorithm produces a static result and the time taken for the search is relatively long. This paper experiments a new algorithm to optimize route search which combines the principle of simulated annealing and genetic algorithm. The experimental results of route search, presented are shown to be computationally fast and accurate compared with timings from generic algorithm. The new algorithm is optimal for random routing feature that is highly sought by many regional operators.
Automatic theory generation from analyst text files using coherence networks
Shaffer, Steven C.
2014-05-01
This paper describes a three-phase process of extracting knowledge from analyst textual reports. Phase 1 involves performing natural language processing on the source text to extract subject-predicate-object triples. In phase 2, these triples are then fed into a coherence network analysis process, using a genetic algorithm optimization. Finally, the highest-value sub networks are processed into a semantic network graph for display. Initial work on a well- known data set (a Wikipedia article on Abraham Lincoln) has shown excellent results without any specific tuning. Next, we ran the process on the SYNthetic Counter-INsurgency (SYNCOIN) data set, developed at Penn State, yielding interesting and potentially useful results.
The tomography of human mobility - what do shortest-path trees reveal?
Energy Technology Data Exchange (ETDEWEB)
Thiemann, Christian [Eng. Sci. and Appl. Math, Northwestern University, Evanston, IL (United States); Max-Planck-Institute for Dynamics and Self-Organization, Goettingen (Germany); Grady, Daniel; Brockmann, Dirk [Eng. Sci. and Appl. Math, Northwestern University, Evanston, IL (United States)
2010-07-01
Similar to illustrating the anatomy of organs using pictures of tissue slices taken at various depths, we construct shortest-path trees of different nodes to create tomograms of large-scale human mobility networks. This tomography allows us to measure global properties of the system conditioned on a reference location in the network to gain a fuller characterization of a node. It also suggests a canonical coordinate system for representing complex networks and dynamical processes thereon in a simplified way, revealing a new symmetry in the human mobility networks we investigated. Furthermore, introducing the notion of tree similarity, we devised a new technique for clustering nodes with similar topological footprint, yielding a unique and efficient method for community identification and topological backbone extraction. We applied these methods to a multi-scale human mobility network obtained from the dollar-bill-tracking site wheresgoerge.com and to the U.S. and world-wide air transportation network.
Directory of Open Access Journals (Sweden)
R. Latha
Full Text Available Nowadays, Wireless Body Area Network (WBAN is emerging very fast and so many new methods and algorithms are coming up for finding the optimal path for disseminating emergency messages. Ant Colony Optimization (ACO is one of the cultural algorithms for solving many hard problems such as Travelling Salesman Problem (TSP. ACO is a natural behaviour of ants, which work stochastically with the help of pheromone trails deposited in the shortest route to find their food. This optimization procedure involves adapting, positive feedback and inherent parallelism. Each ant will deposit certain amount of pheromone in the tour construction it makes searching for food. This type of communication is known as stigmetric communication. In addition, if a dense WBAN environment prevails, such as hospital, i.e. in the environment of overlapping WBAN, game formulation was introduced for analyzing the mixed strategy behaviour of WBAN. In this paper, the ant colony optimization approach to the travelling salesman problem was applied to the WBAN to determine the shortest route for sending emergency message to the doctor via sensor nodes; and also a static Bayesian game formulation with mixed strategy was analysed to enhance the network lifetime. Whenever the patient needs any critical care or any other medical issue arises, emergency messages will be created by the WBAN and sent to the doctor's destination. All the modes of communication were realized in a simulation environment using OMNet++. The authors investigated a balanced model of emergency message dissemination and network lifetime in WBAN using ACO and Bayesian game formulation. Keywords: Wireless body area network, Ant colony optimization, Bayesian game model, Sensor network, Message latency, Network lifetime
Spreading paths in partially observed social networks
Onnela, Jukka-Pekka; Christakis, Nicholas A.
2012-03-01
Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using static, structurally realistic social networks as platforms for simulations, we juxtapose three distinct paths: (1) the stochastic path taken by a simulated spreading process from source to target; (2) the topologically shortest path in the fully observed network, and hence the single most likely stochastic path, between the two nodes; and (3) the topologically shortest path in a partially observed network. In a sampled network, how closely does the partially observed shortest path (3) emulate the unobserved spreading path (1)? Although partial observation inflates the length of the shortest path, the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path. We find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path; in fact, partial observation may, counterintuitively, make the path seem shorter than it actually is.
Spreading paths in partially observed social networks.
Onnela, Jukka-Pekka; Christakis, Nicholas A
2012-03-01
Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using static, structurally realistic social networks as platforms for simulations, we juxtapose three distinct paths: (1) the stochastic path taken by a simulated spreading process from source to target; (2) the topologically shortest path in the fully observed network, and hence the single most likely stochastic path, between the two nodes; and (3) the topologically shortest path in a partially observed network. In a sampled network, how closely does the partially observed shortest path (3) emulate the unobserved spreading path (1)? Although partial observation inflates the length of the shortest path, the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path. We find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path; in fact, partial observation may, counterintuitively, make the path seem shorter than it actually is.
Optimal Tax Routing : Network Analysis of FDI Diversion
van 't Riet, Maarten; Lejour, Arjen
2017-01-01
The international corporate tax system is considered as a network and, just like for transportation, ‘shortest’ paths are computed, minimizing tax payments for multinational enterprises when repatriating profits. We include corporate income tax rates, withholding taxes on dividends, double tax
Energy Balance Routing Algorithm Based on Virtual MIMO Scheme for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Jianpo Li
2014-01-01
Full Text Available Wireless sensor networks are usually energy limited and therefore an energy-efficient routing algorithm is desired for prolonging the network lifetime. In this paper, we propose a new energy balance routing algorithm which has the following three improvements over the conventional LEACH algorithm. Firstly, we propose a new cluster head selection scheme by taking into consideration the remaining energy and the most recent energy consumption of the nodes and the entire network. In this way, the sensor nodes with smaller remaining energy or larger energy consumption will be much less likely to be chosen as cluster heads. Secondly, according to the ratio of remaining energy to distance, cooperative nodes are selected to form virtual MIMO structures. It mitigates the uneven distribution of clusters and the unbalanced energy consumption of the whole network. Thirdly, we construct a comprehensive energy consumption model, which can reflect more realistically the practical energy consumption. Numerical simulations analyze the influences of cooperative node numbers and cluster head node numbers on the network lifetime. It is shown that the energy consumption of the proposed routing algorithm is lower than the conventional LEACH algorithm and for the simulation example the network lifetime is prolonged about 25%.
Optimal Tax Routing: Network Analysis of FDI Diversion
van 't Riet, Maarten; Lejour, Arjen
2017-01-01
The international corporate tax system is considered as a network and, just like for transportation, ‘shortest’ paths are computed, minimizing tax payments for multinational enterprises when repatriating profits. We include corporate income tax rates, withholding taxes on dividends, double tax treaties and the double taxation relief methods. We find that treaty shopping leads to an average potential reduction of the tax burden on repatriated dividends of about 6 percentage points. Moreover, a...
Directory of Open Access Journals (Sweden)
Zhou Feng
2013-09-01
Full Text Available A based on Rapidly-exploring Random Tree(RRT and Particle Swarm Optimizer (PSO for path planning of the robot is proposed.First the grid method is built to describe the working space of the mobile robot,then the Rapidly-exploring Random Tree algorithm is used to obtain the global navigation path,and the Particle Swarm Optimizer algorithm is adopted to get the better path.Computer experiment results demonstrate that this novel algorithm can plan an optimal path rapidly in a cluttered environment.The successful obstacle avoidance is achieved,and the model is robust and performs reliably.
International Nuclear Information System (INIS)
Fonda, James W; Zawodniok, Maciej; Jagannathan, S; Watkins, Steve E
2008-01-01
The development and the implementation issues of a reactive optimized energy-delay sub-network routing (OEDSR) protocol for wireless sensor networks (WSN) are introduced and its performance is contrasted with the popular ad hoc on-demand distance vector (AODV) routing protocol. Analytical results illustrate the performance of the proposed OEDSR protocol, while experimental results utilizing a hardware testbed under various scenarios demonstrate improvements in energy efficiency of the OEDSR protocol. A hardware platform constructed at the University of Missouri-Rolla (UMR), now the Missouri University of Science and Technology (MST), based on the Generation 4 Smart Sensor Node (G4-SSN) prototyping platform is also described. Performance improvements are shown in terms of end-to-end (E2E) delay, throughput, route-set-up time and drop rates and energy usage is given for three topologies, including a mobile topology. Additionally, results from the hardware testbed provide valuable lessons for network deployments. Under testing OEDSR provides a factor of ten improvement in the energy used in the routing session and extends network lifetime compared to AODV. Depletion experiments show that the time until the first node failure is extended by a factor of three with the network depleting and network lifetime is extended by 6.7%
Directory of Open Access Journals (Sweden)
Yourong Chen
2017-01-01
Full Text Available To improve the lifetime of mobile sink-based wireless sensor networks and considering that data transmission delay and hops are limited in actual system, a lifetime optimization algorithm limited by data transmission delay and hops (LOA_DH for mobile sink-based wireless sensor networks is proposed. In LOA_DH, some constraints are analyzed, and an optimization model is proposed. Maximum capacity path routing algorithm is used to calculate the energy consumption of communication. Improved genetic algorithm which modifies individuals to meet all constraints is used to solve the optimization model. The optimal solution of sink node’s sojourn grid centers and sojourn times which maximizes network lifetime is obtained. Simulation results show that, in three node distribution scenes, LOA_DH can find the movement solution of sink node which covers all sensor nodes. Compared with MCP_RAND, MCP_GMRE, and EASR, the solution improves network lifetime and reduces average amount of node discarded data and average energy consumption of nodes.
A Novel Routing Algorithm for Vehicular Ad Hoc Networks
Directory of Open Access Journals (Sweden)
Raúl Aquino Santos
2008-01-01
Full Text Available Este trabajo examina la importancia de las redes inalámbricas ad hoc y el algoritmo de enrutamiento con inundación basada en grupos (LORA-CBF para la comunicación inter-vehicular con la finalidad de optimizar el flujo de tráfico e incrementar la seguridad en las autopistas. Se discute el algoritmo de enrutamiento LORA-CBF y se presentan los resultados de simulaciones realizadas en OPNET de una autopista con alta movilidad vehicular. Primero, el modelo de simulación propuesto se valida a pequeña escala con resultados experimentales. Posteriormente, se emplean simulaciones de nuestro modelo comparándolos con Ad Hoc On-Demand Distance Vector (AODV y Dynamic Source Routing (DSR. Finalmente, se emplea un modelo de tráfico microscópico desarrollado en OPNET para simular la movilidad de 250 vehículos en una autopista y se aplica el algoritmo de enrutamiento LORA-CBF en un escenario vehicular.
An auxiliary optimization method for complex public transit route network based on link prediction
Zhang, Lin; Lu, Jian; Yue, Xianfei; Zhou, Jialin; Li, Yunxuan; Wan, Qian
2018-02-01
Inspired by the missing (new) link prediction and the spurious existing link identification in link prediction theory, this paper establishes an auxiliary optimization method for public transit route network (PTRN) based on link prediction. First, link prediction applied to PTRN is described, and based on reviewing the previous studies, the summary indices set and its algorithms set are collected for the link prediction experiment. Second, through analyzing the topological properties of Jinan’s PTRN established by the Space R method, we found that this is a typical small-world network with a relatively large average clustering coefficient. This phenomenon indicates that the structural similarity-based link prediction will show a good performance in this network. Then, based on the link prediction experiment of the summary indices set, three indices with maximum accuracy are selected for auxiliary optimization of Jinan’s PTRN. Furthermore, these link prediction results show that the overall layout of Jinan’s PTRN is stable and orderly, except for a partial area that requires optimization and reconstruction. The above pattern conforms to the general pattern of the optimal development stage of PTRN in China. Finally, based on the missing (new) link prediction and the spurious existing link identification, we propose optimization schemes that can be used not only to optimize current PTRN but also to evaluate PTRN planning.
Optimization of multicast optical networks with genetic algorithm
Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng
2007-11-01
In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.
Akhtar, Mahmuda; Hannan, M A; Begum, R A; Basri, Hassan; Scavino, Edgar
2017-03-01
Waste collection is an important part of waste management that involves different issues, including environmental, economic, and social, among others. Waste collection optimization can reduce the waste collection budget and environmental emissions by reducing the collection route distance. This paper presents a modified Backtracking Search Algorithm (BSA) in capacitated vehicle routing problem (CVRP) models with the smart bin concept to find the best optimized waste collection route solutions. The objective function minimizes the sum of the waste collection route distances. The study introduces the concept of the threshold waste level (TWL) of waste bins to reduce the number of bins to be emptied by finding an optimal range, thus minimizing the distance. A scheduling model is also introduced to compare the feasibility of the proposed model with that of the conventional collection system in terms of travel distance, collected waste, fuel consumption, fuel cost, efficiency and CO 2 emission. The optimal TWL was found to be between 70% and 75% of the fill level of waste collection nodes and had the maximum tightness value for different problem cases. The obtained results for four days show a 36.80% distance reduction for 91.40% of the total waste collection, which eventually increases the average waste collection efficiency by 36.78% and reduces the fuel consumption, fuel cost and CO 2 emission by 50%, 47.77% and 44.68%, respectively. Thus, the proposed optimization model can be considered a viable tool for optimizing waste collection routes to reduce economic costs and environmental impacts. Copyright © 2017 Elsevier Ltd. All rights reserved.
An Efficient Addressing Scheme and Its Routing Algorithm for a Large-Scale Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Choi Jeonghee
2008-01-01
Full Text Available Abstract So far, various addressing and routing algorithms have been extensively studied for wireless sensor networks (WSNs, but many of them were limited to cover less than hundreds of sensor nodes. It is largely due to stringent requirements for fully distributed coordination among sensor nodes, leading to the wasteful use of available address space. As there is a growing need for a large-scale WSN, it will be extremely challenging to support more than thousands of nodes, using existing standard bodies. Moreover, it is highly unlikely to change the existing standards, primarily due to backward compatibility issue. In response, we propose an elegant addressing scheme and its routing algorithm. While maintaining the existing address scheme, it tackles the wastage problem and achieves no additional memory storage during a routing. We also present an adaptive routing algorithm for location-aware applications, using our addressing scheme. Through a series of simulations, we prove that our approach can achieve two times lesser routing time than the existing standard in a ZigBee network.
An Efficient Addressing Scheme and Its Routing Algorithm for a Large-Scale Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Yongwan Park
2008-12-01
Full Text Available So far, various addressing and routing algorithms have been extensively studied for wireless sensor networks (WSNs, but many of them were limited to cover less than hundreds of sensor nodes. It is largely due to stringent requirements for fully distributed coordination among sensor nodes, leading to the wasteful use of available address space. As there is a growing need for a large-scale WSN, it will be extremely challenging to support more than thousands of nodes, using existing standard bodies. Moreover, it is highly unlikely to change the existing standards, primarily due to backward compatibility issue. In response, we propose an elegant addressing scheme and its routing algorithm. While maintaining the existing address scheme, it tackles the wastage problem and achieves no additional memory storage during a routing. We also present an adaptive routing algorithm for location-aware applications, using our addressing scheme. Through a series of simulations, we prove that our approach can achieve two times lesser routing time than the existing standard in a ZigBee network.
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2013-01-01
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 it 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...
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.
National Research Council Canada - National Science Library
Rodin, Ervin Y
2005-01-01
The purpose of this present research was to develop a generic model and methodology for analyzing and optimizing large-scale air transportation networks including both their routing and their scheduling...
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M. A. Karakuts
2015-01-01
Full Text Available The basic problems of route network and aircraft fleet optimization and its role in airline strategic planning are considered. Measures to improve the methods of its implementation are proposed.
Wang, Jin; Li, Bin; Xia, Feng; Kim, Chang-Seob; Kim, Jeong-Uk
2014-08-18
Traffic patterns in wireless sensor networks (WSNs) usually follow a many-to-one model. Sensor nodes close to static sinks will deplete their limited energy more rapidly than other sensors, since they will have more data to forward during multihop transmission. This will cause network partition, isolated nodes and much shortened network lifetime. Thus, how to balance energy consumption for sensor nodes is an important research issue. In recent years, exploiting sink mobility technology in WSNs has attracted much research attention because it can not only improve energy efficiency, but prolong network lifetime. In this paper, we propose an energy efficient distance-aware routing algorithm with multiple mobile sink for WSNs, where sink nodes will move with a certain speed along the network boundary to collect monitored data. We study the influence of multiple mobile sink nodes on energy consumption and network lifetime, and we mainly focus on the selection of mobile sink node number and the selection of parking positions, as well as their impact on performance metrics above. We can see that both mobile sink node number and the selection of parking position have important influence on network performance. Simulation results show that our proposed routing algorithm has better performance than traditional routing ones in terms of energy consumption.
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Jin Wang
2014-08-01
Full Text Available Traffic patterns in wireless sensor networks (WSNs usually follow a many-to-one model. Sensor nodes close to static sinks will deplete their limited energy more rapidly than other sensors, since they will have more data to forward during multihop transmission. This will cause network partition, isolated nodes and much shortened network lifetime. Thus, how to balance energy consumption for sensor nodes is an important research issue. In recent years, exploiting sink mobility technology in WSNs has attracted much research attention because it can not only improve energy efficiency, but prolong network lifetime. In this paper, we propose an energy efficient distance-aware routing algorithm with multiple mobile sink for WSNs, where sink nodes will move with a certain speed along the network boundary to collect monitored data. We study the influence of multiple mobile sink nodes on energy consumption and network lifetime, and we mainly focus on the selection of mobile sink node number and the selection of parking positions, as well as their impact on performance metrics above. We can see that both mobile sink node number and the selection of parking position have important influence on network performance. Simulation results show that our proposed routing algorithm has better performance than traditional routing ones in terms of energy consumption.
Genetic Algorithm Optimized Neural Networks Ensemble as ...
African Journals Online (AJOL)
NJD
Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous ... process by training a number of neural networks. .... Matlab® version 6.1 was employed for building principal component ... provide a fair simulation of calibration data set with some degree.
WDM Multicast Tree Construction Algorithms and Their Comparative Evaluations
Makabe, Tsutomu; Mikoshi, Taiju; Takenaka, Toyofumi
We propose novel tree construction algorithms for multicast communication in photonic networks. Since multicast communications consume many more link resources than unicast communications, effective algorithms for route selection and wavelength assignment are required. We propose a novel tree construction algorithm, called the Weighted Steiner Tree (WST) algorithm and a variation of the WST algorithm, called the Composite Weighted Steiner Tree (CWST) algorithm. Because these algorithms are based on the Steiner Tree algorithm, link resources among source and destination pairs tend to be commonly used and link utilization ratios are improved. Because of this, these algorithms can accept many more multicast requests than other multicast tree construction algorithms based on the Dijkstra algorithm. However, under certain delay constraints, the blocking characteristics of the proposed Weighted Steiner Tree algorithm deteriorate since some light paths between source and destinations use many hops and cannot satisfy the delay constraint. In order to adapt the approach to the delay-sensitive environments, we have devised the Composite Weighted Steiner Tree algorithm comprising the Weighted Steiner Tree algorithm and the Dijkstra algorithm for use in a delay constrained environment such as an IPTV application. In this paper, we also give the results of simulation experiments which demonstrate the superiority of the proposed Composite Weighted Steiner Tree algorithm compared with the Distributed Minimum Hop Tree (DMHT) algorithm, from the viewpoint of the light-tree request blocking.
DEFF Research Database (Denmark)
Jakobsen, Mikkel Koefoed; Madsen, Jan; Hansen, Michael Reichhardt
2010-01-01
One of the key design goals in Wireless Sensor Networks is long lasting or even continuous operation. Continuous operation is made possible through energy harvesting. Keeping the network operational imposes a demand to prevent network segmentation and power loss in nodes. It is therefore important...... that the best energy-wise route is found for each data transfer from a source node to the sink node. We present a new adaptive and distributed routing algorithm for finding energy optimised routes in a wireless sensor network with energy harvesting. The algorithm finds an energy efficient route from each source...
International Nuclear Information System (INIS)
Zamirian, M.; Kamyad, A.V.; Farahi, M.H.
2009-01-01
In this Letter a new approach for solving optimal path planning problems for a single rigid and free moving object in a two and three dimensional space in the presence of stationary or moving obstacles is presented. In this approach the path planning problems have some incompatible objectives such as the length of path that must be minimized, the distance between the path and obstacles that must be maximized and etc., then a multi-objective dynamic optimization problem (MODOP) is achieved. Considering the imprecise nature of decision maker's (DM) judgment, these multiple objectives are viewed as fuzzy variables. By determining intervals for the values of these fuzzy variables, flexible monotonic decreasing or increasing membership functions are determined as the degrees of satisfaction of these fuzzy variables on their intervals. Then, the optimal path planning policy is searched by maximizing the aggregated fuzzy decision values, resulting in a fuzzy multi-objective dynamic optimization problem (FMODOP). Using a suitable t-norm, the FMODOP is converted into a non-linear dynamic optimization problem (NLDOP). By using parametrization method and some calculations, the NLDOP is converted into the sequence of conventional non-linear programming problems (NLPP). It is proved that the solution of this sequence of the NLPPs tends to a Pareto optimal solution which, among other Pareto optimal solutions, has the best satisfaction of DM for the MODOP. Finally, the above procedure as a novel algorithm integrating parametrization method and fuzzy aggregation to solve the MODOP is proposed. Efficiency of our approach is confirmed by some numerical examples.
Power Balance AODV Routing Algorithm of WSN in Precision Agriculture Environment Monitoring
Directory of Open Access Journals (Sweden)
Xiaoqin Qin
2013-11-01
Full Text Available As one of important technologies of IOT (Internet of Things, WSN (Wireless Sensor Networks has been widely used in precision agriculture environment monitoring. WSN is a kind of energy-constrained network, but power balance is not taken into account in traditional routing protocols. A novel routing algorithm, named Power Balance Ad hoc On-Demand Distance Vector (PB-AODV, is proposed on cross-layer design. In the route discovery process of PB-AODV, routing path is established by the Received Signal Strength Indication (RSSI value. The optimal transmitting power, which is computed according to RSSI value, power threshold and node’s surplus energy, is encapsulated into Route Reply Packet. Hence, the sender node can adjust its transmission power to save energy according to the Route Reply Packet. Simulation results show that the proposed algorithm is effective for load balancing, and increases the WSN’s lifetime 14.3% consequently.
Genetic Algorithm Optimized Neural Networks Ensemble as ...
African Journals Online (AJOL)
Marquardt algorithm by varying conditions such as inputs, hidden neurons, initialization, training sets and random Gaussian noise injection to ... Several such ensembles formed the population which was evolved to generate the fittest ensemble.
Optimal Route Searching with Multiple Dynamical Constraints—A Geometric Algebra Approach
Directory of Open Access Journals (Sweden)
Dongshuang Li
2018-05-01
Full Text Available The process of searching for a dynamic constrained optimal path has received increasing attention in traffic planning, evacuation, and personalized or collaborative traffic service. As most existing multiple constrained optimal path (MCOP methods cannot search for a path given various types of constraints that dynamically change during the search, few approaches for dynamic multiple constrained optimal path (DMCOP with type II dynamics are available for practical use. In this study, we develop a method to solve the DMCOP problem with type II dynamics based on the unification of various types of constraints under a geometric algebra (GA framework. In our method, the network topology and three different types of constraints are represented by using algebraic base coding. With a parameterized optimization of the MCOP algorithm based on a greedy search strategy under the generation-refinement paradigm, this algorithm is found to accurately support the discovery of optimal paths as the constraints of numerical values, nodes, and route structure types are dynamically added to the network. The algorithm was tested with simulated cases of optimal tourism route searches in China’s road networks with various combinations of constraints. The case study indicates that our algorithm can not only solve the DMCOP with different types of constraints but also use constraints to speed up the route filtering.
Competitive game theoretic optimal routing in optical networks
Yassine, Abdulsalam; Kabranov, Ognian; Makrakis, Dimitrios
2002-09-01
Optical transport service providers need control and optimization strategies for wavelength management, network provisioning, restoration and protection, allowing them to define and deploy new services and maintain competitiveness. In this paper, we investigate a game theory based model for wavelength and flow assignment in multi wavelength optical networks, consisting of several backbone long-haul optical network transport service providers (TSPs) who are offering their services -in terms of bandwidth- to Internet service providers (ISPs). The ISPs act as brokers or agents between the TSP and end user. The agent (ISP) buys services (bandwidth) from the TSP. The TSPs compete among themselves to sell their services and maintain profitability. We present a case study, demonstrating the impact of different bandwidth broker demands on the supplier's profit and the price paid by the network broker.
Efficient Routing in Wireless Sensor Networks with Multiple Sessions
Directory of Open Access Journals (Sweden)
Dianjie Lu
2014-05-01
Full Text Available Wireless Sensor Networks (WSNs are subject to node failures because of limited energy and link unreliability which makes the design of routing protocols in such networks a challenging task. The multipath routing scheme is an optimal alternative to address this problem which splits the traffic across multiple paths instead of routing all the traffic along a single path. However, using more paths introduces more contentions which degrade energy efficiency. The problem becomes even more difficult in the scenario of multiple sessions since different source-destination pairs may pass the same link which makes the flow distribution of each link uncertain. Our goal is to minimize the energy cost and provide the robust transmission by choosing the optimal paths. We first study the problem from a theoretical standpoint by mapping it to the multi-commodity network design problem. Since it is hard to build a global addressing scheme due to the great number of sensor nodes, we propose a Distributed Energy Efficient Routing protocol (D2ER. In D2ER, we employ the transportation method which can optimize the flow distribution with minimal energy consumption. Simulation results demonstrate that our optimal algorithm can save energy drastically.
Directory of Open Access Journals (Sweden)
Lijuan Xiang
2017-06-01
Full Text Available This paper identifies the Wireless Power Transfer Network (WPTN as an ideal model for long-distance Wireless Power Transfer (WPT in a certain region with multiple electrical equipment. The schematic circuit and design of each power node and the process of power transmission between the two power nodes are elaborated. The Improved Cross-Entropy (ICE method is proposed as an algorithm to solve for optimal energy route. Non-dominated sorting is introduced for optimization. A demonstration of the optimization result of a 30-nodes WPTN system based on the proposed algorithm proves ICE method to be efficacious and efficiency.
An energy efficient multiple mobile sinks based routing algorithm for wireless sensor networks
Zhong, Peijun; Ruan, Feng
2018-03-01
With the fast development of wireless sensor networks (WSNs), more and more energy efficient routing algorithms have been proposed. However, one of the research challenges is how to alleviate the hot spot problem since nodes close to static sink (or base station) tend to die earlier than other sensors. The introduction of mobile sink node can effectively alleviate this problem since sink node can move along certain trajectories, causing hot spot nodes more evenly distributed. In this paper, we mainly study the energy efficient routing method with multiple mobile sinks support. We divide the whole network into several clusters and study the influence of mobile sink number on network lifetime. Simulation results show that the best network performance appears when mobile sink number is about 3 under our simulation environment.
Route Optimization for the Distribution Network of a Confectionary Chain
Directory of Open Access Journals (Sweden)
Anıl İnanlı
2015-12-01
Full Text Available This study considers the distribution network of a well-known perishable food manufacturer and its franchises in Turkey. As the countrywide number of stores is increasing fast, the company is facing problems due to its central distribution of products from a single factory. The objective is to decrease the cost of transportation while maintaining a high level of customer satisfaction. Hence, the focus is on the vehicle routing problem (VRP of this large franchise chain within each city. The problem is defined as a rich VRP with heterogeneous fleet, site-dependent and compartmentalized vehicles, and soft/hard time windows. This NP-hard problem is modelled and tried with real data on a commercial solver. A basic heuristic procedure which can be used easily by the decision makers is also employed for obtaining quick and high-quality solutions for large instances.
A Hybrid Ant Colony Optimization Algorithm for the Extended Capacitated Arc Routing Problem.
Li-Ning Xing; Rohlfshagen, P; Ying-Wu Chen; Xin Yao
2011-08-01
The capacitated arc routing problem (CARP) is representative of numerous practical applications, and in order to widen its scope, we consider an extended version of this problem that entails both total service time and fixed investment costs. We subsequently propose a hybrid ant colony optimization (ACO) algorithm (HACOA) to solve instances of the extended CARP. This approach is characterized by the exploitation of heuristic information, adaptive parameters, and local optimization techniques: Two kinds of heuristic information, arc cluster information and arc priority information, are obtained continuously from the solutions sampled to guide the subsequent optimization process. The adaptive parameters ease the burden of choosing initial values and facilitate improved and more robust results. Finally, local optimization, based on the two-opt heuristic, is employed to improve the overall performance of the proposed algorithm. The resulting HACOA is tested on four sets of benchmark problems containing a total of 87 instances with up to 140 nodes and 380 arcs. In order to evaluate the effectiveness of the proposed method, some existing capacitated arc routing heuristics are extended to cope with the extended version of this problem; the experimental results indicate that the proposed ACO method outperforms these heuristics.
Efficient Routing in Wireless Sensor Networks with Multiple Sessions
Dianjie Lu; Guijuan Zhang; Ren Han; Xiangwei Zheng; Hong Liu
2014-01-01
Wireless Sensor Networks (WSNs) are subject to node failures because of limited energy and link unreliability which makes the design of routing protocols in such networks a challenging task. The multipath routing scheme is an optimal alternative to address this problem which splits the traffic across multiple paths instead of routing all the traffic along a single path. However, using more paths introduces more contentions which degrade energy efficiency. The problem becomes even more difficu...
A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization.
Ayari, Asma; Bouamama, Sadok
2017-01-01
Multiple robot systems have become a major study concern in the field of robotic research. Their control becomes unreliable and even infeasible if the number of robots increases. In this paper, a new dynamic distributed particle swarm optimization (D 2 PSO) algorithm is proposed for trajectory path planning of multiple robots in order to find collision-free optimal path for each robot in the environment. The proposed approach consists in calculating two local optima detectors, LOD pBest and LOD gBest . Particles which are unable to improve their personal best and global best for predefined number of successive iterations would be replaced with restructured ones. Stagnation and local optima problems would be avoided by adding diversity to the population, without losing the fast convergence characteristic of PSO. Experiments with multiple robots are provided and proved effectiveness of such approach compared with the distributed PSO.
Directory of Open Access Journals (Sweden)
Muhammad Aslam
2017-01-01
Full Text Available Effective utilization of energy resources in Wireless Sensor Networks (WSNs has become challenging under uncertain distributed cluster-formation and single-hop intercluster communication capabilities. So, sensor nodes are forced to operate at expensive full rate transmission power level continuously during whole network operation. These challenging network environments experience unwanted phenomena of drastic energy consumption and packet drop. In this paper, we propose an adaptive immune Multihopping Multilevel Clustering (MHMLC protocol that executes a Hybrid Clustering Algorithm (HCA to perform optimal centralized selection of Cluster-Heads (CHs within radius of centrally located Base Station (BS and distributed CHs selection in the rest of network area. HCA of MHMLC also produces optimal intermediate CHs for intercluster multihop communications that develop heterogeneity-aware economical links. This hybrid cluster-formation facilitates the sensors to function at short range transmission power level that enhances link quality and avoids packet drop. The simulation environments produce fair comparison among proposed MHMLC and existing state-of-the-art routing protocols. Experimental results give significant evidence of better performance of the proposed model in terms of network lifetime, stability period, and data delivery ratio.
Directory of Open Access Journals (Sweden)
T. Nakamura
2012-07-01
Full Text Available Nowadays, for the estimation of traffic demand or people flow, modelling route choice activity in road networks is an important task and many algorithms have been developed to generate route choice sets. However, developing an algorithm based on a small amount of data that can be applied generally within a metropolitan area is difficult. This is because the characteristics of road networks vary widely. On the other hand, recently, the collection of people movement data has lately become much easier, especially through mobile phones. Lately, most mobile phones include GPS functionality. Given this background, we propose a data-oriented algorithm to generate route choice sets using mobile phone GPS data. GPS data contain a number of measurement errors; hence, they must be adjusted to account for these errors before use in advanced people movement analysis. However, this is time-consuming and expensive, because an enormous amount of daily data can be obtained. Hence, the objective of this study is to develop an algorithm that can easily manage GPS data. Specifically, at first movement data from all GPS data are selected by calculating the speed. Next, the nearest roads in the road network are selected from the GPS location and count such data for each road. Then An algorithm based on the GSP (Gateway Shortest Path algorithm is proposed, which searches the shortest path through a given gateway. In the proposed algorithm, the road for which the utilization volume calculated by GPS data is large is selected as the gateway. Thus, route choice sets that are based on trends in real GPS data are generated. To evaluate the proposed method, GPS data from 0.7 million people a year in Japan and DRM (Digital Road Map as the road network are used. DRM is one of the most detailed road networks in Japan. Route choice sets using the proposed algorithm are generated and the cover rate of the utilization volume of each road under evaluation is calculated. As a
Optimization of composite panels using neural networks and genetic algorithms
Ruijter, W.; Spallino, R.; Warnet, Laurent; de Boer, Andries
2003-01-01
The objective of this paper is to present first results of a running study on optimization of aircraft components (composite panels of a typical vertical tail plane) by using Genetic Algorithms (GA) and Neural Networks (NN). The panels considered are standardized to some extent but still there is a
Improved Differential Evolution Algorithm for Wireless Sensor Network Coverage Optimization
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
De-Xin Yu
2013-01-01
Full Text Available Combined with improved Pallottino parallel algorithm, this paper proposes a large-scale route search method, which considers travelers’ route choice preferences. And urban road network is decomposed into multilayers effectively. Utilizing generalized travel time as road impedance function, the method builds a new multilayer and multitasking road network data storage structure with object-oriented class definition. Then, the proposed path search algorithm is verified by using the real road network of Guangzhou city as an example. By the sensitive experiments, we make a comparative analysis of the proposed path search method with the current advanced optimal path algorithms. The results demonstrate that the proposed method can increase the road network search efficiency by more than 16% under different search proportion requests, node numbers, and computing process numbers, respectively. Therefore, this method is a great breakthrough in the guidance field of urban road network.
Bhanot, Gyan [Princeton, NJ; Blumrich, Matthias A [Ridgefield, CT; Chen, Dong [Croton On Hudson, NY; Coteus, Paul W [Yorktown Heights, NY; Gara, Alan G [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Steinmacher-Burow, Burkhard D [Mount Kisco, NY; Takken, Todd E [Mount Kisco, NY; Vranas, Pavlos M [Bedford Hills, NY
2009-09-08
Class network routing is implemented in a network such as a computer network comprising a plurality of parallel compute processors at nodes thereof. Class network routing allows a compute processor to broadcast a message to a range (one or more) of other compute processors in the computer network, such as processors in a column or a row. Normally this type of operation requires a separate message to be sent to each processor. With class network routing pursuant to the invention, a single message is sufficient, which generally reduces the total number of messages in the network as well as the latency to do a broadcast. Class network routing is also applied to dense matrix inversion algorithms on distributed memory parallel supercomputers with hardware class function (multicast) capability. This is achieved by exploiting the fact that the communication patterns of dense matrix inversion can be served by hardware class functions, which results in faster execution times.
Improved routing strategies for data traffic in scale-free networks
International Nuclear Information System (INIS)
Wu, Zhi-Xi; Peng, Gang; Wong, Wing-Ming; Yeung, Kai-Hau
2008-01-01
We study the information packet routing process in scale-free networks by mimicking Internet traffic delivery. We incorporate both the global shortest paths information and local degree information of the network in the dynamic process, via two tunable parameters, α and β, to guide the packet routing. We measure the performance of the routing method by both the average transit times of packets and the critical packet generation rate (above which packet aggregation occurs in the network). We found that the routing strategies which integrate ingredients of both global and local topological information of the underlying networks perform much better than the traditional shortest path routing protocol taking into account the global topological information only. Moreover, by doing comparative studies with some related works, we found that the performance of our proposed method shows universal efficiency characteristic against the amount of traffic
An adaptive routing strategy for packet delivery in complex networks
International Nuclear Information System (INIS)
Zhang, Huan; Liu, Zonghua; Tang, Ming; Hui, P.M.
2007-01-01
We present an efficient routing approach for delivering packets in complex networks. On delivering a message from a node to a destination, a node forwards the message to a neighbor by estimating the waiting time along the shortest path from each of its neighbors to the destination. This projected waiting time is dynamical in nature and the path through which a message is delivered would be adapted to the distribution of messages in the network. Implementing the approach on scale-free networks, we show that the present approach performs better than the shortest-path approach and another approach that takes into account of the waiting time only at the neighboring nodes. Key features in numerical results are explained by a mean field theory. The approach has the merit that messages are distributed among the nodes according to the capabilities of the nodes in handling messages
Algorithms for optimization of branching gravity-driven water networks
Dardani, Ian; Jones, Gerard F.
2018-05-01
The design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the context of gravity-driven water networks (GDWNs), this work assesses three cost-minimization algorithms on six moderate-scale GDWN test cases. Two algorithms, a backtracking algorithm and a genetic algorithm, use a set of discrete pipe diameters, while a new calculus-based algorithm produces a continuous-diameter solution which is mapped onto a discrete-diameter set. The backtracking algorithm finds the global optimum for all but the largest of cases tested, for which its long runtime makes it an infeasible option. The calculus-based algorithm's discrete-diameter solution produced slightly higher-cost results but was more scalable to larger network cases. Furthermore, the new calculus-based algorithm's continuous-diameter and mapped solutions provided lower and upper bounds, respectively, on the discrete-diameter global optimum cost, where the mapped solutions were typically within one diameter size of the global optimum. The genetic algorithm produced solutions even closer to the global optimum with consistently short run times, although slightly higher solution costs were seen for the larger network cases tested. The results of this study highlight the advantages and weaknesses of each GDWN design method including closeness to the global optimum, the ability to prune the solution space of infeasible and suboptimal candidates without missing the global optimum, and algorithm run time. We also extend an existing closed-form model of Jones (2011) to include minor losses and a more comprehensive two-part cost model, which realistically applies to pipe sizes that span a broad range typical of GDWNs of interest in this work, and for smooth and commercial steel roughness values.
Algorithms for optimization of branching gravity-driven water networks
Directory of Open Access Journals (Sweden)
I. Dardani
2018-05-01
Full Text Available The design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the context of gravity-driven water networks (GDWNs, this work assesses three cost-minimization algorithms on six moderate-scale GDWN test cases. Two algorithms, a backtracking algorithm and a genetic algorithm, use a set of discrete pipe diameters, while a new calculus-based algorithm produces a continuous-diameter solution which is mapped onto a discrete-diameter set. The backtracking algorithm finds the global optimum for all but the largest of cases tested, for which its long runtime makes it an infeasible option. The calculus-based algorithm's discrete-diameter solution produced slightly higher-cost results but was more scalable to larger network cases. Furthermore, the new calculus-based algorithm's continuous-diameter and mapped solutions provided lower and upper bounds, respectively, on the discrete-diameter global optimum cost, where the mapped solutions were typically within one diameter size of the global optimum. The genetic algorithm produced solutions even closer to the global optimum with consistently short run times, although slightly higher solution costs were seen for the larger network cases tested. The results of this study highlight the advantages and weaknesses of each GDWN design method including closeness to the global optimum, the ability to prune the solution space of infeasible and suboptimal candidates without missing the global optimum, and algorithm run time. We also extend an existing closed-form model of Jones (2011 to include minor losses and a more comprehensive two-part cost model, which realistically applies to pipe sizes that span a broad range typical of GDWNs of interest in this work, and for smooth and commercial steel
Using neural networks to speed up optimization algorithms
Bazan, M
2000-01-01
The paper presents the application of radial-basis-function (RBF) neural networks to speed up deterministic search algorithms used for the design and optimization of superconducting LHC magnets. The optimization of the iron yoke of the main dipoles requires a number of numerical field computations per trial solution as the field quality depends on the excitation of the magnets. This results in computation times of about 30 minutes for each objective function evaluation (on a DEC-Alpha 600/333) and only the most robust (deterministic) optimization algorithms can be applied. Using a RBF function approximator, the achieved speed-up of the search algorithm is in the order of 25% for problems with two parameters and about 18% for problems with three and five design variables. (13 refs).
Directory of Open Access Journals (Sweden)
Ling Yongfa
2016-01-01
Full Text Available The paper proposes a mobile control sink node data collection method in the wireless sensor network based on compressive sensing. This method, with regular track, selects the optimal data collection points in the monitoring area via the disc method, calcu-lates the shortest path by using the quantum genetic algorithm, and hence determines the data collection route. Simulation results show that this method has higher network throughput and better energy efficiency, capable of collecting a huge amount of data with balanced energy consumption in the network.
A Random Riemannian Metric for Probabilistic Shortest-Path Tractography
DEFF Research Database (Denmark)
Hauberg, Søren; Schober, Michael; Liptrot, Matthew George
2015-01-01
of the diffusion tensor as a “random Riemannian metric”, where a geodesic is a distribution over tracts. We approximate this distribution with a Gaussian process and present a probabilistic numerics algorithm for computing the geodesic distribution. We demonstrate SPT improvements on data from the Human Connectome...
Optimization of IBF parameters based on adaptive tool-path algorithm
Deng, Wen Hui; Chen, Xian Hua; Jin, Hui Liang; Zhong, Bo; Hou, Jin; Li, An Qi
2018-03-01
As a kind of Computer Controlled Optical Surfacing(CCOS) technology. Ion Beam Figuring(IBF) has obvious advantages in the control of surface accuracy, surface roughness and subsurface damage. The superiority and characteristics of IBF in optical component processing are analyzed from the point of view of removal mechanism. For getting more effective and automatic tool path with the information of dwell time, a novel algorithm is proposed in this thesis. Based on the removal functions made through our IBF equipment and the adaptive tool-path, optimized parameters are obtained through analysis the residual error that would be created in the polishing process. A Φ600 mm plane reflector element was used to be a simulation instance. The simulation result shows that after four combinations of processing, the surface accuracy of PV (Peak Valley) value and the RMS (Root Mean Square) value was reduced to 4.81 nm and 0.495 nm from 110.22 nm and 13.998 nm respectively in the 98% aperture. The result shows that the algorithm and optimized parameters provide a good theoretical for high precision processing of IBF.
Minimizing Communication in All-Pairs Shortest Paths
2013-02-13
on a 16,384 vertex, 5% dense graph, is slightly faster using our approach (18.6 vs . 22.6 seconds) than using the replicated Johnson’s algorithm...Oracle and Samsung , as well as MathWorks. Research is also supported by DOE grants DE-SC0004938, DE-SC0005136, DE-SC0003959, DE-SC0008700, and AC02...Brickell, I. S. Dhillon, S. Sra, and J. A. Tropp. The metric nearness problem. SIAM J. Matrix Anal. Appl ., 30:375–396, 2008. [11] A. Buluç, J. R. Gilbert
District Heating Network Design and Configuration Optimization with Genetic Algorithm
DEFF Research Database (Denmark)
Li, Hongwei; Svendsen, Svend
2011-01-01
In this paper, the configuration of a district heating (DH) network which connects from the heating plant to the end users was optimized with emphasizing the network thermal performance. Each end user in the network represents a building block. The locations of the building blocks are fixed while...... 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...... by multi factors as the consumer heating load, the distance between the heating plant to the consumer, the design criteria regarding pressure and temperature limitation, as well as the corresponding network heat loss....
Optimal Operation of Interdependent Power Systems and Electrified Transportation Networks
Directory of Open Access Journals (Sweden)
M. Hadi Amini
2018-01-01
Full Text Available Electrified transportation and power systems are mutually coupled networks. In this paper, a novel framework is developed for interdependent power and transportation networks. Our approach constitutes solving an iterative least cost vehicle routing process, which utilizes the communication of electrified vehicles (EVs with competing charging stations, to exchange data such as electricity price, energy demand, and time of arrival. The EV routing problem is solved to minimize the total cost of travel using the Dijkstra algorithm with the input from EVs battery management system, electricity price from charging stations, powertrain component efficiencies and transportation network traffic conditions. Through the bidirectional communication of EVs with competing charging stations, EVs’ charging demand estimation is done much more accurately. Then the optimal power flow problem is solved for the power system, to find the locational marginal price at load buses where charging stations are connected. Finally, the electricity prices were communicated from the charging stations to the EVs, and the loop is closed. Locational electricity price acts as the shared parameter between the two optimization problems, i.e., optimal power flow and optimal routing problem. Electricity price depends on the power demand, which is affected by the charging of EVs. On the other hand, location of EV charging stations and their different pricing strategies might affect the routing decisions of the EVs. Our novel approach that combines the electrified transportation with power system operation, holds tremendous potential for solving electrified transportation issues and reducing energy costs. The effectiveness of the proposed approach is demonstrated using Shanghai transportation network and IEEE 9-bus test system. The results verify the cost-savings for both power system and transportation networks.
Optimizing well intervention routes
Energy Technology Data Exchange (ETDEWEB)
Paiva, Ronaldo O. [PETROBRAS S.A., Vitoria, ES (Brazil); Schiozer, Denis J.; Bordalo, Sergio N. [Universidade Estadual de Campinas, SP (Brazil). Faculdade de Engenharia Mecanica. Centro de Estudo do Petroleo (CEPETRO)]. E-mail: denis@dep.fem.unicamp.br; bordalo@dep.fem.unicamp.br
2000-07-01
This work presents a method for optimizing the itinerary of work over rigs, i.e., the search for the route of minimum total cost, and demonstrates the importance of the dynamics of reservoir behaviour. The total cost of a route includes the rig expenses (transport, assembly and operation), which are functions of time and distances, plus the losses of revenue in wells waiting for the rig, which are also dependent of time. A reservoir simulator is used to evaluate the monetary influence of the well shutdown on the present value of the production curve. Finally, search algorithms are employed to determine the route of minimal cost. The Simulated Annealing algorithm was also successful in optimizing the distribution of a list of wells among different work over rigs. The rational approach presented here is recommended for management teams as a standard procedure to define the priority of wells scheduled for work over. (author)
An improved hierarchical A * algorithm in the optimization of parking lots
Wang, Yong; Wu, Junjuan; Wang, Ying
2017-08-01
In the parking lot parking path optimization, the traditional evaluation index is the shortest distance as the best index and it does not consider the actual road conditions. Now, the introduction of a more practical evaluation index can not only simplify the hardware design of the boot system but also save the software overhead. Firstly, we establish the parking lot network graph RPCDV mathematical model and all nodes in the network is divided into two layers which were constructed using different evaluation function base on the improved hierarchical A * algorithm which improves the time optimal path search efficiency and search precision of the evaluation index. The final results show that for different sections of the program attribute parameter algorithm always faster the time to find the optimal path.
Directory of Open Access Journals (Sweden)
Sridhar Iyer
2017-11-01
Full Text Available The spectrally efficient transportation of the high bit rate(s data is achievable by the Elastic optical networks (EONs. However, in the EONs, owing to the failure occurrence even in an individual simple element, different service(s maybe interrupted. Hence, it is imperative that the schemes for survivability be developed so that the issues due to the possible failure(s can be overcome. In the current work, in view of survivability of the link failure(s in the EONs, we propose the Spectrum Continuity and Contiguity Established DRP (SCC-E-DRP algorithm which is a novel dedicated route protection (DRP scheme that attempts to avoid the problem of trap topology during its exploration for a pair of link disjoint path. Further, to evaluate the link disjoint paths, we resort to the use of the SCC Established Shortest Route (SCC-E-SR algorithm which is a modified Dijkstra’s algorithm based scheme that selects the path(s pair(s based on the end-toend SCC. We conduct extensive simulations considering realistic network topologies, and compare the performance of the SCCE-DRP scheme with the existing techniques. The obtained results show that, compared to the existing schemes, the SCC-E-DRP scheme achieves better results in terms of blocking probability.
Abd El–Naser A. Mohammed; Ahmed Nabih Zaki Rashed; Osama S. Fragallah; Mohamed G. El-Abyad
2013-01-01
In simple wavelength-division multiplexed (WDM) networks, a connection must be established along a route using a common wavelength on all of the links along the route. The introduction of wavelength converters into WDM cross connects increases the hardware cost and complexity. Given a set of connection requests, the routing and wavelength assignment problem involves finding a route (routing) and assigning a wavelength to each request. This paper has presented the WDM technology is being exten...
DESIGN OF ENERGY EFFICIENT ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORK (WSN) USING PASCAL GRAPH
Deepali Panwar; Subhrendu Guha Neogi
2013-01-01
Development of energy efficient Wireless Sensor Network (WSN) routing protocol is nowadays main area of interest amongst researchers. This research is an effort in designing energy efficient Wireless Sensor Network (WSN) routing protocol under certain parameters consideration. Research report discusses various existing WSN routing protocols and propose a new WSN energy efficient routing protocol. Results show a significant improvement in life cycle of the nodes and enhancement ...
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)
Farzad Kiani
2016-01-01
Full Text Available Energy issue is one of the most important problems in wireless sensor networks. They consist of low-power sensor nodes and a few base station nodes. They must be adaptive and efficient in data transmission to sink in various areas. This paper proposes an aware-routing protocol based on clustering and recursive search approaches. The paper focuses on the energy efficiency issue with various measures such as prolonging network lifetime along with reducing energy consumption in the sensor nodes and increasing the system reliability. Our proposed protocol consists of two phases. In the first phase (network development phase, the sensors are placed into virtual layers. The second phase (data transmission is related to routes discovery and data transferring so it is based on virtual-based Classic-RBFS algorithm in the lake of energy problem environments but, in the nonchargeable environments, all nodes in each layer can be modeled as a random graph and then begin to be managed by the duty cycle method. Additionally, the protocol uses new topology control, data aggregation, and sleep/wake-up schemas for energy saving in the network. The simulation results show that the proposed protocol is optimal in the network lifetime and packet delivery parameters according to the present protocols.
A self-optimizing scheme for energy balanced routing in Wireless Sensor Networks using SensorAnt.
Shamsan Saleh, Ahmed M; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A; Ismail, Alyani
2012-01-01
Planning of energy-efficient protocols is critical for Wireless Sensor Networks (WSNs) because of the constraints on the sensor nodes' energy. The routing protocol should be able to provide uniform power dissipation during transmission to the sink node. In this paper, we present a self-optimization scheme for WSNs which is able to utilize and optimize the sensor nodes' resources, especially the batteries, to achieve balanced energy consumption across all sensor nodes. This method is based on the Ant Colony Optimization (ACO) metaheuristic which is adopted to enhance the paths with the best quality function. The assessment of this function depends on multi-criteria metrics such as the minimum residual battery power, hop count and average energy of both route and network. This method also distributes the traffic load of sensor nodes throughout the WSN leading to reduced energy usage, extended network life time and reduced packet loss. Simulation results show that our scheme performs much better than the Energy Efficient Ant-Based Routing (EEABR) in terms of energy consumption, balancing and efficiency.
Xu, Chuanpei; Niu, Junhao; Ling, Jing; Wang, Suyan
2018-03-01
In this paper, we present a parallel test strategy for bandwidth division multiplexing under the test access mechanism bandwidth constraint. The Pareto solution set is combined with a cloud evolutionary algorithm to optimize the test time and power consumption of a three-dimensional network-on-chip (3D NoC). In the proposed method, all individuals in the population are sorted in non-dominated order and allocated to the corresponding level. Individuals with extreme and similar characteristics are then removed. To increase the diversity of the population and prevent the algorithm from becoming stuck around local optima, a competition strategy is designed for the individuals. Finally, we adopt an elite reservation strategy and update the individuals according to the cloud model. Experimental results show that the proposed algorithm converges to the optimal Pareto solution set rapidly and accurately. This not only obtains the shortest test time, but also optimizes the power consumption of the 3D NoC.
Fuzzy Weight Cluster-Based Routing Algorithm for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Teng Gao
2015-01-01
Full Text Available Cluster-based protocol is a kind of important routing in wireless sensor networks. However, due to the uneven distribution of cluster heads in classical clustering algorithm, some nodes may run out of energy too early, which is not suitable for large-scale wireless sensor networks. In this paper, a distributed clustering algorithm based on fuzzy weighted attributes is put forward to ensure both energy efficiency and extensibility. On the premise of a comprehensive consideration of all attributes, the corresponding weight of each parameter is assigned by using the direct method of fuzzy engineering theory. Then, each node works out property value. These property values will be mapped to the time axis and be triggered by a timer to broadcast cluster headers. At the same time, the radio coverage method is adopted, in order to avoid collisions and to ensure the symmetrical distribution of cluster heads. The aggregated data are forwarded to the sink node in the form of multihop. The simulation results demonstrate that clustering algorithm based on fuzzy weighted attributes has a longer life expectancy and better extensibility than LEACH-like algorithms.
An energy-aware engineered control plane for wavelength-routed networks
DEFF Research Database (Denmark)
Ricciardi, Sergio; Wang, Jiayuan; Palmieri, Francesco
2015-01-01
' operational expenditures. To face this problem, we propose a single-stage routing and wavelength assignment scheme, based on several network engineering extensions to the Generalised Multi-Protocol Label Switching (GMPLS) control plane protocols, mainly Open Shortest Path First, with new composed metrics...
2015-01-01
solution approach that combines heuristic search and integer programming. Boudia et al. (2007) solved an SDVRP instance using a memetic algorithm with...Boudia, M., Prins, C., Reghioui, M., 2007. An effective memetic algorithm with population management for the split delivery vehicle routing problem
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.
Joo, Seong-Soon; Nam, Hyun-Soon; Lim, Chang-Kyu
2003-08-01
With the rapid growth of the Optical Internet, high capacity pipes is finally destined to support end-to-end IP on the WDM optical network. Newly launched 2D MEMS optical switching module in the market supports that expectations of upcoming a transparent optical cross-connect in the network have encouraged the field applicable research on establishing real all-optical transparent network. To open up a customer-driven bandwidth services, design of the optical transport network becomes more challenging task in terms of optimal network resource usage. This paper presents a practical approach to finding a route and wavelength assignment for wavelength routed all-optical network, which has λ-plane OXC switches and wavelength converters, and supports that optical paths are randomly set up and released by dynamic wavelength provisioning to create bandwidth between end users with timescales on the order of seconds or milliseconds. We suggest three constraints to make the RWA problem become more practical one on deployment for wavelength routed all-optical network in network view: limitation on maximum hop of a route within bearable optical network impairments, limitation on minimum hops to travel before converting a wavelength, and limitation on calculation time to find all routes for connections requested at once. We design the NRCD (Normalized Resource and Constraints for All-Optical Network RWA Design) algorithm for the Tera OXC: network resource for a route is calculated by the number of internal switching paths established in each OXC nodes on the route, and is normalized by ratio of number of paths established and number of paths equipped in a node. We show that it fits for the RWA algorithm of the wavelength routed all-optical network through real experiments on the distributed objects platform.
Information communication on complex networks
International Nuclear Information System (INIS)
Igarashi, Akito; Kawamoto, Hiroki; Maruyama, Takahiro; Morioka, Atsushi; Naganuma, Yuki
2013-01-01
Since communication networks such as the Internet, which is regarded as a complex network, have recently become a huge scale and a lot of data pass through them, the improvement of packet routing strategies for transport is one of the most significant themes in the study of computer networks. It is especially important to find routing strategies which can bear as many traffic as possible without congestion in complex networks. First, using neural networks, we introduce a strategy for packet routing on complex networks, where path lengths and queue lengths in nodes are taken into account within a framework of statistical physics. Secondly, instead of using shortest paths, we propose efficient paths which avoid hubs, nodes with a great many degrees, on scale-free networks with a weight of each node. We improve the heuristic algorithm proposed by Danila et. al. which optimizes step by step routing properties on congestion by using the information of betweenness, the probability of paths passing through a node in all optimal paths which are defined according to a rule, and mitigates the congestion. We confirm the new heuristic algorithm which balances traffic on networks by achieving minimization of the maximum betweenness in much smaller number of iteration steps. Finally, We model virus spreading and data transfer on peer-to-peer (P2P) networks. Using mean-field approximation, we obtain an analytical formulation and emulate virus spreading on the network and compare the results with those of simulation. Moreover, we investigate the mitigation of information traffic congestion in the P2P networks.
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.
Hierarchical artificial bee colony algorithm for RFID network planning optimization.
Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong
2014-01-01
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.
Personal computer based decision support system for routing nuclear spent fuel
International Nuclear Information System (INIS)
Chin, Shih-Miao; Joy, D.S.; Johnson, P.E.; Bobic, S.M.; Miaou, Shaw-Pin
1989-01-01
An approach has been formulated to route nuclear spent fuel over the US Interstate highway network. This approach involves the generation of alternative routes so that any potential adverse impacts will not only concentrate on regions along the shortest path between the nuclear power plant and repository. Extensive literature research on the shortest path finding algorithms has been carried out. Consequently, an extremely efficient shortest path algorithm has been implemented and significantly increases the overall system performance. State-of-the-art interactive computer graphics is used. In addition to easy-to-use pop-up menus, full color mapping and display capabilities are also incorporated. All of these features have been implemented on commonly available personal computers. 6 figs., 2 tabs
Optimal Switch Configuration in Software-Defined Networks
Directory of Open Access Journals (Sweden)
Béla GENGE
2016-06-01
Full Text Available The emerging Software-Defined Networks (SDN paradigm facilitates innovative applications and enables the seamless provisioning of resilient communications. Nevertheless, the installation of communication flows in SDN requires careful planning in order to avoid configuration errors and to fulfill communication requirements. In this paper we propose an approach that installs automatically and optimally static flows in SDN switches. The approach aims to select high capacity links and shortest path routing, and enforces communication link and switch capacity limitations. Experimental results demonstrate the effectiveness and scalability of the developed methodology.
Huang, Guo-Jiao; Bai, Chao-Ying; Greenhalgh, Stewart
2013-09-01
The traditional grid/cell-based wavefront expansion algorithms, such as the shortest path algorithm, can only find the first arrivals or multiply reflected (or mode converted) waves transmitted from subsurface interfaces, but cannot calculate the other later reflections/conversions having a minimax time path. In order to overcome the above limitations, we introduce the concept of a stationary minimax time path of Fermat's Principle into the multistage irregular shortest path method. Here we extend it from Cartesian coordinates for a flat earth model to global ray tracing of multiple phases in a 3-D complex spherical earth model. The ray tracing results for 49 different kinds of crustal, mantle and core phases show that the maximum absolute traveltime error is less than 0.12 s and the average absolute traveltime error is within 0.09 s when compared with the AK135 theoretical traveltime tables for a 1-D reference model. Numerical tests in terms of computational accuracy and CPU time consumption indicate that the new scheme is an accurate, efficient and a practical way to perform 3-D multiphase arrival tracking in regional or global traveltime tomography.
Routing versus energy optimization in a linear network
Coenen, Tom Johannes Maria; van Ommeren, Jan C.W.; de Graaf, Maurits
In wireless networks, devices (or nodes) often have a limited battery supply to use for the sending and reception of transmissions. By allowing nodes to relay messages for other nodes, the distance that needs to be bridged can be reduced, thus limiting the energy needed for a transmission. However,
The QKD network: model and routing scheme
Yang, Chao; Zhang, Hongqi; Su, Jinhai
2017-11-01
Quantum key distribution (QKD) technology can establish unconditional secure keys between two communicating parties. Although this technology has some inherent constraints, such as the distance and point-to-point mode limits, building a QKD network with multiple point-to-point QKD devices can overcome these constraints. Considering the development level of current technology, the trust relaying QKD network is the first choice to build a practical QKD network. However, the previous research didn't address a routing method on the trust relaying QKD network in detail. This paper focuses on the routing issues, builds a model of the trust relaying QKD network for easily analysing and understanding this network, and proposes a dynamical routing scheme for this network. From the viewpoint of designing a dynamical routing scheme in classical network, the proposed scheme consists of three components: a Hello protocol helping share the network topology information, a routing algorithm to select a set of suitable paths and establish the routing table and a link state update mechanism helping keep the routing table newly. Experiments and evaluation demonstrates the validity and effectiveness of the proposed routing scheme.
Directory of Open Access Journals (Sweden)
Shuai Deng
2016-01-01
Full Text Available This paper presents a closed-loop location-inventory-routing problem model considering both quality defect returns and nondefect returns in e-commerce supply chain system. The objective is to minimize the total cost produced in both forward and reverse logistics networks. We propose a combined optimization algorithm named hybrid ant colony optimization algorithm (HACO to address this model that is an NP-hard problem. Our experimental results show that the proposed HACO is considerably efficient and effective in solving this model.
Olariu, Victor; Manesso, Erica; Peterson, Carsten
2017-06-01
Depicting developmental processes as movements in free energy genetic landscapes is an illustrative tool. However, exploring such landscapes to obtain quantitative or even qualitative predictions is hampered by the lack of free energy functions corresponding to the biochemical Michaelis-Menten or Hill rate equations for the dynamics. Being armed with energy landscapes defined by a network and its interactions would open up the possibility of swiftly identifying cell states and computing optimal paths, including those of cell reprogramming, thereby avoiding exhaustive trial-and-error simulations with rate equations for different parameter sets. It turns out that sigmoidal rate equations do have approximate free energy associations. With this replacement of rate equations, we develop a deterministic method for estimating the free energy surfaces of systems of interacting genes at different noise levels or temperatures. Once such free energy landscape estimates have been established, we adapt a shortest path algorithm to determine optimal routes in the landscapes. We explore the method on three circuits for haematopoiesis and embryonic stem cell development for commitment and reprogramming scenarios and illustrate how the method can be used to determine sequential steps for onsets of external factors, essential for efficient reprogramming.
Estimating Common Pedestrian Routes through Indoor Path Networks using Position Traces
DEFF Research Database (Denmark)
Prentow, Thor Siiger; Blunck, Henrik; Grønbæk, Kaj
2014-01-01
routes between locations. These methods are sufficiently efficient to provide common routes based on real-time data from thousands of devices simultaneously. Furthermore, we show that the methods operate robustly even on basis of noisy and coarse-grained position estimates as provided by large......Abstract—Accurate information about how people commonly travel in a given large-scale building environment and which routes they take for given start and destination points is essential for applications such as indoor navigation, route prediction, and mobile work planning and logistics...
Directory of Open Access Journals (Sweden)
Adithya Guru Vaishnav.S
2015-08-01
Full Text Available This paper aims at providing a theoretical framework to find an optimized route from any source to destination considering the real-time traffic congestion issues. The distance of various possible routes from the source and destination are calculated and a PathRank is allocated in the descending order of distance to each possible path. Each intermediate locations are considered as nodes of a graph and the edges are represented by real-time traffic flow monitored using GoogleMaps GPS crowdsourcing data. The Page Rank is calculated for each intermediate node. From the values of PageRank and PathRank the minimum sum term is used to find an optimized route with minimal trade-off between shortest path and real-time traffic.
Epidemic metapopulation model with traffic routing in scale-free networks
International Nuclear Information System (INIS)
Huang, Wei; Chen, Shengyong
2011-01-01
In this paper, we propose a model incorporating both the traffic routing dynamics and the virus prevalence dynamics. In this model, each packet may be isolated from the network on its transporting path, which means that the packet cannot be successfully delivered to its destination. In contrast, a successful transport means that a packet can be delivered from source to destination without being isolated. The effects of model parameters on the delivery success rate and the delivery failure rate are intensively studied and analyzed. Several routing strategies are performed for our model. Results show that the shortest path routing strategy is the most effective for enhancing the delivery success rate, especially when each packet is only allowed to be delivered to the neighbor with the lowest degree along the shortest path. We also find that, by minimizing the sum of the nodes' degree along the transporting path, we can also obtain a satisfactory delivery success rate
Ranking paths in stochastic time-dependent networks
DEFF Research Database (Denmark)
Nielsen, Lars Relund; Andersen, Kim Allan; Pretolani, Daniele D.
2014-01-01
In this paper we address optimal routing problems in networks where travel times are both stochastic and time-dependent. In these networks, the best route choice is not necessarily a path, but rather a time-adaptive strategy that assigns successors to nodes as a function of time. Nevertheless, in...
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
2016-01-01
In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…
Directory of Open Access Journals (Sweden)
Ehsan Zakeri
Full Text Available Abstract In this research, generation of a short and smooth path in three-dimensional space with obstacles for guiding an Unmanned Underwater Vehicle (UUV without collision is investigated. This is done by utilizing spline technique, in which the spline control points positions are determined by Imperialist Competitive Algorithm (ICA in three-dimensional space such that the shortest possible path from the starting point to the target point without colliding with obstacles is achieved. Furthermore, for guiding the UUV in the generated path, an Interval Type-2 Fuzzy Logic Controller (IT2FLC, the coefficients of which are optimized by considering an objective function that includes quadratic terms of the input forces and state error of the system, is used. Selecting such objective function reduces the control error and also the force applied to the UUV, which consequently leads to reduction of energy consumption. Therefore, by using a special method, desired signals of UUV state are obtained from generated three-dimensional optimal path such that tracking these signals by the controller leads to the tracking of this path by UUV. In this paper, the dynamical model of the UUV, entitled as "mUUV-WJ-1" , is derived and its hydrodynamic coefficients are calculated by CFD in order to be used in the simulations. For simulation by the method presented in this study, three environments with different obstacles are intended in order to check the performance of the IT2FLC controller in generating optimal paths for guiding the UUV. In this article, in addition to ICA, Particle Swarm Optimization (PSO and Artificial Bee Colony (ABC are also used for generation of the paths and the results are compared with each other. The results show the appropriate performance of ICA rather than ABC and PSO. Moreover, to evaluate the performance of the IT2FLC, optimal Type-1 Fuzzy Logic Controller (T1FLC and Proportional Integrator Differentiator (PID controller are designed
Basile, Vito; Guadagno, Gianluca; Ferrario, Maddalena; Fassi, Irene
2018-03-01
In this paper a parametric, modular and scalable algorithm allowing a fully automated assembly of a backplane fiber-optic interconnection circuit is presented. This approach guarantees the optimization of the optical fiber routing inside the backplane with respect to specific criteria (i.e. bending power losses), addressing both transmission performance and overall costs issues. Graph theory has been exploited to simplify the complexity of the NxN full-mesh backplane interconnection topology, firstly, into N independent sub-circuits and then, recursively, into a limited number of loops easier to be generated. Afterwards, the proposed algorithm selects a set of geometrical and architectural parameters whose optimization allows to identify the optimal fiber optic routing for each sub-circuit of the backplane. The topological and numerical information provided by the algorithm are then exploited to control a robot which performs the automated assembly of the backplane sub-circuits. The proposed routing algorithm can be extended to any array architecture and number of connections thanks to its modularity and scalability. Finally, the algorithm has been exploited for the automated assembly of an 8x8 optical backplane realized with standard multimode (MM) 12-fiber ribbons.
NQAR: Network Quality Aware Routing in Error-Prone Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Jaewon Choi
2010-01-01
Full Text Available We propose a network quality aware routing (NQAR mechanism to provide an enabling method of the delay-sensitive data delivery over error-prone wireless sensor networks. Unlike the existing routing methods that select routes with the shortest arrival latency or the minimum hop count, the proposed scheme adaptively selects the route based on the network qualities including link errors and collisions with minimum additional complexity. It is designed to avoid the paths with potential noise and collision that may cause many non-deterministic backoffs and retransmissions. We propose a generic framework to select a minimum cost route that takes the packet loss rate and collision history into account. NQAR uses a data centric approach to estimate a single-hop delay based on processing time, propagation delay, packet loss rate, number of backoffs, and the retransmission timeout between two neighboring nodes. This enables a source node to choose the shortest expected end-to-end delay path to send a delay-sensitive data. The experiment results show that NQAR reduces the end-to-end transfer delay up to approximately 50% in comparison with the latency-based directed diffusion and the hop count-based directed diffusion under the error-prone network environments. Moreover, NQAR shows better performance than those routing methods in terms of jitter, reachability, and network lifetime.
Finding Shortest Paths on Terrains by Killing Two Birds with One Stone
DEFF Research Database (Denmark)
Kaul, Manohar; Wong, Raymond Chi-Wing; Yang, Bin
2013-01-01
With the increasing availability of terrain data, e.g., from aerial laser scans, the management of such data is attracting increasing at- tention in both industry and academia. In particular, spatial queries, e.g., k -nearest neighbor and reverse nearest neighbor queries, in Euclidean and spatial...... network spaces are being extended to ter- rains. Such queries all rely on an important operation, that of finding shortest surface distances. However, shortest surface dis- tance computation is very time consuming. We propose techniques that enable efficient computation of lower and upper bounds...... of the shortest surface distance, which enable faster query processing by eliminating expensive distance computations. Empirical studies show that our bounds are much tighter than the best-known bounds in many cases and that they enable speedups of up to 43 times for some well-known spatial querie...
A Joint Link and Channel Assignment Routing Scheme for Cognitive Radio Networks
Directory of Open Access Journals (Sweden)
H.S.Zhao
2013-12-01
Full Text Available Cognitive radio (CR is a promising technology that enables opportunistic utilization of the temporarily vacant spectrum to mitigate the spectrum scarcity in wireless communications. Since secondary users (SUs should vacate the channel in use immediately after detecting the reappearances of primary users (PUs in cognitive radio networks (CRNs, the route reliability is a distinctive challenge for routing in CRNs. Furthermore, the throughput requirement of an SU session should be satisfied and it is always preferable to select a route with less negative influence on other current or latish sessions. To account for the route reliability challenge, we study the joint link and channel assignment routing problem for CRNs. It is formulated in a form of integer nonlinear programming (INLP, which is NP-hard, with the objective of minimizing the interference of a new route to other routes while providing route reliability and throughput guarantee. An on-demand route discovery algorithm is proposed to find reliable candidate paths, while a joint link and channel assignment routing algorithm with sequentially-connected-link coordination is proposed to choose the near-optimal route for improving the route reliability and minimizing negative influence. Simulation results demonstrate that the proposed algorithm achieves considerable improvement over existing schemes in both route reliability and throughput.
Wang, Xiaoming; Lin, Yaguang; Zhang, Shanshan; Cai, Zhipeng
2017-05-01
Sudden disasters such as earthquake, flood and hurricane necessitate the employment of communication networks to carry out emergency response activities. Routing has a significant impact on the functionality, performance and flexibility of communication networks. In this article, the routing problem is studied considering the delivery ratio of messages, the overhead ratio of messages and the average delay of messages in mobile opportunistic networks (MONs) for enterprise-level emergency response communications in sudden disaster scenarios. Unlike the traditional routing methods for MONS, this article presents a new two-stage spreading and forwarding dynamic routing algorithm based on the proposed social activity degree and physical contact factor for mobile customers. A new modelling method for describing a dynamic evolving process of the topology structure of a MON is first proposed. Then a multi-copy spreading strategy based on the social activity degree of nodes and a single-copy forwarding strategy based on the physical contact factor between nodes are designed. Compared with the most relevant routing algorithms such as Epidemic, Prophet, Labelled-sim, Dlife-comm and Distribute-sim, the proposed routing algorithm can significantly increase the delivery ratio of messages, and decrease the overhead ratio and average delay of messages.
Large and Dense Swarms: Simulation of a Shortest Path Alarm Propagation
Directory of Open Access Journals (Sweden)
Claudia Snels
2015-07-01
Full Text Available This paper deals with the transmission of alarm messages in large and dense underwater swarms of Autonomous Underwater Vehicles (AUVs and describes the verification process of the derived algorithm results by means of two simulation tools realized by the authors. A collision-free communication protocol has been developed, tailored to a case where a single AUV needs to send a message to a specific subset of swarm members regarding a perceived danger. The protocol includes a handshaking procedure that creates a silence region before the transmission of the message obtained through specific acoustic tones out of the normal transmission frequencies or through optical signals. This region will include all members of the swarm involved in the alarm message and their neighbours, preventing collisions between them. The AUV sending messages to a target area computes a delay function on appropriate arcs and runs a Dijkstra-like algorithm obtaining a multicast tree. After an explanation of the whole building of this collision-free multicast tree, a simulation has been carried out assuming different scenarios relevant to swarm density, signal power of the modem and the geometrical configuration of the nodes.
DEFF Research Database (Denmark)
Kulkarni, Nandkumar P.; Prasad, Neeli R.; Prasad, Ramjee
deliberation. To tackle these two problems, Mobile Wireless Sensor Networks (MWSNs) is a better choice. In MWSN, Sensor nodes move freely to a target area without the need for any special infrastructure. Due to mobility, the routing process in MWSN has become more complicated as connections in the network can...... such as Average Energy consumption, Control Overhead, Reaction Time, LQI, and HOP Count. The authors study the influence of energy heterogeneity and mobility of sensor nodes on the performance of EMRP. The Performance of EMRP compared with Simple Hybrid Routing Protocol (SHRP) and Dynamic Multi-Objective Routing...
A Decision Processing Algorithm for CDC Location Under Minimum Cost SCM Network
Park, N. K.; Kim, J. Y.; Choi, W. Y.; Tian, Z. M.; Kim, D. J.
Location of CDC in the matter of network on Supply Chain is becoming on the high concern these days. Present status of methods on CDC has been mainly based on the calculation manually by the spread sheet to achieve the goal of minimum logistics cost. This study is focused on the development of new processing algorithm to overcome the limit of present methods, and examination of the propriety of this algorithm by case study. The algorithm suggested by this study is based on the principle of optimization on the directive GRAPH of SCM model and suggest the algorithm utilizing the traditionally introduced MST, shortest paths finding methods, etc. By the aftermath of this study, it helps to assess suitability of the present on-going SCM network and could be the criterion on the decision-making process for the optimal SCM network building-up for the demand prospect in the future.
A Novel Chaotic Particle Swarm Optimization Algorithm for Parking Space Guidance
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Na Dong
2016-01-01
Full Text Available An evolutionary approach of parking space guidance based upon a novel Chaotic Particle Swarm Optimization (CPSO algorithm is proposed. In the newly proposed CPSO algorithm, the chaotic dynamics is combined into the position updating rules of Particle Swarm Optimization to improve the diversity of solutions and to avoid being trapped in the local optima. This novel approach, that combines the strengths of Particle Swarm Optimization and chaotic dynamics, is then applied into the route optimization (RO problem of parking lots, which is an important issue in the management systems of large-scale parking lots. It is used to find out the optimized paths between any source and destination nodes in the route network. Route optimization problems based on real parking lots are introduced for analyzing and the effectiveness and practicability of this novel optimization algorithm for parking space guidance have been verified through the application results.
Improved Artificial Fish Algorithm for Parameters Optimization of PID Neural Network
Jing Wang; Yourui Huang
2013-01-01
In order to solve problems such as initial weights are difficult to be determined, training results are easy to trap in local minima in optimization process of PID neural network parameters by traditional BP algorithm, this paper proposed a new method based on improved artificial fish algorithm for parameters optimization of PID neural network. This improved artificial fish algorithm uses a composite adaptive artificial fish algorithm based on optimal artificial fish and nearest artificial fi...
A two-hop based adaptive routing protocol for real-time wireless sensor networks.
Rachamalla, Sandhya; Kancherla, Anitha Sheela
2016-01-01
One of the most important and challenging issues in wireless sensor networks (WSNs) is to optimally manage the limited energy of nodes without degrading the routing efficiency. In this paper, we propose an energy-efficient adaptive routing mechanism for WSNs, which saves energy of nodes by removing the much delayed packets without degrading the real-time performance of the used routing protocol. It uses the adaptive transmission power algorithm which is based on the attenuation of the wireless link to improve the energy efficiency. The proposed routing mechanism can be associated with any geographic routing protocol and its performance is evaluated by integrating with the well known two-hop based real-time routing protocol, PATH and the resulting protocol is energy-efficient adaptive routing protocol (EE-ARP). The EE-ARP performs well in terms of energy consumption, deadline miss ratio, packet drop and end-to-end delay.
Generalized routing protocols for multihop relay networks
Khan, Fahd Ahmed
2011-07-01
Performance of multihop cooperative networks depends on the routing protocols employed. In this paper we propose the last-n-hop selection protocol, the dual path protocol, the forward-backward last-n-hop selection protocol and the forward-backward dual path protocol for the routing of data through multihop relay networks. The average symbol error probability performance of the schemes is analysed by simulations. It is shown that close to optimal performance can be achieved by using the last-n-hop selection protocol and its forward-backward variant. Furthermore we also compute the complexity of the protocols in terms of number of channel state information required and the number of comparisons required for routing the signal through the network. © 2011 IEEE.
Pheromone based alternative route planning
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Liangbing Feng
2016-08-01
Full Text Available In this work, we propose an improved alternative route calculation based on alternative figures, which is suitable for practical environments. The improvement is based on the fact that the main traffic route is the road network skeleton in a city. Our approach using nodes may generate a higher possibility of overlapping. We employ a bidirectional Dijkstra algorithm to search the route. To measure the quality of an Alternative Figures (AG, three quotas are proposed. The experiment results indicate that the improved algorithm proposed in this paper is more effective than others.
International Nuclear Information System (INIS)
Jain, Sanjay Kumar; Vietla, Srinivas; Roy, D.A.; Biswas, B.B.; Pithawa, C.K.
2010-01-01
A Wireless Sensor Network is a collection of wireless sensor nodes arranged in a self-forming network without aid of any infrastructure or administration. The individual nodes have limited resources and hence efficient communication mechanisms between the nodes have to be devised for continued operation of the network in a plant environment. In wireless sensor networks a sink node or base station at one end acts as the recipient of information gathered by all other sensor nodes in the network and the information arrives at the sink through multiple hops across the nodes of the network. A routing algorithm has been developed in which a virtual sink node is generated whenever hop count of an ordinary node crosses a certain specified value. The virtual sink node acts as a recipient node for data of all neighboring nodes. This virtual sink helps in reducing routing overhead, especially when the sensor network is scaled to a larger network. The advantages with this scheme are less energy consumption, reduced congestion in the network and longevity of the network. The above algorithm is suitable for event based or interval based monitoring systems in nuclear plants. This paper describes the working of the proposed algorithm and provides its implementation details. (author)
Path selection and bandwidth allocation in MPLS networks: a nonlinear programming approach
Burns, J. E.; Ott, Teunis J.; de Kock, Johan M.; Krzesinski, Anthony E.
2001-07-01
Multi-protocol Label Switching extends the IPv4 destination-based routing protocols to provide new and scalable routing capabilities in connectionless networks using relatively simple packet forwarding mechanisms. MPLS networks carry traffic on virtual connections called label switched paths. This paper considers path selection and bandwidth allocation in MPLS networks in order to optimize the network quality of service. The optimization is based upon the minimization of a non-linear objective function which under light load simplifies to OSPF routing with link metrics equal to the link propagation delays. The behavior under heavy load depends on the choice of certain parameters: It can essentially be made to minimize maximal expected utilization, or to maximize minimal expected weighted slacks (both over all links). Under certain circumstances it can be made to minimize the probability that a link has an instantaneous offered load larger than its transmission capacity. We present a model of an MPLS network and an algorithm to find and capacitate optimal LSPs. The algorithm is an improvement of the well-known flow deviation non-linear programming method. The algorithm is applied to compute optimal LSPs for several test networks carrying a single traffic class.
Capacitated Hub Routing Problem in Hub-and-Feeder Network Design: Modeling and Solution Algorithm
Gelareh , Shahin; Neamatian Monemi , Rahimeh; Semet , Frédéric
2015-01-01
International audience; In this paper, we address the Bounded Cardinality Hub Location Routing with Route Capacity wherein eachhub acts as a transshipment node for one directed route. The number of hubs lies between a minimum anda maximum and the hub-level network is a complete subgraph. The transshipment operations take place atthe hub nodes and flow transfer time from a hub-level transporter to a spoke-level vehicle influences spoketo-hub allocations. We propose a mathematical model and a b...
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....
Bang, Soonam; Heo, Joon; Han, Soohee; Sohn, Hong-Gyoo
2010-01-01
Infiltration-route analysis is a military application of geospatial information system (GIS) technology. In order to find susceptible routes, optimal-path-searching algorithms are applied to minimize the cost function, which is the summed result of detection probability. The cost function was determined according to the thermal observation device (TOD) detection probability, the viewshed analysis results, and two feature layers extracted from the vector product interim terrain data. The detection probability is computed and recorded for an individual cell (50 m × 50 m), and the optimal infiltration routes are determined with A* algorithm by minimizing the summed costs on the routes from a start point to an end point. In the present study, in order to simulate the dynamic nature of a real-world problem, one thousand cost surfaces in the GIS environment were generated with randomly located TODs and randomly selected infiltration start points. Accordingly, one thousand sets of vulnerable routes for infiltration purposes could be found, which could be accumulated and presented as an infiltration vulnerability map. This application can be further utilized for both optimal infiltration routing and surveillance network design. Indeed, dynamic simulation in the GIS environment is considered to be a powerful and practical solution for optimization problems. A similar approach can be applied to the dynamic optimal routing for civil infrastructure, which requires consideration of terrain-related constraints and cost functions.
A shortest-path graph kernel for estimating gene product semantic similarity
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Alvarez Marco A
2011-07-01
Full Text Available Abstract Background Existing methods for calculating semantic similarity between gene products using the Gene Ontology (GO often rely on external resources, which are not part of the ontology. Consequently, changes in these external resources like biased term distribution caused by shifting of hot research topics, will affect the calculation of semantic similarity. One way to avoid this problem is to use semantic methods that are "intrinsic" to the ontology, i.e. independent of external knowledge. Results We present a shortest-path graph kernel (spgk method that relies exclusively on the GO and its structure. In spgk, a gene product is represented by an induced subgraph of the GO, which consists of all the GO terms annotating it. Then a shortest-path graph kernel is used to compute the similarity between two graphs. In a comprehensive evaluation using a benchmark dataset, spgk compares favorably with other methods that depend on external resources. Compared with simUI, a method that is also intrinsic to GO, spgk achieves slightly better results on the benchmark dataset. Statistical tests show that the improvement is significant when the resolution and EC similarity correlation coefficient are used to measure the performance, but is insignificant when the Pfam similarity correlation coefficient is used. Conclusions Spgk uses a graph kernel method in polynomial time to exploit the structure of the GO to calculate semantic similarity between gene products. It provides an alternative to both methods that use external resources and "intrinsic" methods with comparable performance.
Directory of Open Access Journals (Sweden)
Shi Chen-guang
2014-08-01
Full Text Available A novel optimal power allocation algorithm for radar network systems is proposed for Low Probability of Intercept (LPI technology in modern electronic warfare. The algorithm is based on the LPI optimization. First, the Schleher intercept factor for a radar network is derived, and then the Schleher intercept factor is minimized by optimizing the transmission power allocation among netted radars in the network to guarantee target-tracking performance. Furthermore, the Nonlinear Programming Genetic Algorithm (NPGA is used to solve the resulting nonconvex, nonlinear, and constrained optimization problem. Numerical simulation results show the effectiveness of the proposed algorithm.
Directory of Open Access Journals (Sweden)
Jianfeng Guan
2017-01-01
Full Text Available The existing spray-based routing algorithms in DTN cannot dynamically adjust the number of message copies based on actual conditions, which results in a waste of resource and a reduction of the message delivery rate. Besides, the existing spray-based routing protocols may result in blind spots or dead end problems due to the limitation of various given metrics. Therefore, this paper proposes a social relationship based adaptive multiple spray-and-wait routing algorithm (called SRAMSW which retransmits the message copies based on their residence times in the node via buffer management and selects forwarders based on the social relationship. By these means, the proposed algorithm can remove the plight of the message congestion in the buffer and improve the probability of replicas to reach their destinations. The simulation results under different scenarios show that the SRAMSW algorithm can improve the message delivery rate and reduce the messages’ dwell time in the cache and further improve the buffer effectively.
Optimization of heat exchanger networks using genetic algorithms
International Nuclear Information System (INIS)
Teyssedou, A.; Dipama, J.; Sorin, M.
2004-01-01
Most thermal processes encountered in the power industry (chemical, metallurgical, nuclear and thermal power stations) necessitate the transfer of large amounts of heat between fluids having different thermal potentials. A common practice applied to achieve such a requirement consists of using heat exchangers. In general, each current of fluid is conveniently cooled or heated independently from each other in the power plant. When the number of heat exchangers is large enough, however, a convenient arrangement of different flow currents may allow a considerable reduction in energy consumption to be obtained (Linnhoff and Hidmarsh, 1983). In such a case the heat exchangers form a 'Heat Exchanger Network' (HEN) that can be optimized to reduce the overall energy consumption. This type of optimization problem, involves two separates calculation procedures. First, it is necessary to optimize the topology of the HEN that will permit a reduction in energy consumption to be obtained. In a second step the power distribution across the HEN should be optimized without violating the second law of thermodynamics. The numerical treatment of this kind of problem requires the use of both discrete variables (for taking into account each heat exchanger unit) and continuous variables for handling the thermal load of each unit. It is obvious that for a large number of heat exchangers, the use of conventional calculation methods, i.e., Simplexe, becomes almost impossible. Therefore, in this paper we present a 'Genetic Algorithm' (GA), that has been implemented and successfully used to treat complex HENs, containing a large number of heat exchangers. As opposed to conventional optimization techniques that require the knowledge of the derivatives of a function, GAs start the calculation process from a large population of possible solutions of a given problem (Goldberg, 1999). Each possible solution is in turns evaluated according to a 'fitness' criterion obtained from an objective
Optimal design of mixed-media packet-switching networks - Routing and capacity assignment
Huynh, D.; Kuo, F. F.; Kobayashi, H.
1977-01-01
This paper considers a mixed-media packet-switched computer communication network which consists of a low-delay terrestrial store-and-forward subnet combined with a low-cost high-bandwidth satellite subnet. We show how to route traffic via ground and/or satellite links by means of static, deterministic procedures and assign capacities to channels subject to a given linear cost such that the network average delay is minimized. Two operational schemes for this network model are investigated: one is a scheme in which the satellite channel is used as a slotted ALOHA channel; the other is a new multiaccess scheme we propose in which whenever a channel collision occurs, retransmission of the involved packets will route through ground links to their destinations. The performance of both schemes is evaluated and compared in terms of cost and average packet delay tradeoffs for some examples. The results offer guidelines for the design and optimal utilization of mixed-media networks.
Tang, Jiqiang; Yang, Wu; Zhu, Lingyun; Wang, Dong; Feng, Xin
2017-04-26
In recent years, Wireless Sensor Networks with a Mobile Sink (WSN-MS) have been an active research topic due to the widespread use of mobile devices. However, how to get the balance between data delivery latency and energy consumption becomes a key issue of WSN-MS. In this paper, we study the clustering approach by jointly considering the Route planning for mobile sink and Clustering Problem (RCP) for static sensor nodes. We solve the RCP problem by using the minimum travel route clustering approach, which applies the minimum travel route of the mobile sink to guide the clustering process. We formulate the RCP problem as an Integer Non-Linear Programming (INLP) problem to shorten the travel route of the mobile sink under three constraints: the communication hops constraint, the travel route constraint and the loop avoidance constraint. We then propose an Imprecise Induction Algorithm (IIA) based on the property that the solution with a small hop count is more feasible than that with a large hop count. The IIA algorithm includes three processes: initializing travel route planning with a Traveling Salesman Problem (TSP) algorithm, transforming the cluster head to a cluster member and transforming the cluster member to a cluster head. Extensive experimental results show that the IIA algorithm could automatically adjust cluster heads according to the maximum hops parameter and plan a shorter travel route for the mobile sink. Compared with the Shortest Path Tree-based Data-Gathering Algorithm (SPT-DGA), the IIA algorithm has the characteristics of shorter route length, smaller cluster head count and faster convergence rate.
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.
Institute of Scientific and Technical Information of China (English)
张利斌
2013-01-01
To prolong the service life of wireless sensor network, puts forward the optimization of sensor network routing of the genetic algorithm. The algorithm analysis nodes sending power change of flow balance constraint link, link maximum transmis?sion efficiency constraint and node energy consumption constraint conditions,. Energy perception routing from data transmission based on the energy consumption, and discuss the optimal energy consumption path, efficient use of node energy, prolong the network life cycle. The basic realization is based on the rest of the node energy or transmission path energy needs to select rout?ing path. Simulation experiments show that this algorithm can balance node energy and neighbor node quantity, prolong the net?work life time.%为了延长无线传感网络的使用寿命,提出了一种优化传感网络路由的遗传算法.该算法通过分析节点发送功率变化下的链路流量约束,链路最大传输效率约束,节点能耗约束等条件.感知路由从数据传输的能量消耗量出发,讨论最优能量消耗路径,提高节点能量利用率,延长网络生存期.其基本实现是根据节点的剩余能量和传输路径上的能量消耗来选择路由路径.仿真实验表明,该算法可以平衡节点能耗和邻节点使用数量,延长网络的生存寿命.
Optimal siting of capacitors in radial distribution network using Whale Optimization Algorithm
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D.B. Prakash
2017-12-01
Full Text Available In present days, continuous effort is being made in bringing down the line losses of the electrical distribution networks. Therefore proper allocation of capacitors is of utmost importance because, it will help in reducing the line losses and maintaining the bus voltage. This in turn results in improving the stability and reliability of the system. In this paper Whale Optimization Algorithm (WOA is used to find optimal sizing and placement of capacitors for a typical radial distribution system. Multi objectives such as operating cost reduction and power loss minimization with inequality constraints on voltage limits are considered and the proposed algorithm is validated by applying it on standard radial systems: IEEE-34 bus and IEEE-85 bus radial distribution test systems. The results obtained are compared with those of existing algorithms. The results show that the proposed algorithm is more effective in bringing down the operating costs and in maintaining better voltage profile. Keywords: Whale Optimization Algorithm (WOA, Optimal allocation and sizing of capacitors, Power loss reduction and voltage stability improvement, Radial distribution system, Operating cost minimization
An overview of smart grid routing algorithms
Wang, Junsheng; OU, Qinghai; Shen, Haijuan
2017-08-01
This paper summarizes the typical routing algorithm in smart grid by analyzing the communication business and communication requirements of intelligent grid. Mainly from the two kinds of routing algorithm is analyzed, namely clustering routing algorithm and routing algorithm, analyzed the advantages and disadvantages of two kinds of typical routing algorithm in routing algorithm and applicability.
A QoS-Guaranteed Coverage Precedence Routing Algorithm for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Jiun-Chuan Lin
2011-03-01
Full Text Available For mission-critical applications of wireless sensor networks (WSNs involving extensive battlefield surveillance, medical healthcare, etc., it is crucial to have low-power, new protocols, methodologies and structures for transferring data and information in a network with full sensing coverage capability for an extended working period. The upmost mission is to ensure that the network is fully functional providing reliable transmission of the sensed data without the risk of data loss. WSNs have been applied to various types of mission-critical applications. Coverage preservation is one of the most essential functions to guarantee quality of service (QoS in WSNs. However, a tradeoff exists between sensing coverage and network lifetime due to the limited energy supplies of sensor nodes. In this study, we propose a routing protocol to accommodate both energy-balance and coverage-preservation for sensor nodes in WSNs. The energy consumption for radio transmissions and the residual energy over the network are taken into account when the proposed protocol determines an energy-efficient route for a packet. The simulation results demonstrate that the proposed protocol is able to increase the duration of the on-duty network and provide up to 98.3% and 85.7% of extra service time with 100% sensing coverage ratio comparing with LEACH and the LEACH-Coverage-U protocols, respectively.
Identification of Evacuation Routes in Tacloban City using Geographic Information System
Mendoza, Jerico; Mahar Francisco Lagmay, Alfredo; Santiago, Joy; Suarez, John Kenneth
2016-04-01
The Philippines is the second most at risk to natural hazards according to the 2014 World Risk Report. On 8 November 2013, category 5 Typhoon Haiyan crossed the central region of the Philippines with maximum sustained wind reaching 315 kph. Considered as one of the strongest typhoons that made landfall in recorded history, Typhoon Haiyan caused USD 8 billion damage to properties, 6,293 deaths, 28,689 injured and 1,061 missing persons. Tacloban City, located in the north-eastern part of the island of Leyte in Eastern Visayas region, is one of the area most devastated by Typhoon Haiyan. The city is susceptible to other natural hazards given its geography, topography and geology. This condition emphasizes the need for preventive measures to avoid further loss of lives and destruction to properties. Evacuation is a mitigating strategy which involves the process of moving people from dangerous places to safer locations. Using Geographic Information System (GIS), a multi-hazard map of Tacloban City was created to determine safe areas for evacuation centers. The optimal route for evacuation was identified using ArcGIS Network Analyst's routing solver based on Dijkstra's algorithm. The medium of transportation used in the analysis is by foot with an average speed of 5.0 kph. Furthermore, the study assumes that all roads are passable and fully functional during the travel period and that there are no structures, trees and other debris that may act as road blockage. The study can be used as a reference in hazard assessment for disaster risk management and evacuation planning. This can be further improved by incorporating behaviour of the affected population and other socio-economic factors, different modes of transportation and detailed analysis of topography.
Optimal multigrid algorithms for the massive Gaussian model and path integrals
International Nuclear Information System (INIS)
Brandt, A.; Galun, M.
1996-01-01
Multigrid algorithms are presented which, in addition to eliminating the critical slowing down, can also eliminate the open-quotes volume factorclose quotes. The elimination of the volume factor removes the need to produce many independent fine-grid configurations for averaging out their statistical deviations, by averaging over the many samples produced on coarse grids during the multigrid cycle. Thermodynamic limits of observables can be calculated to relative accuracy var-epsilon r in just O(var-epsilon r -2 ) computer operations, where var-epsilon r is the error relative to the standard deviation of the observable. In this paper, we describe in detail the calculation of the susceptibility in the one-dimensional massive Gaussian model, which is also a simple example of path integrals. Numerical experiments show that the susceptibility can be calculated to relative accuracy var-epsilon r in about 8 var-epsilon r -2 random number generations, independent of the mass size
Finding the K shortest hyperpaths using reoptimization
DEFF Research Database (Denmark)
Nielsen, Lars Relund; Pretolani, Daniele; Andersen, Kim Allan
2006-01-01
The shortest hyperpath problem is an extension of the classical shortest path problem and has applications in many different areas. Recently, algorithms for finding the K shortest hyperpaths in a directed hypergraph have been developed by Andersen, Nielsen and Pretolani. In this paper we improve...... the worst-case computational complexity of an algorithm for finding the K shortest hyperpaths in an acyclic hypergraph. This result is obtained by applying new reoptimization techniques for shortest hyperpaths. The algorithm turns out to be quite effective in practice and has already been successfully...
Directory of Open Access Journals (Sweden)
Muhammer İLKUÇAR
2018-05-01
Full Text Available EXTENDED SUMMARY Background: Today, people travel for different purposes, both domestic and abroad, according to their afford. A travel can be consist of different alternatives like beach, sand-sun, adventure, culture, art, history, nature, entertainment, congress, gastronomy, health. Each person has his own travel concept. Someone wandered the streets of the city with other people to feel the senses of the city; the other may like art and exhibitions. Therefore, the travel plan should be personalized. Purpose: In this study, an Android application has been developed to create personalized travel plan via Google maps. A trip plan can be created through Google maps or by selecting from previously identified sightseeing spots. The trip plan can take the form of a tour (starting from one location and back to the starting point by travelling all the sightseeing locations or a route (start from a location to finish at another location. The user should be able to rearrange the routs according to their location. Method: Shortest route or tour of the trip plan is optimized by genetic algorithm. The genes of genetic algorithm consist of sightseeing locations which are selected by the user through Google maps. Locations (genes that can connect to each other constitute the chromosome structure. Thus each chromosome represents a possible tour. Calculation of distances between connected locations yields a length of a tour (fitness. It can also be calculated over a chromosome and shows the chromosome's fitness value. When the calculating the shortest route with genetic algorithm process (select-cross-mutation in the new chromosome are assigned unsuitable paths. In the new chromosome, same location used twice or the next location unsuitable. In this situation is corrected by correction function with the aid of greedy algorithm in feasible alternatives. Thus, the new chromosome is improved and more suitable individuals are obtained. Results and Conclusions: In
Near-Optimal Resource Allocation in Cooperative Cellular Networks Using Genetic Algorithms
Luo, Zihan; Armour, Simon; McGeehan, Joe
2015-01-01
This paper shows how a genetic algorithm can be used as a method of obtaining the near-optimal solution of the resource block scheduling problem in a cooperative cellular network. An exhaustive search is initially implementedto guarantee that the optimal result, in terms of maximizing the bandwidth efficiency of the overall network, is found, and then the genetic algorithm with the properly selected termination conditions is used in the same network. The simulation results show that the genet...
OSM-ORIENTED METHOD OF MULTIMODAL ROUTE PLANNING
Directory of Open Access Journals (Sweden)
X. Li
2015-07-01
Full Text Available With the increasing pervasiveness of basic facilitate of transportation and information, the need of multimodal route planning is becoming more essential in the fields of communication and transportation, urban planning, logistics management, etc. This article mainly described an OSM-oriented method of multimodal route planning. Firstly, it introduced how to extract the information we need from OSM data and build proper network model and storage model; then it analysed the accustomed cost standard adopted by most travellers; finally, we used shortest path algorithm to calculate the best route with multiple traffic means.
Zhu, Liucun; Zhang, Yu-Hang; Su, Fangchu; Chen, Lei; Huang, Tao; Cai, Yu-Dong
2016-01-01
Biologically, fruits are defined as seed-bearing reproductive structures in angiosperms that develop from the ovary. The fertilization, development and maturation of fruits are crucial for plant reproduction and are precisely regulated by intrinsic genetic regulatory factors. In this study, we used Arabidopsis thaliana as a model organism and attempted to identify novel genes related to fruit-associated biological processes. Specifically, using validated genes, we applied a shortest-path-based method to identify several novel genes in a large network constructed using the protein-protein interactions observed in Arabidopsis thaliana. The described analyses indicate that several of the discovered genes are associated with fruit fertilization, development and maturation in Arabidopsis thaliana.
INTERLINE, a railroad routing model: program description and user's manual
International Nuclear Information System (INIS)
Peterson, B.E.
1985-11-01
INTERLINE is an interactive computer program that finds likely routes for shipments over the US railroad system. It is based on a shortest path algorithm modified both to reflect the nature of railroad company operations and to accommodate computer resource limitations in dealing with a large transportation network. The first section of the report discusses the nature of railroad operations and routing practices in the United States, including the tendency to concentrate traffic on a limited number of mainlines, the competition for traffic by different companies operating in the same corridors, and the tendency of originating carriers to retain traffic on their systems before transferring it to terminating carriers. The theoretical foundation and operation of shortest path algorithms are described, as well as the techniques used to simulate actual operating practices within this framework. The second section is a user's guide that describes the program operation and data structures, program features, and user access. 11 refs., 11 figs
Hardware Genetic Algorithm Optimization by Critical Path Analysis using a Custom VLSI Architecture
Directory of Open Access Journals (Sweden)
Farouk Smith
2015-07-01
Full Text Available This paper propose a Virtual-Field Programmable Gate Array (V-FPGA architecture that allows direct access to its configuration bits to facilitate hardware evolution, thereby allowing any combinational or sequential digital circuit to be realized. By using the V-FPGA, this paper investigates two possible ways of making evolutionary hardware systems more scalable: by optimizing the system’s genetic algorithm (GA; and by decomposing the solution circuit into smaller, evolvable sub-circuits. GA optimization is done by: omitting a canonical GA’s crossover operator (i.e. by using a 1+λ algorithm; applying evolution constraints; and optimizing the fitness function. A noteworthy contribution this research has made is the in-depth analysis of the phenotypes’ CPs. Through analyzing the CPs, it has been shown that a great amount of insight can be gained into a phenotype’s fitness. We found that as the number of columns in the Cartesian Genetic Programming array increases, so the likelihood of an external output being placed in the column decreases. Furthermore, the number of used LEs per column also substantially decreases per added column. Finally, we demonstrated the evolution of a state-decomposed control circuit. It was shown that the evolution of each state’s sub-circuit was possible, and suggest that modular evolution can be a successful tool when dealing with scalability.
Warehouse stocking optimization based on dynamic ant colony genetic algorithm
Xiao, Xiaoxu
2018-04-01
In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.
Optimization of a truck-drone in tandem delivery network using k-means and genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Ferrandez, S. M.; Harbison, T.; Weber, T.; Sturges, R.; Rich, R.
2016-07-01
The purpose of this paper is to investigate the effectiveness of implementing unmanned aerial delivery vehicles in delivery networks. We investigate the notion of the reduced overall delivery time, energy, and costs for a truck-drone network by comparing the in-tandem system with a stand-alone delivery effort. The objectives are (1) to investigate the time, energy, and costs associated to a truck-drone delivery network compared to standalone truck or drone, (2) to propose an optimization algorithm that determines the optimal number of launch sites and locations given delivery requirements, and drones per truck, (3) to develop mathematical formulations for closed form estimations for the optimal number of launch locations, optimal total time, as well as the associated cost for the system. The design of the algorithm herein computes the minimal time of delivery utilizing K-means clustering to find launch locations, as well as a genetic algorithm to solve the truck route as a traveling salesmen problem (TSP). The optimal solution is determined by finding the minimum cost associated to the parabolic convex cost function. The optimal min-cost is determined by finding the most efficient launch locations using K-means algorithms to determine launch locations and a genetic algorithm to determine truck route between those launch locations. Results show improvements with in-tandem delivery efforts as opposed to standalone systems. Further, multiple drones per truck are more optimal and contribute to savings in both energy and time. For this, we sampled various initialization variables to derive closed form mathematical solutions for the problem. Ultimately, this provides the necessary analysis of an integrated truck-drone delivery system which could be implemented by a company in order to maximize deliveries while minimizing time and energy. Closed-form mathematical solutions can be used as close estimators for final costs and time. (Author)
Optimization of a truck-drone in tandem delivery network using k-means and genetic algorithm
Directory of Open Access Journals (Sweden)
Sergio Mourelo Ferrandez
2016-04-01
Full Text Available Purpose: The purpose of this paper is to investigate the effectiveness of implementing unmanned aerial delivery vehicles in delivery networks. We investigate the notion of the reduced overall delivery time, energy, and costs for a truck-drone network by comparing the in-tandem system with a stand-alone delivery effort. The objectives are (1 to investigate the time, energy, and costs associated to a truck-drone delivery network compared to standalone truck or drone, (2 to propose an optimization algorithm that determines the optimal number of launch sites and locations given delivery requirements, and drones per truck, (3 to develop mathematical formulations for closed form estimations for the optimal number of launch locations, optimal total time, as well as the associated cost for the system. Design/methodology/approach: The design of the algorithm herein computes the minimal time of delivery utilizing K-means clustering to find launch locations, as well as a genetic algorithm to solve the truck route as a traveling salesmen problem (TSP. The optimal solution is determined by finding the minimum cost associated to the parabolic convex cost function. The optimal min-cost is determined by finding the most efficient launch locations using K-means algorithms to determine launch locations and a genetic algorithm to determine truck route between those launch locations. Findings: Results show improvements with in-tandem delivery efforts as opposed to standalone systems. Further, multiple drones per truck are more optimal and contribute to savings in both energy and time. For this, we sampled various initialization variables to derive closed form mathematical solutions for the problem. Originality/value: Ultimately, this provides the necessary analysis of an integrated truck-drone delivery system which could be implemented by a company in order to maximize deliveries while minimizing time and energy. Closed-form mathematical solutions can be used as
B-iTRS: A Bio-Inspired Trusted Routing Scheme for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Mingchuan Zhang
2015-01-01
Full Text Available In WSNs, routing algorithms need to handle dynamical changes of network topology, extra overhead, energy saving, and other requirements. Therefore, routing in WSNs is an extremely interesting and challenging issue. In this paper, we present a novel bio-inspired trusted routing scheme (B-iTRS based on ant colony optimization (ACO and Physarum autonomic optimization (PAO. For trust assessment, B-iTRS monitors neighbors’ behavior in real time, receives feedback from Sink, and then assesses neighbors’ trusts based on the acquired information. For routing scheme, each node finds routes to the Sink based on ACO and PAO. In the process of path finding, B-iTRS senses the load and trust value of each node and then calculates the link load and link trust of the found routes to support the route selection. Moreover, B-iTRS also assesses the route based on PAO to maintain the route table. Simulation results show how B-iTRS can achieve the effective performance compared to existing state-of-the-art algorithms.
Optimization of territories and transport routes for hazardous products in a distribution network
Energy Technology Data Exchange (ETDEWEB)
Cantú, José Manuel Velarde; Solano, Alfredo Bueno; Leyva, Ernesto Alonso Lagarda; Acosta, Mauricio Lopez
2017-07-01
In a system of distribution of products are involved in different factors that determine their efficiency, profitability or optimal state, among these factors is the type of goods to be collected or delivered, it must also be considered the physical-chemical composition, hazard index to transport etc., in this sense, exist different standards for collection, delivery and transportation of such material causing with it an increase in operating costs associated with territory design and planning of distribution routes, the current paper present a general model based in mixed integer programming which integrates both problems, seeking to minimize the total distance traveled by the vehicle in each territory. It should be noted that one of the main features of this model is that it only considers the collection of goods in the distribution network which gives us the opportunity to offer low-cost solutions in terms of time and quality, addressing the specific and general characteristics of the emerging markets in Mexico.
Optimization of territories and transport routes for hazardous products in a distribution network
International Nuclear Information System (INIS)
Cantú, José Manuel Velarde; Solano, Alfredo Bueno; Leyva, Ernesto Alonso Lagarda; Acosta, Mauricio Lopez
2017-01-01
In a system of distribution of products are involved in different factors that determine their efficiency, profitability or optimal state, among these factors is the type of goods to be collected or delivered, it must also be considered the physical-chemical composition, hazard index to transport etc., in this sense, exist different standards for collection, delivery and transportation of such material causing with it an increase in operating costs associated with territory design and planning of distribution routes, the current paper present a general model based in mixed integer programming which integrates both problems, seeking to minimize the total distance traveled by the vehicle in each territory. It should be noted that one of the main features of this model is that it only considers the collection of goods in the distribution network which gives us the opportunity to offer low-cost solutions in terms of time and quality, addressing the specific and general characteristics of the emerging markets in Mexico
Route Selection with Unspecified Sites Using Knowledge Based Genetic Algorithm
Kanoh, Hitoshi; Nakamura, Nobuaki; Nakamura, Tomohiro
This paper addresses the problem of selecting a route to a given destination that traverses several non-specific sites (e.g. a bank, a gas station) as requested by a driver. The proposed solution uses a genetic algorithm that includes viral infection. The method is to generate two populations of viruses as domain specific knowledge in addition to a population of routes. A part of an arterial road is regarded as a main virus, and a road that includes a site is regarded as a site virus. An infection occurs between two points common to a candidate route and the virus, and involves the substitution of the intersections carried by the virus for those on the existing candidate route. Crossover and infection determine the easiest-to-drive and quasi-shortest route through the objective landmarks. Experiments using actual road maps show that this infection-based mechanism is an effective way of solving the problem. Our strategy is general, and can be effectively used in other optimization problems.
Morita, Yusuke; Ogihara, Naomichi; Kanai, Takashi; Suzuki, Hiromasa
2013-08-01
Three-dimensional geometric morphometric techniques have been widely used in quantitative comparisons of craniofacial morphology in humans and nonhuman primates. However, few anatomical landmarks can actually be defined on the neurocranium. In this study, an alternative method is proposed for defining semi-landmarks on neurocranial surfaces for use in detailed analysis of cranial shape. Specifically, midsagittal, nuchal, and temporal lines were approximated using Bezier curves and equally spaced points along each of the curves were defined as semi-landmarks. The shortest paths connecting pairs of anatomical landmarks as well as semi-landmarks were then calculated in order to represent the surface morphology between landmarks using equally spaced points along the paths. To evaluate the efficacy of this method, the previously outlined technique was used in morphological analysis of sexual dimorphism in modern Japanese crania. The study sample comprised 22 specimens that were used to generate 110 anatomical semi-landmarks, which were used in geometric morphometric analysis. Although variations due to sexual dimorphism in human crania are very small, differences could be identified using the proposed landmark placement, which demonstrated the efficacy of the proposed method. Copyright © 2013 Wiley Periodicals, Inc.
Analysis of the Chinese air route network as a complex network
Cai, Kai-Quan; Zhang, Jun; Du, Wen-Bo; Cao, Xian-Bin
2012-02-01
The air route network, which supports all the flight activities of the civil aviation, is the most fundamental infrastructure of air traffic management system. In this paper, we study the Chinese air route network (CARN) within the framework of complex networks. We find that CARN is a geographical network possessing exponential degree distribution, low clustering coefficient, large shortest path length and exponential spatial distance distribution that is obviously different from that of the Chinese airport network (CAN). Besides, via investigating the flight data from 2002 to 2010, we demonstrate that the topology structure of CARN is homogeneous, howbeit the distribution of flight flow on CARN is rather heterogeneous. In addition, the traffic on CARN keeps growing in an exponential form and the increasing speed of west China is remarkably larger than that of east China. Our work will be helpful to better understand Chinese air traffic systems.
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
Optimization of municipal solid waste collection and transportation routes
Energy Technology Data Exchange (ETDEWEB)
Das, Swapan, E-mail: swapan2009sajal@gmail.com; Bhattacharyya, Bidyut Kr., E-mail: bidyut53@yahoo.co.in
2015-09-15
Graphical abstract: Display Omitted - Highlights: • Profitable integrated solid waste management system. • Optimal municipal waste collection scheme between the sources and waste collection centres. • Optimal path calculation between waste collection centres and transfer stations. • Optimal waste routing between the transfer stations and processing plants. - Abstract: Optimization of municipal solid waste (MSW) collection and transportation through source separation becomes one of the major concerns in the MSW management system design, due to the fact that the existing MSW management systems suffer by the high collection and transportation cost. Generally, in a city different waste sources scatter throughout the city in heterogeneous way that increase waste collection and transportation cost in the waste management system. Therefore, a shortest waste collection and transportation strategy can effectively reduce waste collection and transportation cost. In this paper, we propose an optimal MSW collection and transportation scheme that focus on the problem of minimizing the length of each waste collection and transportation route. We first formulize the MSW collection and transportation problem into a mixed integer program. Moreover, we propose a heuristic solution for the waste collection and transportation problem that can provide an optimal way for waste collection and transportation. Extensive simulations and real testbed results show that the proposed solution can significantly improve the MSW performance. Results show that the proposed scheme is able to reduce more than 30% of the total waste collection path length.
Optimization of municipal solid waste collection and transportation routes
International Nuclear Information System (INIS)
Das, Swapan; Bhattacharyya, Bidyut Kr.
2015-01-01
Graphical abstract: Display Omitted - Highlights: • Profitable integrated solid waste management system. • Optimal municipal waste collection scheme between the sources and waste collection centres. • Optimal path calculation between waste collection centres and transfer stations. • Optimal waste routing between the transfer stations and processing plants. - Abstract: Optimization of municipal solid waste (MSW) collection and transportation through source separation becomes one of the major concerns in the MSW management system design, due to the fact that the existing MSW management systems suffer by the high collection and transportation cost. Generally, in a city different waste sources scatter throughout the city in heterogeneous way that increase waste collection and transportation cost in the waste management system. Therefore, a shortest waste collection and transportation strategy can effectively reduce waste collection and transportation cost. In this paper, we propose an optimal MSW collection and transportation scheme that focus on the problem of minimizing the length of each waste collection and transportation route. We first formulize the MSW collection and transportation problem into a mixed integer program. Moreover, we propose a heuristic solution for the waste collection and transportation problem that can provide an optimal way for waste collection and transportation. Extensive simulations and real testbed results show that the proposed solution can significantly improve the MSW performance. Results show that the proposed scheme is able to reduce more than 30% of the total waste collection path length
Directory of Open Access Journals (Sweden)
Ahmed R. Abdelaziz
2015-08-01
Full Text Available This paper presents an application of Chaotic differential evolution optimization approach meta-heuristics in solving transmission network expansion planning TNEP using an AC model associated with reactive power planning RPP. The reliabilityredundancy of network analysis optimization problems implicate selection of components with multiple choices and redundancy levels that produce maximum benefits can be subject to the cost weight and volume constraints is presented in this paper. Classical mathematical methods have failed in handling non-convexities and non-smoothness in optimization problems. As an alternative to the classical optimization approaches the meta-heuristics have attracted lot of attention due to their ability to find an almost global optimal solution in reliabilityredundancy optimization problems. Evolutionary algorithms EAs paradigms of evolutionary computation field are stochastic and robust meta-heuristics useful to solve reliabilityredundancy optimization problems. EAs such as genetic algorithm evolutionary programming evolution strategies and differential evolution are being used to find global or near global optimal solution. The Differential Evolution Algorithm DEA population-based algorithm is an optimal algorithm with powerful global searching capability but it is usually in low convergence speed and presents bad searching capability in the later evolution stage. A new Chaotic Differential Evolution algorithm CDE based on the cat map is recommended which combines DE and chaotic searching algorithm. Simulation results and comparisons show that the chaotic differential evolution algorithm using Cat map is competitive and stable in performance with other optimization approaches and other maps.
Directory of Open Access Journals (Sweden)
Shuai Zeng
2013-01-01
Full Text Available With the development of wireless technologies, mobile communication applies more and more extensively in the various walks of life. The social network of both fixed and mobile users can be seen as networked agent system. At present, kinds of devices and access network technology are widely used. Different users in this networked agent system may need different coding rates multimedia data due to their heterogeneous demand. This paper proposes a distributed flow rate control algorithm to optimize multimedia data transmission of the networked agent system with the coexisting various coding rates. In this proposed algorithm, transmission path and upload bandwidth of different coding rate data between source node, fixed and mobile nodes are appropriately arranged and controlled. On the one hand, this algorithm can provide user nodes with differentiated coding rate data and corresponding flow rate. On the other hand, it makes the different coding rate data and user nodes networked, which realizes the sharing of upload bandwidth of user nodes which require different coding rate data. The study conducts mathematical modeling on the proposed algorithm and compares the system that adopts the proposed algorithm with the existing system based on the simulation experiment and mathematical analysis. The results show that the system that adopts the proposed algorithm achieves higher upload bandwidth utilization of user nodes and lower upload bandwidth consumption of source node.
Routing in Optical and Stochastic Networks
Yang, S.
2015-01-01
In most types of networks (e.g., optical or transportation networks), finding one or more best paths from a source to a destination, is one of the biggest concerns of network users and providers. This process is known as routing. The routing problems differ accordingly depending on different
Information spread of emergency events: path searching on social networks.
Dai, Weihui; Hu, Hongzhi; Wu, Tunan; Dai, Yonghui
2014-01-01
Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.
Information Spread of Emergency Events: Path Searching on Social Networks
Directory of Open Access Journals (Sweden)
Weihui Dai
2014-01-01
Full Text Available Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.
Directory of Open Access Journals (Sweden)
Ye Sun
2011-12-01
Full Text Available In order to establish an available emergency management system, it is important to conduct effective evacuation with reliable and real time optimal route plans. This paper aims at creating a route finding strategy by considering the time dependent factors as well as uncertainties that may be encountered during the emergency management system. To combine dynamic features with the level of reliability in the process of fastest route planning, the speed distribution of typical intercity roads is studied in depth, and the strategy of modifying real time speed to a more reliable value based on speed distribution is proposed. Two algorithms of route planning have been developed to find three optimal routes with the shortest travel time and the reliability of 0.9. In order to validate the new strategy, experimental implementation of the route planning method is conducted based on road speed information acquired by field study. The results show that the proposed strategy might provide more reliable routes in dynamic traffic networks by conservatively treating roads with large speed discretion or with relative extreme real speed value.
An Energy-Efficient Target-Tracking Strategy for Mobile Sensor Networks.
Mahboubi, Hamid; Masoudimansour, Walid; Aghdam, Amir G; Sayrafian-Pour, Kamran
2017-02-01
In this paper, an energy-efficient strategy is proposed for tracking a moving target in an environment with obstacles, using a network of mobile sensors. Typically, the most dominant sources of energy consumption in a mobile sensor network are sensing, communication, and movement. The proposed algorithm first divides the field into a grid of sufficiently small cells. The grid is then represented by a graph whose edges are properly weighted to reflect the energy consumption of sensors. The proposed technique searches for near-optimal locations for the sensors in different time instants to route information from the target to destination, using a shortest path algorithm. Simulations confirm the efficacy of the proposed algorithm.
Routing protocols for wireless sensor networks: What the literature says?
Directory of Open Access Journals (Sweden)
Amit Sarkar
2016-12-01
Full Text Available Routing in Wireless Sensor Networks (WSNs plays a significant role in the field of environment-oriented monitoring, traffic monitoring, etc. Here, wide contributions that are made toward routing in WSN are explored. The paper mainly aims to categorize the routing problems and examines the routing-related optimization problems. For achieving the motive, 50 papers from the standard journals are collected and primarily reviewed in a chronological way. Later, various features that are related to energy, security, speed and reliability problems of routing are discussed. Subsequently, the literature is analyzed based on the simulation environment and experimental setup, awareness over the Quality of Service (QoS and the deployment against various applications. In addition, the optimization of the routing algorithms and the meta-heuristic study of routing optimization are explored. Routing is a vast area with numerous unsolved issues and hence, various research gaps along with future directions are also presented.
Directory of Open Access Journals (Sweden)
Li Li
2013-01-01
Full Text Available The Automatic Meter Reading (AMR network for the next generation Smart Grid is required to possess many essential functions, such as data reading and writing, intelligent power transmission, and line damage detection. However, the traditional AMR network cannot meet the previous requirement. With the development of the WiFi sensor node in the low power cost, a new kind of wireless sensor network based on the WiFi technology can be used in application. In this paper, we have designed a new architecture of WiFi-based wireless sensor network, which is suitable for the next generation AMR system. We have also proposed a new routing algorithm called Energy Saving-Based Hybrid Wireless Mesh Protocol (E-HWMP on the premise of current algorithm, which can improve the energy saving of the HWMP and be suitable for the WiFi-based wireless sensor network. The simulation results show that the life cycle of network is extended.
Directory of Open Access Journals (Sweden)
David J. Aldous
2016-04-01
Full Text Available Modeling a road network as a planar graph seems very natural. However, in studying continuum limits of such networks it is useful to take {\\em routes} rather than {\\em edges} as primitives. This article is intended to introduce the relevant (discrete setting notion of {\\em routed network} to graph theorists. We give a naive classification of all 71 topologically different such networks on 4 leaves, and pose a variety of challenging research questions.
Dehwah, Ahmad H.
2014-04-01
This poster is aimed at solving the problem of maximizing the energy margin of a solar-powered sensor network at a fixed time horizon, to maximize the network performance during an event to monitor. Using a game theoretic approach, the optimal routing maximizing the energy margin of the network at a given time under solar power forcing can be computed in a decentralized way and solved exactly through dynamic programming with a low overall complexity. We also show that this decentralized algorithm is simple enough to be implemented on practical sensor nodes. Such an algorithm would be very useful whenever the energy margin of a solar-powered sensor network has to be maximized at a specific time. © 2014 IEEE.
Dehwah, Ahmad H.; Tembine, Hamidou; Claudel, Christian G.
2014-01-01
This poster is aimed at solving the problem of maximizing the energy margin of a solar-powered sensor network at a fixed time horizon, to maximize the network performance during an event to monitor. Using a game theoretic approach, the optimal routing maximizing the energy margin of the network at a given time under solar power forcing can be computed in a decentralized way and solved exactly through dynamic programming with a low overall complexity. We also show that this decentralized algorithm is simple enough to be implemented on practical sensor nodes. Such an algorithm would be very useful whenever the energy margin of a solar-powered sensor network has to be maximized at a specific time. © 2014 IEEE.
Energy-efficient routing, modulation and spectrum allocation in elastic optical networks
Tan, Yanxia; Gu, Rentao; Ji, Yuefeng
2017-07-01
With tremendous growth in bandwidth demand, energy consumption problem in elastic optical networks (EONs) becomes a hot topic with wide concern. The sliceable bandwidth-variable transponder in EON, which can transmit/receive multiple optical flows, was recently proposed to improve a transponder's flexibility and save energy. In this paper, energy-efficient routing, modulation and spectrum allocation (EE-RMSA) in EONs with sliceable bandwidth-variable transponder is studied. To decrease the energy consumption, we develop a Mixed Integer Linear Programming (MILP) model with corresponding EE-RMSA algorithm for EONs. The MILP model jointly considers the modulation format and optical grooming in the process of routing and spectrum allocation with the objective of minimizing the energy consumption. With the help of genetic operators, the EE-RMSA algorithm iteratively optimizes the feasible routing path, modulation format and spectrum resources solutions by explore the whole search space. In order to save energy, the optical-layer grooming strategy is designed to transmit the lightpath requests. Finally, simulation results verify that the proposed scheme is able to reduce the energy consumption of the network while maintaining the blocking probability (BP) performance compare with the existing First-Fit-KSP algorithm, Iterative Flipping algorithm and EAMGSP algorithm especially in large network topology. Our results also demonstrate that the proposed EE-RMSA algorithm achieves almost the same performance as MILP on an 8-node network.
Directory of Open Access Journals (Sweden)
Enrico Budianto
2012-07-01
Full Text Available In post-disaster rehabilitation efforts, the availability of telecommunication facilities takes important role. However, the process to improve telecommunication facilities in disaster area is risky if it is done by humans. Therefore, a network method that can work efficiently, effectively, and capable to reach the widest possible area is needed. This research introduces a cluster-based routing protocol named Adaptive Cluster Based Routing Protocol (ACBRP equipped by Ant Colony Optimization method, and its implementation in a simulator developed by author. After data analysis and statistical tests, it can be concluded that routing protocol ACBRP performs better than AODV and DSR routing protocol. Pada upaya rehabilitasi pascabencana, ketersediaan fasilitas telekomunikasi memiliki peranan yang sangat penting. Namun, proses untuk memperbaiki fasilitas telekomunikasi di daerah bencana memiliki resiko jika dilakukan oleh manusia. Oleh karena itu, metode jaringan yang dapat bekerja secara efisien, efektif, dan mampu mencapai area seluas mungkin diperlukan. Penelitian ini memperkenalkan sebuah protokol routing berbasis klaster bernama Adaptive Cluster Based Routing Protocol (ACBRP, yang dilengkapi dengan metode Ant Colony Optimization, dan diimplementasikan pada simulator yang dikembangkan penulis. Setelah data dianalisis dan dilakukan uji statistik, disimpulkan bahwa protokol routing ACBRP beroperasi lebih baik daripada protokol routing AODV maupun DSR.
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Li Ran
2017-01-01
Full Text Available Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability.
Adaptive local routing strategy on a scale-free network
International Nuclear Information System (INIS)
Feng, Liu; Han, Zhao; Ming, Li; Yan-Bo, Zhu; Feng-Yuan, Ren
2010-01-01
Due to the heterogeneity of the structure on a scale-free network, making the betweennesses of all nodes become homogeneous by reassigning the weights of nodes or edges is very difficult. In order to take advantage of the important effect of high degree nodes on the shortest path communication and preferentially deliver packets by them to increase the probability to destination, an adaptive local routing strategy on a scale-free network is proposed, in which the node adjusts the forwarding probability with the dynamical traffic load (packet queue length) and the degree distribution of neighbouring nodes. The critical queue length of a node is set to be proportional to its degree, and the node with high degree has a larger critical queue length to store and forward more packets. When the queue length of a high degree node is shorter than its critical queue length, it has a higher probability to forward packets. After higher degree nodes are saturated (whose queue lengths are longer than their critical queue lengths), more packets will be delivered by the lower degree nodes around them. The adaptive local routing strategy increases the probability of a packet finding its destination quickly, and improves the transmission capacity on the scale-free network by reducing routing hops. The simulation results show that the transmission capacity of the adaptive local routing strategy is larger than that of three previous local routing strategies. (general)
QoS Supported IPTV Service Architecture over Hybrid-Tree-Based Explicit Routed Multicast Network
Directory of Open Access Journals (Sweden)
Chih-Chao Wen
2012-01-01
Full Text Available With the rapid advance in multimedia streaming and multicast transport technology, current IP multicast protocols, especially PIM-SM, become the major channel delivery mechanism for IPTV system over Internet. The goals for IPTV service are to provide two-way interactive services for viewers to select popular program channel with high quality for watching during fast channel surfing period. However, existing IP multicast protocol cannot meet above QoS requirements for IPTV applications between media server and subscribers. Therefore, we propose a cooperative scheme of hybrid-tree based on explicit routed multicast, called as HT-ERM to combine the advantages of shared tree and source tree for QoS-supported IPTV service. To increase network utilization, the constrained shortest path first (CSPF routing algorithm is designed for construction of hybrid tree to deliver the high-quality video stream over watching channel and standard quality over surfing channel. Furthermore, the Resource Reservation Protocol- Traffic Engineering (RSVP-TE is used as signaling mechanism to set up QoS path for multicast channel admission control. Our simulation results demonstrated that the proposed HT-ERM scheme outperforms other multicast QoS-based delivery scheme in terms of channel switching delay, resource utilization, and blocking ratio for IPTV service.
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.
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T. Velmurugan
2016-03-01
Full Text Available Heterogeneous wireless networks are an integration of two different networks. For better performance, connections are to be exchanged among the different networks using seamless Vertical Handoff. The evolutionary algorithm of invasive weed optimization algorithm popularly known as the IWO has been used in this paper, to solve the Vertical Handoff (VHO and Horizontal Handoff (HHO problems. This integer coded algorithm is based on the colonizing behavior of weed plants and has been developed to optimize the system load and reduce the battery power consumption of the Mobile Node (MN. Constraints such as Receiver Signal Strength (RSS, battery lifetime, mobility, load and so on are taken into account. Individual as well as a combination of a number of factors are considered during decision process to make it more effective. This paper brings out the novel method of IWO algorithm for decision making during Vertical Handoff. Therefore the proposed VHO decision making algorithm is compared with the existing SSF and OPTG methods.
A hybrid neural network – world cup optimization algorithm for melanoma detection
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Razmjooy Navid
2018-03-01
Full Text Available One of the most dangerous cancers in humans is Melanoma. However, early detection of melanoma can help us to cure it completely. This paper presents a new efficient method to detect malignancy in melanoma via images. At first, the extra scales are eliminated by using edge detection and smoothing. Afterwards, the proposed method can be utilized to segment the cancer images. Finally, the extra information is eliminated by morphological operations and used to focus on the area which melanoma boundary potentially exists. To do this, World Cup Optimization algorithm is utilized to optimize an MLP neural Networks (ANN. World Cup Optimization algorithm is a new meta-heuristic algorithm which is recently presented and has a good performance in some optimization problems. WCO is a derivative-free, Meta-Heuristic algorithm, mimicking the world’s FIFA competitions. World cup Optimization algorithm is a global search algorithm while gradient-based back propagation method is local search. In this proposed algorithm, multi-layer perceptron network (MLP employs the problem’s constraints and WCO algorithm attempts to minimize the root mean square error. Experimental results show that the proposed method can develop the performance of the standard MLP algorithm significantly.
Nuclear reactors project optimization based on neural network and genetic algorithm
International Nuclear Information System (INIS)
Pereira, Claudio M.N.A.; Schirru, Roberto; Martinez, Aquilino S.
1997-01-01
This work presents a prototype of a system for nuclear reactor core design optimization based on genetic algorithms and artificial neural networks. A neural network is modeled and trained in order to predict the flux and the neutron multiplication factor values based in the enrichment, network pitch and cladding thickness, with average error less than 2%. The values predicted by the neural network are used by a genetic algorithm in this heuristic search, guided by an objective function that rewards the high flux values and penalizes multiplication factors far from the required value. Associating the quick prediction - that may substitute the reactor physics calculation code - with the global optimization capacity of the genetic algorithm, it was obtained a quick and effective system for nuclear reactor core design optimization. (author). 11 refs., 8 figs., 3 tabs
Energy Aware Cluster Based Routing Scheme For Wireless Sensor Network
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Roy Sohini
2015-09-01
Full Text Available Wireless Sensor Network (WSN has emerged as an important supplement to the modern wireless communication systems due to its wide range of applications. The recent researches are facing the various challenges of the sensor network more gracefully. However, energy efficiency has still remained a matter of concern for the researches. Meeting the countless security needs, timely data delivery and taking a quick action, efficient route selection and multi-path routing etc. can only be achieved at the cost of energy. Hierarchical routing is more useful in this regard. The proposed algorithm Energy Aware Cluster Based Routing Scheme (EACBRS aims at conserving energy with the help of hierarchical routing by calculating the optimum number of cluster heads for the network, selecting energy-efficient route to the sink and by offering congestion control. Simulation results prove that EACBRS performs better than existing hierarchical routing algorithms like Distributed Energy-Efficient Clustering (DEEC algorithm for heterogeneous wireless sensor networks and Energy Efficient Heterogeneous Clustered scheme for Wireless Sensor Network (EEHC.
Social Milieu Oriented Routing: A New Dimension to Enhance Network Security in WSNs.
Liu, Lianggui; Chen, Li; Jia, Huiling
2016-02-19
In large-scale wireless sensor networks (WSNs), in order to enhance network security, it is crucial for a trustor node to perform social milieu oriented routing to a target a trustee node to carry out trust evaluation. This challenging social milieu oriented routing with more than one end-to-end Quality of Trust (QoT) constraint has proved to be NP-complete. Heuristic algorithms with polynomial and pseudo-polynomial-time complexities are often used to deal with this challenging problem. However, existing solutions cannot guarantee the efficiency of searching; that is, they can hardly avoid obtaining partial optimal solutions during a searching process. Quantum annealing (QA) uses delocalization and tunneling to avoid falling into local minima without sacrificing execution time. This has been proven a promising way to many optimization problems in recently published literatures. In this paper, for the first time, with the help of a novel approach, that is, configuration path-integral Monte Carlo (CPIMC) simulations, a QA-based optimal social trust path (QA_OSTP) selection algorithm is applied to the extraction of the optimal social trust path in large-scale WSNs. Extensive experiments have been conducted, and the experiment results demonstrate that QA_OSTP outperforms its heuristic opponents.
Directory of Open Access Journals (Sweden)
Michaeli Shulamit
2007-10-01
Full Text Available Abstract Background In recent years, RNA molecules that are not translated into proteins (ncRNAs have drawn a great deal of attention, as they were shown to be involved in many cellular functions. One of the most important computational problems regarding ncRNA is to predict the secondary structure of a molecule from its sequence. In particular, we attempted to predict the secondary structure for a set of unaligned ncRNA molecules that are taken from the same family, and thus presumably have a similar structure. Results We developed the RNAspa program, which comparatively predicts the secondary structure for a set of ncRNA molecules in linear time in the number of molecules. We observed that in a list of several hundred suboptimal minimal free energy (MFE predictions, as provided by the RNAsubopt program of the Vienna package, it is likely that at least one suggested structure would be similar to the true, correct one. The suboptimal solutions of each molecule are represented as a layer of vertices in a graph. The shortest path in this graph is the basis for structural predictions for the molecule. We also show that RNA secondary structures can be compared very rapidly by a simple string Edit-Distance algorithm with a minimal loss of accuracy. We show that this approach allows us to more deeply explore the suboptimal structure space. Conclusion The algorithm was tested on three datasets which include several ncRNA families taken from the Rfam database. These datasets allowed for comparison of the algorithm with other methods. In these tests, RNAspa performed better than four other programs.
Partial Path Column Generation for the Vehicle Routing Problem
DEFF Research Database (Denmark)
Jepsen, Mads Kehlet; Petersen, Bjørn
This paper presents a column generation algorithm for the Capacitated Vehicle Routing Problem (CVRP) and the Vehicle Routing Problem with Time Windows (VRPTW). Traditionally, column generation models of the CVRP and VRPTW have consisted of a Set Partitioning master problem with each column...... of the giant tour’; a so-called partial path, i.e., not necessarily starting and ending in the depot. This way, the length of the partial path can be bounded and a better control of the size of the solution space for the pricing problem can be obtained....
PERENCANAAN RUTE PERJALANAN DI JAWA TIMUR DENGAN DUKUNGAN GIS MENGGUNAKAN METODE DIJKSTRA S
Directory of Open Access Journals (Sweden)
Kartika Gunadi
2002-01-01
Full Text Available Many people need geographical information nowadays, such as: the distance between areas, information about some areas, information about nature resources in that area, to search for accident area, and others geographical information. Geographical Information System (GIS is only one from many others solution to seek for geographical information. This research aim is to make software that can give some geographical information for shortest path between towns in East Java. Others information that can be gain is information about governmental, population, tourism places, mountains, special food, handicraft, and traditional art. This software is designed with database not using satellite, so much cheaper compare with using satellite. This software use Dijkstra's method to seek the shortest path from one node to another node in the picture, so this program can't give alternative path. GIS can give answer for anything that related with geographical situation. Peoples can use GIS power to reach for a better life. Abstract in Bahasa Indonesia : Informasi mengenai geografi semakin dibutuhkan oleh banyak pihak, misalnya informasi untuk mengetahui jarak antara satu daerah dengan daerah lain, informasi seputar daerah yang diinginkan, informasi tentang sumber daya alam yang dicari, informasi untuk menemukan lokasi kecelakaan dengan cepat, dan banyak informasi mengenai geografi lainnya. Geographical Information Systems (GIS merupakan salah satu solusi untuk mendapatkan informasi geografi tersebut. Tujuan perancangan adalah membuat suatu perangkat lunak yang dapat memberikan informasi geografi mengenai rute jalan terpendek antara kota yang satu dengan kota yang lainnya di Jawa Timur. Sedangkan informasi lainnya yang dapat diperoleh antara lain informasi mengenai pemerintahan, jumlah penduduk, tempat wisata, nama gunung, makanan khas, kerajinan, dan `kesenian tradisional yang berasal dari suatu daerah. Program ini dirancang tanpa menggunakan satelit namun hanya
Directory of Open Access Journals (Sweden)
Wen-Xiang Wu
2014-01-01
Full Text Available The cost-based system optimum problem in networks with continuously distributed value of time is formulated as a path-based form, which cannot be solved by the Frank-Wolfe algorithm. In light of magnitude improvement in the availability of computer memory in recent years, path-based algorithms have been regarded as a viable approach for traffic assignment problems with reasonably large network sizes. We develop a path-based gradient projection algorithm for solving the cost-based system optimum model, based on Goldstein-Levitin-Polyak method which has been successfully applied to solve standard user equilibrium and system optimum problems. The Sioux Falls network tested is used to verify the effectiveness of the algorithm.
A bat algorithm with mutation for UCAV path planning.
Wang, Gaige; Guo, Lihong; Duan, Hong; Liu, Luo; Wang, Heqi
2012-01-01
Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models.
Jakovetic, Dusan; Xavier, João; Moura, José M. F.
2011-08-01
We study distributed optimization in networked systems, where nodes cooperate to find the optimal quantity of common interest, x=x^\\star. The objective function of the corresponding optimization problem is the sum of private (known only by a node,) convex, nodes' objectives and each node imposes a private convex constraint on the allowed values of x. We solve this problem for generic connected network topologies with asymmetric random link failures with a novel distributed, decentralized algorithm. We refer to this algorithm as AL-G (augmented Lagrangian gossiping,) and to its variants as AL-MG (augmented Lagrangian multi neighbor gossiping) and AL-BG (augmented Lagrangian broadcast gossiping.) The AL-G algorithm is based on the augmented Lagrangian dual function. Dual variables are updated by the standard method of multipliers, at a slow time scale. To update the primal variables, we propose a novel, Gauss-Seidel type, randomized algorithm, at a fast time scale. AL-G uses unidirectional gossip communication, only between immediate neighbors in the network and is resilient to random link failures. For networks with reliable communication (i.e., no failures,) the simplified, AL-BG (augmented Lagrangian broadcast gossiping) algorithm reduces communication, computation and data storage cost. We prove convergence for all proposed algorithms and demonstrate by simulations the effectiveness on two applications: l_1-regularized logistic regression for classification and cooperative spectrum sensing for cognitive radio networks.
An Intuitive Dominant Test Algorithm of CP-nets Applied on Wireless Sensor Network
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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.
An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.
Vimalarani, C; Subramanian, R; Sivanandam, S N
2016-01-01
Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.
An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network
Directory of Open Access Journals (Sweden)
C. Vimalarani
2016-01-01
Full Text Available Wireless Sensor Network (WSN is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.
Energy Technology Data Exchange (ETDEWEB)
Niknam, Taher; Meymand, Hamed Zeinoddini; Nayeripour, Majid [Electrical and Electronic Engineering Department, Shiraz University of Technology, Shiraz (Iran)
2010-08-15
Fuel cell power plants (FCPPs) have been taken into a great deal of consideration in recent years. The continuing growth of the power demand together with environmental constraints is increasing interest to use FCPPs in power system. Since FCPPs are usually connected to distribution network, the effect of FCPPs on distribution network is more than other sections of power system. One of the most important issues in distribution networks is optimal operation management (OOM) which can be affected by FCPPs. This paper proposes a new approach for optimal operation management of distribution networks including FCCPs. In the article, we consider the total electrical energy losses, the total electrical energy cost and the total emission as the objective functions which should be minimized. Whereas the optimal operation in distribution networks has a nonlinear mixed integer optimization problem, the optimal solution could be obtained through an evolutionary method. We use a new evolutionary algorithm based on Fuzzy Adaptive Particle Swarm Optimization (FAPSO) to solve the optimal operation problem and compare this method with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO) and Tabu Search (TS) over two distribution test feeders. (author)
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Vatutin Eduard
2017-12-01
Full Text Available The article deals with the problem of analysis of effectiveness of the heuristic methods with limited depth-first search techniques of decision obtaining in the test problem of getting the shortest path in graph. The article briefly describes the group of methods based on the limit of branches number of the combinatorial search tree and limit of analyzed subtree depth used to solve the problem. The methodology of comparing experimental data for the estimation of the quality of solutions based on the performing of computational experiments with samples of graphs with pseudo-random structure and selected vertices and arcs number using the BOINC platform is considered. It also shows description of obtained experimental results which allow to identify the areas of the preferable usage of selected subset of heuristic methods depending on the size of the problem and power of constraints. It is shown that the considered pair of methods is ineffective in the selected problem and significantly inferior to the quality of solutions that are provided by ant colony optimization method and its modification with combinatorial returns.
Vatutin, Eduard
2017-12-01
The article deals with the problem of analysis of effectiveness of the heuristic methods with limited depth-first search techniques of decision obtaining in the test problem of getting the shortest path in graph. The article briefly describes the group of methods based on the limit of branches number of the combinatorial search tree and limit of analyzed subtree depth used to solve the problem. The methodology of comparing experimental data for the estimation of the quality of solutions based on the performing of computational experiments with samples of graphs with pseudo-random structure and selected vertices and arcs number using the BOINC platform is considered. It also shows description of obtained experimental results which allow to identify the areas of the preferable usage of selected subset of heuristic methods depending on the size of the problem and power of constraints. It is shown that the considered pair of methods is ineffective in the selected problem and significantly inferior to the quality of solutions that are provided by ant colony optimization method and its modification with combinatorial returns.
Hoenicke, Dirk
2014-12-02
Disclosed are a unified method and apparatus to classify, route, and process injected data packets into a network so as to belong to a plurality of logical networks, each implementing a specific flow of data on top of a common physical network. The method allows to locally identify collectives of packets for local processing, such as the computation of the sum, difference, maximum, minimum, or other logical operations among the identified packet collective. Packets are injected together with a class-attribute and an opcode attribute. Network routers, employing the described method, use the packet attributes to look-up the class-specific route information from a local route table, which contains the local incoming and outgoing directions as part of the specifically implemented global data flow of the particular virtual network.
An optimal control-based algorithm for hybrid electric vehicle using preview route information
Ngo, D.V.; Hofman, T.; Steinbuch, M.; Serrarens, A.F.A.
2010-01-01
Control strategies for Hybrid Electric Vehicles (HEVs) are generally aimed at optimally choosing the power distribution between the internal combustion engine and the electric motor in order to minimize the fuel consumption and/or emissions. Using vehicle navigation systems in combination with
Elements of an algorithm for optimizing a parameter-structural neural network
Mrówczyńska, Maria
2016-06-01
The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.
Route planning algorithms: Planific@ Project
Directory of Open Access Journals (Sweden)
Gonzalo Martín Ortega
2009-12-01
Full Text Available Planific@ is a route planning project for the city of Madrid (Spain. Its main aim is to develop an intelligence system capable of routing people from one place in the city to any other using the public transport. In order to do this, it is necessary to take into account such things as: time, traffic, user preferences, etc. Before beginning to design the project is necessary to make a comprehensive study of the variety of main known route planning algorithms suitable to be used in this project.
Pheromone Static Routing Strategy for Complex Networks
Hu, Mao-Bin; Henry, Y. K. Lau; Ling, Xiang; Jiang, Rui
2012-12-01
We adopt the concept of using pheromones to generate a set of static paths that can reach the performance of global dynamic routing strategy [Phys. Rev. E 81 (2010) 016113]. The path generation method consists of two stages. In the first stage, a pheromone is dropped to the nodes by packets forwarded according to the global dynamic routing strategy. In the second stage, pheromone static paths are generated according to the pheromone density. The output paths can greatly improve traffic systems' overall capacity on different network structures, including scale-free networks, small-world networks and random graphs. Because the paths are static, the system needs much less computational resources than the global dynamic routing strategy.
A Particle Swarm Optimization Algorithm for Neural Networks in Recognition of Maize Leaf Diseases
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Zhiyong ZHANG
2014-03-01
Full Text Available The neural networks have significance on recognition of crops disease diagnosis? but it has disadvantage of slow convergent speed and shortcoming of local optimum. In order to identify the maize leaf diseases by using machine vision more accurately, we propose an improved particle swarm optimization algorithm for neural networks. With the algorithm, the neural network property is improved. It reasonably confirms threshold and connection weight of neural network, and improves capability of solving problems in the image recognition. At last, an example of the emulation shows that neural network model based on recognizes significantly better than without optimization. Model accuracy has been improved to a certain extent to meet the actual needs of maize leaf diseases recognition.
Zone routing in a torus network
Chen, Dong; Heidelberger, Philip; Kumar, Sameer
2013-01-29
A system for routing data in a network comprising a network logic device at a sending node for determining a path between the sending node and a receiving node, wherein the network logic device sets one or more selection bits and one or more hint bits within the data packet, a control register for storing one or more masks, wherein the network logic device uses the one or more selection bits to select a mask from the control register and the network logic device applies the selected mask to the hint bits to restrict routing of the data packet to one or more routing directions for the data packet within the network and selects one of the restricted routing directions from the one or more routing directions and sends the data packet along a link in the selected routing direction toward the receiving node.
Optimal recombination in genetic algorithms for combinatorial optimization problems: Part II
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Eremeev Anton V.
2014-01-01
Full Text Available This paper surveys results on complexity of the optimal recombination problem (ORP, which consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. In Part II, we consider the computational complexity of ORPs arising in genetic algorithms for problems on permutations: the Travelling Salesman Problem, the Shortest Hamilton Path Problem and the Makespan Minimization on Single Machine and some other related problems. The analysis indicates that the corresponding ORPs are NP-hard, but solvable by faster algorithms, compared to the problems they are derived from.
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Yueyue Deng
2013-01-01
Full Text Available Dynamic and unstructured multiple cooperative autonomous underwater vehicle (AUV missions are highly complex operations, and task allocation and path planning are made significantly more challenging under realistic underwater acoustic communication constraints. This paper presents a solution for the task allocation and path planning for multiple AUVs under marginal acoustic communication conditions: a location-aided task allocation framework (LAAF algorithm for multitarget task assignment and the grid-based multiobjective optimal programming (GMOOP mathematical model for finding an optimal vehicle command decision given a set of objectives and constraints. Both the LAAF and GMOOP algorithms are well suited in poor acoustic network condition and dynamic environment. Our research is based on an existing mobile ad hoc network underwater acoustic simulator and blind flooding routing protocol. Simulation results demonstrate that the location-aided auction strategy performs significantly better than the well-accepted auction algorithm developed by Bertsekas in terms of task-allocation time and network bandwidth consumption. We also demonstrate that the GMOOP path-planning technique provides an efficient method for executing multiobjective tasks by cooperative agents with limited communication capabilities. This is in contrast to existing multiobjective action selection methods that are limited to networks where constant, reliable communication is assumed to be available.
Optimal parallel algorithms for problems modeled by a family of intervals
Olariu, Stephan; Schwing, James L.; Zhang, Jingyuan
1992-01-01
A family of intervals on the real line provides a natural model for a vast number of scheduling and VLSI problems. Recently, a number of parallel algorithms to solve a variety of practical problems on such a family of intervals have been proposed in the literature. Computational tools are developed, and it is shown how they can be used for the purpose of devising cost-optimal parallel algorithms for a number of interval-related problems including finding a largest subset of pairwise nonoverlapping intervals, a minimum dominating subset of intervals, along with algorithms to compute the shortest path between a pair of intervals and, based on the shortest path, a parallel algorithm to find the center of the family of intervals. More precisely, with an arbitrary family of n intervals as input, all algorithms run in O(log n) time using O(n) processors in the EREW-PRAM model of computation.
Vessels Route Planning Problem with Uncertain Data
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Tomasz Neumann
2016-09-01
Full Text Available The purpose of this paper is to find a solution for route planning in a transport networks, where the costs of tracks, factor of safety and travel time are ambiguous. This approach is based on the Dempster-Shafer theory and well known Dijkstra's algorithm. In this approach important are the influencing factors of the mentioned coefficients using uncertain possibilities presented by probability intervals. Based on these intervals the quality intervals of each route can be determined. Applied decision rules can be described by the end user.
Optimization of the Compensation of a Meshed MV Network by a Modified Genetic Algorithm
DEFF Research Database (Denmark)
Nielsen, Hans; Paar, M.; Toman, P.
2007-01-01
The article discusses the utilization of a modified genetic algorithm (GA) for the optimization of the shunt compensation in meshed and radial MV distribution networks. The algorithm looks for minimum costs of the network power losses and minimum capital and operating costs of applied capacitors......, all of this under limitations specified by a multicriteria penalization function. The parallel evolution branches in the GA are used for the purpose of the optimization accelaration. The application of this GA has been implemented in Matlab. The evaluation part of the GA implementation is based...... on the steady-state analysis using a linear one-line diagram model of a power network. The results of steady-state solutions are compared with the results from the DIgSILENT PowerFactory program. Its practical applicability is demonstrated on examples of 22 kV and meshed overhead distribution networks....
Hao, Yufang; Xie, Shaodong
2018-03-01
Air quality monitoring networks play a significant role in identifying the spatiotemporal patterns of air pollution, and they need to be deployed efficiently, with a minimum number of sites. The revision and optimal adjustment of existing monitoring networks is crucial for cities that have undergone rapid urban expansion and experience temporal variations in pollution patterns. The approach based on the Weather Research and Forecasting-California PUFF (WRF-CALPUFF) model and genetic algorithm (GA) was developed to design an optimal monitoring network. The maximization of coverage with minimum overlap and the ability to detect violations of standards were developed as the design objectives for redistributed networks. The non-dominated sorting genetic algorithm was applied to optimize the network size and site locations simultaneously for Shijiazhuang city, one of the most polluted cities in China. The assessment on the current network identified the insufficient spatial coverage of SO2 and NO2 monitoring for the expanding city. The optimization results showed that significant improvements were achieved in multiple objectives by redistributing the original network. Efficient coverage of the resulting designs improved to 60.99% and 76.06% of the urban area for SO2 and NO2, respectively. The redistributing design for multi-pollutant including 8 sites was also proposed, with the spatial representation covered 52.30% of the urban area and the overlapped areas decreased by 85.87% compared with the original network. The abilities to detect violations of standards were not improved as much as the other two objectives due to the conflicting nature between the multiple objectives. Additionally, the results demonstrated that the algorithm was slightly sensitive to the parameter settings, with the number of generations presented the most significant effect. Overall, our study presents an effective and feasible procedure for air quality network optimization at a city scale.
Verhoeven, Hannah; Van Hecke, Linde; Van Dyck, Delfien; Baert, Tim; Van de Weghe, Nico; Clarys, Peter; Deforche, Benedicte; Van Cauwenberg, Jelle
2018-05-29
The objective evaluation of the physical environmental characteristics (e.g. speed limit, cycling infrastructure) along adolescents' actual cycling routes remains understudied, although it may provide important insights into why adolescents prefer one cycling route over another. The present study aims to gain insight into the physical environmental characteristics determining the route choice of adolescent cyclists by comparing differences in physical environmental characteristics between their actual cycling routes and the shortest possible cycling routes. Adolescents (n = 204; 46.5% boys; 14.4 ± 1.2 years) recruited at secondary schools in and around Ghent (city in Flanders, northern part of Belgium) were instructed to wear a Global Positioning System device in order to identify cycling trips. For all identified cycling trips, the shortest possible route that could have been taken was calculated. Actual cycling routes that were not the shortest possible cycling routes were divided into street segments. Segments were audited with a Google Street View-based tool to assess physical environmental characteristics along actual and shortest cycling routes. Out of 160 actual cycling trips, 73.1% did not differ from the shortest possible cycling route. For actual cycling routes that were not the shortest cycling route, a speed limit of 30 km/h, roads having few buildings with windows on the street side and roads without cycle lane were more frequently present compared to the shortest possible cycling routes. A mixed land use, roads with commercial destinations, arterial roads, cycle lanes separated from traffic by white lines, small cycle lanes and cycle lanes covered by lighting were less frequently present along actual cycling routes compared to the shortest possible cycling routes. Results showed that distance mainly determines the route along which adolescents cycle. In addition, adolescents cycled more along residential streets (even if no cycle lane was
A simultaneous navigation and radiation evasion algorithm (SNARE)
Energy Technology Data Exchange (ETDEWEB)
Khasawneh, Mohammed A., E-mail: mkha@ieee.org [Department of Electrical Engineering, Jordan University of Science and Technology, Irbid 221 10 (Jordan); Jaradat, Mohammad A., E-mail: majaradat@just.edu.jo [Department of Mechanical Engineering, Jordan University of Science and Technology, Irbid 221 10 (Jordan); Al-Shboul, Zeina Aman M., E-mail: xeinaaman@gmail.com [Department of Electrical Engineering, Jordan University of Science and Technology, Irbid 221 10 (Jordan)
2013-12-15
Highlights: • A new navigation algorithm for radiation evasion around nuclear facilities. • An optimization criteria minimized under algorithm operation. • A man-borne device guiding the occupational worker towards paths that warrant least radiation × time products. • Benefits of using localized navigation as opposed to global navigation schemas. • A path discrimination function for finding the navigational paths exhibiting the least amounts of radiation. - Abstract: In this paper, we address the issue of localization as pertains to indoor navigation under radiation contaminated environments. In this context, navigation, in the absence of any GPS signals, is guided by the location of the sensors that make up the entire wireless sensor network in a given locality within a nuclear facility. It, also, draws on the radiation levels as measured by the sensors around a given locale. Here, localization is inherently embedded into the algorithm presented in (Khasawneh et al., 2011a, 2011b) which was designed to provide navigational guidance to optimize any of two criteria: “Radiation Evasion” and “Nearest Exit”. As such, the algorithm can either be applied to setting a navigational “lowest” radiation exposure path from an initial point A to some other point B; a case typical of occupational workers performing maintenance operations around the facility; or providing a radiation-safe passage from point A to the nearest exit. Algorithm's navigational performance is tested under statistical reference, wherein for a given number of runs (trials) algorithm performance is evaluated as a function of the number of steps of look-ahead it uses to acquire navigational information, and is compared against the performance of the renowned Dijkstra global navigation algorithm. This is done with reference to the amount of (radiation × time) product and that of the time needed to reach an exit point, under the two optimization criteria. To evaluate algorithm
A simultaneous navigation and radiation evasion algorithm (SNARE)
International Nuclear Information System (INIS)
Khasawneh, Mohammed A.; Jaradat, Mohammad A.; Al-Shboul, Zeina Aman M.
2013-01-01
Highlights: • A new navigation algorithm for radiation evasion around nuclear facilities. • An optimization criteria minimized under algorithm operation. • A man-borne device guiding the occupational worker towards paths that warrant least radiation × time products. • Benefits of using localized navigation as opposed to global navigation schemas. • A path discrimination function for finding the navigational paths exhibiting the least amounts of radiation. - Abstract: In this paper, we address the issue of localization as pertains to indoor navigation under radiation contaminated environments. In this context, navigation, in the absence of any GPS signals, is guided by the location of the sensors that make up the entire wireless sensor network in a given locality within a nuclear facility. It, also, draws on the radiation levels as measured by the sensors around a given locale. Here, localization is inherently embedded into the algorithm presented in (Khasawneh et al., 2011a, 2011b) which was designed to provide navigational guidance to optimize any of two criteria: “Radiation Evasion” and “Nearest Exit”. As such, the algorithm can either be applied to setting a navigational “lowest” radiation exposure path from an initial point A to some other point B; a case typical of occupational workers performing maintenance operations around the facility; or providing a radiation-safe passage from point A to the nearest exit. Algorithm's navigational performance is tested under statistical reference, wherein for a given number of runs (trials) algorithm performance is evaluated as a function of the number of steps of look-ahead it uses to acquire navigational information, and is compared against the performance of the renowned Dijkstra global navigation algorithm. This is done with reference to the amount of (radiation × time) product and that of the time needed to reach an exit point, under the two optimization criteria. To evaluate algorithm
Survivability and Impairment-aware Routing in Optical Networks : An Algorithmic Study
Beshir, A.A.
2011-01-01
Optical networks employing Wavelength Division Multiplexing (WDM) technology allow the multiplexing of several independent wavelength channels into a fiber. Since each wavelength channel operates independently at several Gb/s, WDM optical networks offer a tremendous transport capacity (which is in
Distributed routing algorithms to manage power flow in agent-based active distribution network
Nguyen, H.P.; Kling, W.L.; Georgiadis, G.; Papatriantafilou, M.; Anh-Tuan, L.; Bertling, L.
2010-01-01
The current transition from passive to active electric distribution networks comes with problems and challenges on bi-directional power flow in the network and the uncertainty in the forecast of power generation from grid-connected renewable and distributed energy sources. The power flow management
Mobile Ad Hoc Network Energy Cost Algorithm Based on Artificial Bee Colony
Directory of Open Access Journals (Sweden)
Mustafa Tareq
2017-01-01
Full Text Available A mobile ad hoc network (MANET is a collection of mobile nodes that dynamically form a temporary network without using any existing network infrastructure. MANET selects a path with minimal number of intermediate nodes to reach the destination node. As the distance between each node increases, the quantity of transmission power increases. The power level of nodes affects the simplicity with which a route is constituted between a couple of nodes. This study utilizes the swarm intelligence technique through the artificial bee colony (ABC algorithm to optimize the energy consumption in a dynamic source routing (DSR protocol in MANET. The proposed algorithm is called bee DSR (BEEDSR. The ABC algorithm is used to identify the optimal path from the source to the destination to overcome energy problems. The performance of the BEEDSR algorithm is compared with DSR and bee-inspired protocols (BeeIP. The comparison was conducted based on average energy consumption, average throughput, average end-to-end delay, routing overhead, and packet delivery ratio performance metrics, varying the node speed and packet size. The BEEDSR algorithm is superior in performance than other protocols in terms of energy conservation and delay degradation relating to node speed and packet size.
Optimization of China Crude Oil Transportation Network with Genetic Ant Colony Algorithm
Directory of Open Access Journals (Sweden)
Yao Wang
2015-08-01
Full Text Available Taking into consideration both shipping and pipeline transport, this paper first analysed the risk factors for different modes of crude oil import transportation. Then, based on the minimum of both transportation cost and overall risk, a multi-objective programming model was established to optimize the transportation network of crude oil import, and the genetic algorithm and ant colony algorithm were employed to solve the problem. The optimized result shows that VLCC (Very Large Crude Carrier is superior in long distance sea transportation, whereas pipeline transport is more secure than sea transport. Finally, this paper provides related safeguard suggestions on crude oil import transportation.
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.
Directory of Open Access Journals (Sweden)
Aydin Azizi
2017-01-01
Full Text Available Recent advances in modern manufacturing industries have created a great need to track and identify objects and parts by obtaining real-time information. One of the main technologies which has been utilized for this need is the Radio Frequency Identification (RFID system. As a result of adopting this technology to the manufacturing industry environment, RFID Network Planning (RNP has become a challenge. Mainly RNP deals with calculating the number and position of antennas which should be deployed in the RFID network to achieve full coverage of the tags that need to be read. The ultimate goal of this paper is to present and evaluate a way of modelling and optimizing nonlinear RNP problems utilizing artificial intelligence (AI techniques. This effort has led the author to propose a novel AI algorithm, which has been named “hybrid AI optimization technique,” to perform optimization of RNP as a hard learning problem. The proposed algorithm is composed of two different optimization algorithms: Redundant Antenna Elimination (RAE and Ring Probabilistic Logic Neural Networks (RPLNN. The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS, and results have been compared with Genetic Algorithm (GA that demonstrates the feasibility of the proposed architecture successfully.
Directory of Open Access Journals (Sweden)
Peppino Fazio
2013-01-01
Full Text Available Vehicular Ad hoc NETworks (VANETs represent a particular mobile technology that permits the communication among vehicles, offering security and comfort. Nowadays, distributed mobile wireless computing is becoming a very important communications paradigm, due to its flexibility to adapt to different mobile applications. VANETs are a practical example of data exchanging among real mobile nodes. To enable communications within an ad-hoc network, characterized by continuous node movements, routing protocols are needed to react to frequent changes in network topology. In this paper, the attention is focused mainly on the network layer of VANETs, proposing a novel approach to reduce the interference level during mobile transmission, based on the multi-channel nature of IEEE 802.11p (1609.4 standard. In this work a new routing protocol based on Distance Vector algorithm is presented to reduce the delay end to end and to increase packet delivery ratio (PDR and throughput in VANETs. A new metric is also proposed, based on the maximization of the average Signal-to-Interference Ratio (SIR level and the link duration probability between two VANET nodes. In order to relieve the effects of the co-channel interference perceived by mobile nodes, transmission channels are switched on a basis of a periodical SIR evaluation. A Network Simulator has been used for implementing and testing the proposed idea.
Routing and Scheduling Algorithms in Resource-Limited Wireless Multi-Hop Networks
National Research Council Canada - National Science Library
Michail, Anastassios
2001-01-01
...) to transmit their messages to the desired destinations. The distinguishing features of such all-wireless network architectures give rise to new trade-offs between traditional concerns in wireless communications...
Directory of Open Access Journals (Sweden)
Kazem Mohammadi- Aghdam
2015-10-01
Full Text Available This paper proposes the application of a new version of the heuristic particle swarm optimization (PSO method for designing water distribution networks (WDNs. The optimization problem of looped water distribution networks is recognized as an NP-hard combinatorial problem which cannot be easily solved using traditional mathematical optimization techniques. In this paper, the concept of dynamic swarm size is considered in an attempt to increase the convergence speed of the original PSO algorithm. In this strategy, the size of the swarm is dynamically changed according to the iteration number of the algorithm. Furthermore, a novel mutation approach is introduced to increase the diversification property of the PSO and to help the algorithm to avoid trapping in local optima. The new version of the PSO algorithm is called dynamic mutated particle swarm optimization (DMPSO. The proposed DMPSO is then applied to solve WDN design problems. Finally, two illustrative examples are used for comparison to verify the efficiency of the proposed DMPSO as compared to other intelligent algorithms.
Robustness of airline route networks
Lordan, Oriol; Sallan, Jose M.; Escorihuela, Nuria; Gonzalez-Prieto, David
2016-03-01
Airlines shape their route network by defining their routes through supply and demand considerations, paying little attention to network performance indicators, such as network robustness. However, the collapse of an airline network can produce high financial costs for the airline and all its geographical area of influence. The aim of this study is to analyze the topology and robustness of the network route of airlines following Low Cost Carriers (LCCs) and Full Service Carriers (FSCs) business models. Results show that FSC hubs are more central than LCC bases in their route network. As a result, LCC route networks are more robust than FSC networks.
Practical mine ventilation optimization based on genetic algorithms for free splitting networks
Energy Technology Data Exchange (ETDEWEB)
Acuna, E.; Maynard, R.; Hall, S. [Laurentian Univ., Sudbury, ON (Canada). Mirarco Mining Innovation; Hardcastle, S.G.; Li, G. [Natural Resources Canada, Sudbury, ON (Canada). CANMET Mining and Mineral Sciences Laboratories; Lowndes, I.S. [Nottingham Univ., Nottingham (United Kingdom). Process and Environmental Research Division; Tonnos, A. [Bestech, Sudbury, ON (Canada)
2010-07-01
The method used to optimize the design and operation of mine ventilation has generally been based on case studies and expert knowledge. It has yet to benefit from optimization techniques used and proven in other fields of engineering. Currently, optimization of mine ventilation systems is a manual based decision process performed by an experienced mine ventilation specialist assisted by commercial ventilation distribution solvers. These analysis tools are widely used in the mining industry to evaluate the practical and economic viability of alternative ventilation system configurations. The scenario which is usually selected is the one that reports the lowest energy consumption while delivering the required airflow distribution. Since most commercial solvers do not have an integrated optimization algorithm network, the process of generating a series of potential ventilation solutions using the conventional iterative design strategy can be time consuming. For that reason, a genetic algorithm (GA) optimization routine was developed in combination with a ventilation solver to determine the potential optimal solutions of a primary mine ventilation system based on a free splitting network. The optimization method was used in a small size mine ventilation network. The technique was shown to have the capacity to generate good feasible solutions and improve upon the manual results obtained by mine ventilation specialists. 9 refs., 7 tabs., 3 figs.
Joint Power Charging and Routing in Wireless Rechargeable Sensor Networks.
Jia, Jie; Chen, Jian; Deng, Yansha; Wang, Xingwei; Aghvami, Abdol-Hamid
2017-10-09
The development of wireless power transfer (WPT) technology has inspired the transition from traditional battery-based wireless sensor networks (WSNs) towards wireless rechargeable sensor networks (WRSNs). While extensive efforts have been made to improve charging efficiency, little has been done for routing optimization. In this work, we present a joint optimization model to maximize both charging efficiency and routing structure. By analyzing the structure of the optimization model, we first decompose the problem and propose a heuristic algorithm to find the optimal charging efficiency for the predefined routing tree. Furthermore, by coding the many-to-one communication topology as an individual, we further propose to apply a genetic algorithm (GA) for the joint optimization of both routing and charging. The genetic operations, including tree-based recombination and mutation, are proposed to obtain a fast convergence. Our simulation results show that the heuristic algorithm reduces the number of resident locations and the total moving distance. We also show that our proposed algorithm achieves a higher charging efficiency compared with existing algorithms.
Real-time Collision Avoidance and Path Optimizer for Semi-autonomous UAVs.
Hawary, A. F.; Razak, N. A.
2018-05-01
Whilst UAV offers a potentially cheaper and more localized observation platform than current satellite or land-based approaches, it requires an advance path planner to reveal its true potential, particularly in real-time missions. Manual control by human will have limited line-of-sights and prone to errors due to careless and fatigue. A good alternative solution is to equip the UAV with semi-autonomous capabilities that able to navigate via a pre-planned route in real-time fashion. In this paper, we propose an easy-and-practical path optimizer based on the classical Travelling Salesman Problem and adopts a brute force search method to re-optimize the route in the event of collisions using range finder sensor. The former utilizes a Simple Genetic Algorithm and the latter uses Nearest Neighbour algorithm. Both algorithms are combined to optimize the route and avoid collision at once. Although many researchers proposed various path planning algorithms, we find that it is difficult to integrate on a basic UAV model and often lacks of real-time collision detection optimizer. Therefore, we explore a practical benefit from this approach using on-board Arduino and Ardupilot controllers by manually emulating the motion of an actual UAV model prior to test on the flying site. The result showed that the range finder sensor provides a real-time data to the algorithm to find a collision-free path and eventually optimized the route successfully.
Optimal Path Planner for Mobile Robot in 2D Environment
Directory of Open Access Journals (Sweden)
Valeri Kroumov
2004-06-01
Full Text Available The problem of path planning for the case of a mobile robot moving in an environment filled with obstacles with known shapes and positions is studied. A path planner based on the genetic algorithm approach, which generates optimal in length path is proposed. The population member paths are generated by another algorithm, which uses for description of the obstacles an artificial annealing neural network and is based on potential field approach. The resulting path is piecewise linear with changing directions at the corners of the obstacles. Because of this feature, the inverse kinematics problems in controlling differential drive robots are simply solved: to drive the robot to some goal pose (x, y, theta, the robot can be spun in place until it is aimed at (x, y, then driven forward until it is at (x, y, and then spun in place until the required goal orientation
A secure and lightweight ad-hoc routing algorithm for personal networks
Jehangir, A.; Heemstra de Groot, S.M.
2005-01-01
Over the past few years, there has been increasing interest in utilizing Personal Area Networks (PANs) to offer users innovative and personalized services. This interest is a consequence of the widespread use of mobile devices such as laptops, mobile phones, PDAs, digital cameras, wireless headsets,
Rajkumar, T.; Aragon, Cecilia; Bardina, Jorge; Britten, Roy
2002-01-01
A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.
Benefit of adaptive FEC in shared backup path protected elastic optical network.
Guo, Hong; Dai, Hua; Wang, Chao; Li, Yongcheng; Bose, Sanjay K; Shen, Gangxiang
2015-07-27
We apply an adaptive forward error correction (FEC) allocation strategy to an Elastic Optical Network (EON) operated with shared backup path protection (SBPP). To maximize the protected network capacity that can be carried, an Integer Linear Programing (ILP) model and a spectrum window plane (SWP)-based heuristic algorithm are developed. Simulation results show that the FEC coding overhead required by the adaptive FEC scheme is significantly lower than that needed by a fixed FEC allocation strategy resulting in higher network capacity for the adaptive strategy. The adaptive FEC allocation strategy can also significantly outperform the fixed FEC allocation strategy both in terms of the spare capacity redundancy and the average FEC coding overhead needed per optical channel. The proposed heuristic algorithm is efficient and not only performs closer to the ILP model but also does much better than the shortest-path algorithm.
Energy Efficient Routing in Nomadic Networks
DEFF Research Database (Denmark)
Kristensen, Mads Darø; Bouvin, Niels Olof
2007-01-01
We present an evaluation of a novel energy-efficient routing protocol for mobile ad-hoc networks. We combine two techniques for optimizing energy levels with a well-known routing protocol. We examine the behavior of this combination in a nomadic network setting, where some nodes are stationary...
Route Selection Problem Based on Hopfield Neural Network
Directory of Open Access Journals (Sweden)
N. Kojic
2013-12-01
Full Text Available Transport network is a key factor of economic, social and every other form of development in the region and the state itself. One of the main conditions for transport network development is the construction of new routes. Often, the construction of regional roads is dominant, since the design and construction in urban areas is quite limited. The process of analysis and planning the new roads is a complex process that depends on many factors (the physical characteristics of the terrain, the economic situation, political decisions, environmental impact, etc. and can take several months. These factors directly or indirectly affect the final solution, and in combination with project limitations and requirements, sometimes can be mutually opposed. In this paper, we present one software solution that aims to find Pareto optimal path for preliminary design of the new roadway. The proposed algorithm is based on many different factors (physical and social with the ability of their increase. This solution is implemented using Hopfield's neural network, as a kind of artificial intelligence, which has shown very good results for solving complex optimization problems.
An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
Directory of Open Access Journals (Sweden)
Guangyuan Wu
2018-01-01
Full Text Available In Body Area Networks (BANs, how to achieve energy management to extend the lifetime of the body area networks system is one of the most critical problems. In this paper, we design a body area network system powered by renewable energy, in which the sensors carried by patient with energy harvesting module can transmit data to a personal device. We do not require any a priori knowledge of the stochastic nature of energy harvesting and energy consumption. We formulate a user utility optimization problem. We use Lyapunov Optimization techniques to decompose the problem into three sub-problems, i.e., battery management, collecting rate control and transmission power allocation. We propose an online resource allocation algorithm to achieve two major goals: (1 balancing sensors’ energy harvesting and energy consumption while stabilizing the BANs system; and (2 maximizing the user utility. Performance analysis addresses required battery capacity, bounded data queue length and optimality of the proposed algorithm. Simulation results verify the optimization of algorithm.
When Dijkstra Meets Vanishing Point: A Stereo Vision Approach for Road Detection.
Zhang, Yigong; Su, Yingna; Yang, Jian; Ponce, Jean; Kong, Hui
2018-05-01
In this paper, we propose a vanishing-point constrained Dijkstra road model for road detection in a stereo-vision paradigm. First, the stereo-camera is used to generate the u- and v-disparity maps of road image, from which the horizon can be extracted. With the horizon and ground region constraints, we can robustly locate the vanishing point of road region. Second, a weighted graph is constructed using all pixels of the image, and the detected vanishing point is treated as the source node of the graph. By computing a vanishing-point constrained Dijkstra minimum-cost map, where both disparity and gradient of gray image are used to calculate cost between two neighbor pixels, the problem of detecting road borders in image is transformed into that of finding two shortest paths that originate from the vanishing point to two pixels in the last row of image. The proposed approach has been implemented and tested over 2600 grayscale images of different road scenes in the KITTI data set. The experimental results demonstrate that this training-free approach can detect horizon, vanishing point, and road regions very accurately and robustly. It can achieve promising performance.
Path planning of decentralized multi-quadrotor based on fuzzy-cell decomposition algorithm
Iswanto, Wahyunggoro, Oyas; Cahyadi, Adha Imam
2017-04-01
The paper aims to present a design algorithm for multi quadrotor lanes in order to move towards the goal quickly and avoid obstacles in an area with obstacles. There are several problems in path planning including how to get to the goal position quickly and avoid static and dynamic obstacles. To overcome the problem, therefore, the paper presents fuzzy logic algorithm and fuzzy cell decomposition algorithm. Fuzzy logic algorithm is one of the artificial intelligence algorithms which can be applied to robot path planning that is able to detect static and dynamic obstacles. Cell decomposition algorithm is an algorithm of graph theory used to make a robot path map. By using the two algorithms the robot is able to get to the goal position and avoid obstacles but it takes a considerable time because they are able to find the shortest path. Therefore, this paper describes a modification of the algorithms by adding a potential field algorithm used to provide weight values on the map applied for each quadrotor by using decentralized controlled, so that the quadrotor is able to move to the goal position quickly by finding the shortest path. The simulations conducted have shown that multi-quadrotor can avoid various obstacles and find the shortest path by using the proposed algorithms.
Eslaminejad, Mohammadreza; Razak, Shukor Abd
2012-01-01
Wireless sensor networks basically consist of low cost sensor nodes which collect data from environment and relay them to a sink, where they will be subsequently processed. Since wireless nodes are severely power-constrained, the major concern is how to conserve the nodes' energy so that network lifetime can be extended significantly. Employing one static sink can rapidly exhaust the energy of sink neighbors. Furthermore, using a non-optimal single path together with a maximum transmission power level may quickly deplete the energy of individual nodes on the route. This all results in unbalanced energy consumption through the sensor field, and hence a negative effect on the network lifetime. In this paper, we present a comprehensive taxonomy of the various mechanisms applied for increasing the network lifetime. These techniques, whether in the routing or cross-layer area, fall within the following types: multi-sink, mobile sink, multi-path, power control and bio-inspired algorithms, depending on the protocol operation. In this taxonomy, special attention has been devoted to the multi-sink, power control and bio-inspired algorithms, which have not yet received much consideration in the literature. Moreover, each class covers a variety of the state-of-the-art protocols, which should provide ideas for potential future works. Finally, we compare these mechanisms and discuss open research issues. PMID:23202008
Vector Network Coding Algorithms
Ebrahimi, Javad; Fragouli, Christina
2010-01-01
We develop new algebraic algorithms for scalar and vector network coding. In vector network coding, the source multicasts information by transmitting vectors of length L, while intermediate nodes process and combine their incoming packets by multiplying them with L x L coding matrices that play a similar role as coding c in scalar coding. Our algorithms for scalar network jointly optimize the employed field size while selecting the coding coefficients. Similarly, for vector coding, our algori...
A comparison between genetic algorithms and neural networks for optimizing fuel recharges in BWR
International Nuclear Information System (INIS)
Ortiz J, J.; Requena, I.
2002-01-01
In this work the results of a genetic algorithm (AG) and a neural recurrent multi state network (RNRME) for optimizing the fuel reload of 5 cycles of the Laguna Verde nuclear power plant (CNLV) are presented. The fuel reload obtained by both methods are compared and it was observed that the RNRME creates better fuel distributions that the AG. Moreover a comparison of the utility for using one or another one techniques is make. (Author)
Li, Jinze; Qu, Zhi; He, Xiaoyang; Jin, Xiaoming; Li, Tie; Wang, Mingkai; Han, Qiu; Gao, Ziji; Jiang, Feng
2018-02-01
Large-scale access of distributed power can improve the current environmental pressure, at the same time, increasing the complexity and uncertainty of overall distribution system. Rational planning of distributed power can effectively improve the system voltage level. To this point, the specific impact on distribution network power quality caused by the access of typical distributed power was analyzed and from the point of improving the learning factor and the inertia weight, an improved particle swarm optimization algorithm (IPSO) was proposed which could solve distributed generation planning for distribution network to improve the local and global search performance of the algorithm. Results show that the proposed method can well reduce the system network loss and improve the economic performance of system operation with distributed generation.
Multihop Wireless Networks Opportunistic Routing
Zeng, Kai; Li, Ming
2011-01-01
This book provides an introduction to opportunistic routing an emerging technology designed to improve the packet forwarding reliability, network capacity and energy efficiency of multihop wireless networks This book presents a comprehensive background to the technological challenges lying behind opportunistic routing. The authors cover many fundamental research issues for this new concept, including the basic principles, performance limit and performance improvement of opportunistic routing compared to traditional routing, energy efficiency and distributed opportunistic routing protocol desig
Zhou, Chuan; Chan, Heang-Ping; Guo, Yanhui; Wei, Jun; Chughtai, Aamer; Hadjiiski, Lubomir M.; Sundaram, Baskaran; Patel, Smita; Kuriakose, Jean W.; Kazerooni, Ella A.
2013-03-01
The curved planar reformation (CPR) method re-samples the vascular structures along the vessel centerline to generate longitudinal cross-section views. The CPR technique has been commonly used in coronary CTA workstation to facilitate radiologists' visual assessment of coronary diseases, but has not yet been used for pulmonary vessel analysis in CTPA due to the complicated tree structures and the vast network of pulmonary vasculature. In this study, a new curved planar reformation and optimal path tracing (CROP) method was developed to facilitate feature extraction and false positive (FP) reduction and improve our PE detection system. PE candidates are first identified in the segmented pulmonary vessels at prescreening. Based on Dijkstra's algorithm, the optimal path (OP) is traced from the pulmonary trunk bifurcation point to each PE candidate. The traced vessel is then straightened and a reformatted volume is generated using CPR. Eleven new features that characterize the intensity, gradient, and topology are extracted from the PE candidate in the CPR volume and combined with the previously developed 9 features to form a new feature space for FP classification. With IRB approval, CTPA of 59 PE cases were retrospectively collected from our patient files (UM set) and 69 PE cases from the PIOPED II data set with access permission. 595 and 800 PEs were manually marked by experienced radiologists as reference standard for the UM and PIOPED set, respectively. At a test sensitivity of 80%, the average FP rate was improved from 18.9 to 11.9 FPs/case with the new method for the PIOPED set when the UM set was used for training. The FP rate was improved from 22.6 to 14.2 FPs/case for the UM set when the PIOPED set was used for training. The improvement in the free response receiver operating characteristic (FROC) curves was statistically significant (p<0.05) by JAFROC analysis, indicating that the new features extracted from the CROP method are useful for FP reduction.
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......-oblivious algorithm be specified on a parallel model of computation where the only parameter is the problem’s input size, and then evaluated on a model with two parameters, capturing parallelism granularity and communication latency. It is shown that for a wide class of network-oblivious algorithms, optimality...... of cache hierarchies, to the realm of parallel computation. Its effectiveness is illustrated by providing optimal network-oblivious algorithms for a number of key problems. Some limitations of the oblivious approach are also discussed....
Policy Gradient SMDP for Resource Allocation and Routing in Integrated Services Networks
Vien, Ngo Anh; Viet, Nguyen Hoang; Lee, Seunggwan; Chung, Taechoong
In this paper, we solve the call admission control (CAC) and routing problem in an integrated network that handles several classes of calls of different values and with different resource requirements. The problem of maximizing the average reward (or cost) of admitted calls per unit time is naturally formulated as a semi-Markov Decision Process (SMDP) problem, but is too complex to allow for an exact solution. Thus in this paper, a policy gradient algorithm, together with a decomposition approach, is proposed to find the dynamic (state-dependent) optimal CAC and routing policy among a parameterized policy space. To implement that gradient algorithm, we approximate the gradient of the average reward. Then, we present a simulation-based algorithm to estimate the approximate gradient of the average reward (called GSMDP algorithm), using only a single sample path of the underlying Markov chain for the SMDP of CAC and routing problem. The algorithm enhances performance in terms of convergence speed, rejection probability, robustness to the changing arrival statistics and an overall received average revenue. The experimental simulations will compare our method's performance with other existing methods and show the robustness of our method.
Optimized smart grid energy procurement for LTE networks using evolutionary algorithms
Ghazzai, Hakim
2014-11-01
Energy efficiency aspects in cellular networks can contribute significantly to reducing worldwide greenhouse gas emissions. The base station (BS) sleeping strategy has become a well-known technique to achieve energy savings by switching off redundant BSs mainly for lightly loaded networks. Moreover, introducing renewable energy as an alternative power source has become a real challenge among network operators. In this paper, we formulate an optimization problem that aims to maximize the profit of Long-Term Evolution (LTE) cellular operators and to simultaneously minimize the CO2 emissions in green wireless cellular networks without affecting the desired quality of service (QoS). The BS sleeping strategy lends itself to an interesting implementation using several heuristic approaches, such as the genetic (GA) and particle swarm optimization (PSO) algorithms. In this paper, we propose GA-based and PSO-based methods that reduce the energy consumption of BSs by not only shutting down underutilized BSs but by optimizing the amounts of energy procured from different retailers (renewable energy and electricity retailers), as well. A comparison with another previously proposed algorithm is also carried out to evaluate the performance and the computational complexity of the employed methods.
Least loaded and route fragmentation aware RSA strategies for elastic optical networks
Batham, Deepak; Yadav, Dharmendra Singh; Prakash, Shashi
2017-12-01
Elastic optical networks (EONs) provide flexibility to assign wide range of spectral resources to the connection requests. In this manuscript, we address two issues related to spectrum assignment in EONs: the non uniform spectrum assignment along different links of the route and the spectrum fragmentation in the network. To address these issues, two routing and spectrum assignment (RSA) strategies have been proposed: Least Loaded RSA (LLRSA) and Route Fragmentation Aware RSA (RFARSA). The LLRSA allocates spectrum homogeneously along different links in the network, where as RFARSA accords priority to the routes which are less fragmented. To highlight the salient features of the two strategies, two new metrics, route fragmentation index (RFI) and standard deviation (SD) are introduced. RFI is defined as the ratio of non-contiguous FSs to the total available free FSs on the route, and SD relates to the measure of non-uniformity in the allocation of resources on the links in the network. A simulation program has been developed to evaluate the performance of the proposed (LLRSA and RFARSA) strategies, and the existing strategies of shortest path RSA (SPRSA) and spectrum compactness based defragmentation (SCD) strategies, on the metric of RFI, bandwidth blocking probability (BBP), network capacity utilized, and SD. The variation in the metrics on the basis of number of requests and the bandwidth (number of FSs) requested has been studied. It has been conclusively established that the proposed strategies (LLRSA and RFARSA) outperform the existing strategies in terms of all the metrics.
International Nuclear Information System (INIS)
Chan, Apple L.S.; Hanby, Vic I.; Chow, T.T.
2007-01-01
A district cooling system is a sustainable means of distribution of cooling energy through mass production. A cooling medium like chilled water is generated at a central refrigeration plant and supplied to serve a group of consumer buildings through a piping network. Because of the substantial capital investment involved, an optimal design of the distribution piping configuration is one of the crucial factors for successful implementation of the district cooling scheme. In the present study, genetic algorithm (GA) incorporated with local search techniques was developed to find the optimal/near optimal configuration of the piping network in a hypothetical site. The effect of local search, mutation rate and frequency of local search on the performance of the GA in terms of both solution quality and computation time were investigated and presented in this paper
Directory of Open Access Journals (Sweden)
Anish Pandey
2017-02-01
Full Text Available This article introduces a singleton type-1 fuzzy logic system (T1-SFLS controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment. The WDO (Wind Driven Optimization algorithm is used to optimize and tune the input/output membership function parameters of the fuzzy controller. The WDO algorithm is working based on the atmospheric motion of infinitesimal small air parcels navigates over an N-dimensional search domain. The performance of this proposed technique has compared through many computer simulations and real-time experiments by using Khepera-III mobile robot. As compared to the T1-SFLS controller the Fuzzy-WDO algorithm is found good agreement for mobile robot navigation.
Directory of Open Access Journals (Sweden)
Alaa Azmi Allahham
2017-02-01
Full Text Available Mobile Ad-hoc networks (MANETs involved in many applications, whether commercial or military because of their characteristics that do not depend on the infrastructure as well as the freedom movement of their elements, but in return has caused this random mobility of the nodes many of the challenges, where the routing is considered one of these challenges. There are many types of routing protocols that operate within MANET networks, which responsible for finding paths between the source and destination nodes with the modernization of these paths which are constantly changing due to the dynamic topology of the network stemming from the constant random movement of the nodes. The DSR (Dynamic Source Routing routing protocol algorithm is one of these routing protocols which consist of two main stages; route discovery and maintenance, where the route discovery algorithm operates based on blind flooding of request messages. blind flooding is considered as the most well known broadcasting mechanism, it is inefficient in terms of communication and resource utilization, which causing increasing the probability of collisions, repeating send several copies of the same message, as well as increasing the delay. Hence, a new mechanism in route discovery stage and in caching the routes in DSR algorithm according to the node's location in the network and the direction of the broadcast is proposed for better performance especially in terms of delay as well as redundant packets rate. The implementation of proposed algorithms showed positive results in terms of delay, overhead, and improve the performance of MANETs in general.
Satellite network robust QoS-aware routing
Long, Fei
2014-01-01
Satellite Network Robust QoS-aware Routing presents a novel routing strategy for satellite networks. This strategy is useful for the design of multi-layered satellite networks as it can greatly reduce the number of time slots in one system cycle. The traffic prediction and engineering approaches make the system robust so that the traffic spikes can be handled effectively. The multi-QoS optimization routing algorithm can satisfy various potential user requirements. Clear and sufficient illustrations are also presented in the book. As the chapters cover the above topics independently, readers from different research backgrounds in constellation design, multi-QoS routing, and traffic engineering can benefit from the book. Fei Long is a senior engineer at Beijing R&D Center of 54th Research Institute of China Electronics Technology Group Corporation.
Directory of Open Access Journals (Sweden)
R. Rajakumar
2017-01-01
Full Text Available Seyedali Mirjalili et al. (2014 introduced a completely unique metaheuristic technique particularly grey wolf optimization (GWO. This algorithm mimics the social behavior of grey wolves whereas it follows the leadership hierarchy and attacking strategy. The rising issue in wireless sensor network (WSN is localization problem. The objective of this problem is to search out the geographical position of unknown nodes with the help of anchor nodes in WSN. In this work, GWO algorithm is incorporated to spot the correct position of unknown nodes, so as to handle the node localization problem. The proposed work is implemented using MATLAB 8.2 whereas nodes are deployed in a random location within the desired network area. The parameters like computation time, percentage of localized node, and minimum localization error measures are utilized to analyse the potency of GWO rule with other variants of metaheuristics algorithms such as particle swarm optimization (PSO and modified bat algorithm (MBA. The observed results convey that the GWO provides promising results compared to the PSO and MBA in terms of the quick convergence rate and success rate.
Zheng, Yan
2015-03-01
Internet of things (IoT), focusing on providing users with information exchange and intelligent control, attracts a lot of attention of researchers from all over the world since the beginning of this century. IoT is consisted of large scale of sensor nodes and data processing units, and the most important features of IoT can be illustrated as energy confinement, efficient communication and high redundancy. With the sensor nodes increment, the communication efficiency and the available communication band width become bottle necks. Many research work is based on the instance which the number of joins is less. However, it is not proper to the increasing multi-join query in whole internet of things. To improve the communication efficiency between parallel units in the distributed sensor network, this paper proposed parallel query optimization algorithm based on distribution attributes cost graph. The storage information relations and the network communication cost are considered in this algorithm, and an optimized information changing rule is established. The experimental result shows that the algorithm has good performance, and it would effectively use the resource of each node in the distributed sensor network. Therefore, executive efficiency of multi-join query between different nodes could be improved.
Optimal Location and Sizing of UPQC in Distribution Networks Using Differential Evolution Algorithm
Directory of Open Access Journals (Sweden)
Seyed Abbas Taher
2012-01-01
Full Text Available Differential evolution (DE algorithm is used to determine optimal location of unified power quality conditioner (UPQC considering its size in the radial distribution systems. The problem is formulated to find the optimum location of UPQC based on an objective function (OF defined for improving of voltage and current profiles, reducing power loss and minimizing the investment costs considering the OF's weighting factors. Hence, a steady-state model of UPQC is derived to set in forward/backward sweep load flow. Studies are performed on two IEEE 33-bus and 69-bus standard distribution networks. Accuracy was evaluated by reapplying the procedures using both genetic (GA and immune algorithms (IA. Comparative results indicate that DE is capable of offering a nearer global optimal in minimizing the OF and reaching all the desired conditions than GA and IA.
A Bioinspired Adaptive Congestion-Avoidance Routing for Mobile Ad Hoc Networks
Directory of Open Access Journals (Sweden)
Huang Qiong
2014-01-01
Full Text Available Traditional mobile Ad Hoc network routing protocols are mainly based on the Shortest Path, which possibly results in many congestion nodes that incur routing instability and rerouting. To mitigate the side-efforts, this paper proposed a new bioinspired adaptive routing protocol (ATAR based on a mathematics biology model ARAS. This paper improved the ARAS by reducing the randomness and by introducing a new routing-decision metric “the next-hop fitness” which was denoted as the congestion level of node and the length of routing path. In the route maintenance, the nodes decide to forward the data to next node according to a threshold value of the fitness. In the recovery phase, the node will adopt random manner to select the neighbor as the next hop by calculation of the improved ARAS. With this route mechanism, the ATAR could adaptively circumvent the congestion nodes and the rerouting action is taken in advance. Theoretical analysis and numerical simulation results show that the ATAR protocol outperforms AODV and MARAS in terms of delivery ratio, ETE delay, and the complexity. In particular, ATAR can efficiently mitigate the congestion.
Indoor Semantic Modelling for Routing: The Two-Level Routing Approach for Indoor Navigation
Directory of Open Access Journals (Sweden)
Liu Liu
2017-11-01
for the logical network can exclude unrelated spaces and then derive geometric paths more efficiently. In this thesis, two options are proposed for routing just on the logical network, three options are proposed for routing just on the geometric networks, and seven options for two-level routing. On the logical network, six routing criteria are proposed and three human wayfinding strategies are adopted to simulate human indoor behaviours. According to a specific criterion, space semantics of logical nodes is utilized to assign different weights to logical nodes and edges. Therefore, routing on the logical network can be accomplished by applying the Dijkstra algorithm. If multiple criteria are adopted, an order of criteria is applied for routing according to a specific user. In this way, logical paths can be computed as a sequence of indoor spaces with clear semantics. On geometric networks, this thesis proposes a new routing method to provide detailed paths avoiding indoor obstacles with respect to pedestrian sizes. This method allows geometric networks to be derived for individual users with different sizes for any specified spaces. To demonstrate the use of the two types of network, this thesis tests routing on one level (the logical or the geometric network. Four case studies about the logical network are presented in both simple and complex buildings. In the simple building, no multiple paths lie between spaces A and B, but in the complex buildings, multiple logical paths exist and the candidate paths can be reduced by applying these routing criteria in an order for a user. The relationships of these criteria to user profiles are assumed in this thesis. The proposed geometric routing regarding user sizes is tested with three case studies: 1 routing for pedestrians with two distinct sizes in one space; 2 routing for pedestrians with changed sizes in one space; and 3 a larger geometric network formed by the ones in a given sequence of spaces. The first case shows
Optimization of municipal solid waste collection and transportation routes.
Das, Swapan; Bhattacharyya, Bidyut Kr
2015-09-01
Optimization of municipal solid waste (MSW) collection and transportation through source separation becomes one of the major concerns in the MSW management system design, due to the fact that the existing MSW management systems suffer by the high collection and transportation cost. Generally, in a city different waste sources scatter throughout the city in heterogeneous way that increase waste collection and transportation cost in the waste management system. Therefore, a shortest waste collection and transportation strategy can effectively reduce waste collection and transportation cost. In this paper, we propose an optimal MSW collection and transportation scheme that focus on the problem of minimizing the length of each waste collection and transportation route. We first formulize the MSW collection and transportation problem into a mixed integer program. Moreover, we propose a heuristic solution for the waste collection and transportation problem that can provide an optimal way for waste collection and transportation. Extensive simulations and real testbed results show that the proposed solution can significantly improve the MSW performance. Results show that the proposed scheme is able to reduce more than 30% of the total waste collection path length. Copyright © 2015 Elsevier Ltd. All rights reserved.
Predicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks
Directory of Open Access Journals (Sweden)
Najla S Dar-Odeh
2010-05-01
Full Text Available Najla S Dar-Odeh1, Othman M Alsmadi2, Faris Bakri3, Zaer Abu-Hammour2, Asem A Shehabi3, Mahmoud K Al-Omiri1, Shatha M K Abu-Hammad4, Hamzeh Al-Mashni4, Mohammad B Saeed4, Wael Muqbil4, Osama A Abu-Hammad1 1Faculty of Dentistry, 2Faculty of Engineering and Technology, 3Faculty of Medicine, University of Jordan, Amman, Jordan; 4Dental Department, University of Jordan Hospital, Amman, JordanObjective: To construct and optimize a neural network that is capable of predicting the occurrence of recurrent aphthous ulceration (RAU based on a set of appropriate input data.Participants and methods: Artificial neural networks (ANN software employing genetic algorithms to optimize the architecture neural networks was used. Input and output data of 86 participants (predisposing factors and status of the participants with regards to recurrent aphthous ulceration were used to construct and train the neural networks. The optimized neural networks were then tested using untrained data of a further 10 participants.Results: The optimized neural network, which produced the most accurate predictions for the presence or absence of recurrent aphthous ulceration was found to employ: gender, hematological (with or without ferritin and mycological data of the participants, frequency of tooth brushing, and consumption of vegetables and fruits.Conclusions: Factors appearing to be related to recurrent aphthous ulceration and appropriate for use as input data to construct ANNs that predict recurrent aphthous ulceration were found to include the following: gender, hemoglobin, serum vitamin B12, serum ferritin, red cell folate, salivary candidal colony count, frequency of tooth brushing, and the number of fruits or vegetables consumed daily.Keywords: artifical neural networks, recurrent, aphthous ulceration, ulcer
Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Nadeem Javaid
2017-03-01
Full Text Available In this paper, three opportunistic pressure based routing techniques for underwater wireless sensor networks (UWSNs are proposed. The first one is the cooperative opportunistic pressure based routing protocol (Co-Hydrocast, second technique is the improved Hydrocast (improved-Hydrocast, and third one is the cooperative improved Hydrocast (Co-improved Hydrocast. In order to minimize lengthy routing paths between the source and the destination and to avoid void holes at the sparse networks, sensor nodes are deployed at different strategic locations. The deployment of sensor nodes at strategic locations assure the maximum monitoring of the network field. To conserve the energy consumption and minimize the number of hops, greedy algorithm is used to transmit data packets from the source to the destination. Moreover, the opportunistic routing is also exploited to avoid void regions by making backward transmissions to find reliable path towards the destination in the network. The relay cooperation mechanism is used for reliable data packet delivery, when signal to noise ratio (SNR of the received signal is not within the predefined threshold then the maximal ratio combining (MRC is used as a diversity technique to improve the SNR of the received signals at the destination. Extensive simulations validate that our schemes perform better in terms of packet delivery ratio and energy consumption than the existing technique; Hydrocast.
Cooperative Opportunistic Pressure Based Routing for Underwater Wireless Sensor Networks.
Javaid, Nadeem; Muhammad; Sher, Arshad; Abdul, Wadood; Niaz, Iftikhar Azim; Almogren, Ahmad; Alamri, Atif
2017-03-19
In this paper, three opportunistic pressure based routing techniques for underwater wireless sensor networks (UWSNs) are proposed. The first one is the cooperative opportunistic pressure based routing protocol (Co-Hydrocast), second technique is the improved Hydrocast (improved-Hydrocast), and third one is the cooperative improved Hydrocast (Co-improved Hydrocast). In order to minimize lengthy routing paths between the source and the destination and to avoid void holes at the sparse networks, sensor nodes are deployed at different strategic locations. The deployment of sensor nodes at strategic locations assure the maximum monitoring of the network field. To conserve the energy consumption and minimize the number of hops, greedy algorithm is used to transmit data packets from the source to the destination. Moreover, the opportunistic routing is also exploited to avoid void regions by making backward transmissions to find reliable path towards the destination in the network. The relay cooperation mechanism is used for reliable data packet delivery, when signal to noise ratio (SNR) of the received signal is not within the predefined threshold then the maximal ratio combining (MRC) is used as a diversity technique to improve the SNR of the received signals at the destination. Extensive simulations validate that our schemes perform better in terms of packet delivery ratio and energy consumption than the existing technique; Hydrocast.
Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)
2002-01-01
Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic
Reconfigurable Robust Routing for Mobile Outreach Network
Lin, Ching-Fang
2010-01-01
The Reconfigurable Robust Routing for Mobile Outreach Network (R3MOO N) provides advanced communications networking technologies suitable for the lunar surface environment and applications. The R3MOON techn ology is based on a detailed concept of operations tailored for luna r surface networks, and includes intelligent routing algorithms and wireless mesh network implementation on AGNC's Coremicro Robots. The product's features include an integrated communication solution inco rporating energy efficiency and disruption-tolerance in a mobile ad h oc network, and a real-time control module to provide researchers an d engineers a convenient tool for reconfiguration, investigation, an d management.
Sriram, Vinay K; Montgomery, Doug
2017-07-01
The Internet is subject to attacks due to vulnerabilities in its routing protocols. One proposed approach to attain greater security is to cryptographically protect network reachability announcements exchanged between Border Gateway Protocol (BGP) routers. This study proposes and evaluates the performance and efficiency of various optimization algorithms for validation of digitally signed BGP updates. In particular, this investigation focuses on the BGPSEC (BGP with SECurity extensions) protocol, currently under consideration for standardization in the Internet Engineering Task Force. We analyze three basic BGPSEC update processing algorithms: Unoptimized, Cache Common Segments (CCS) optimization, and Best Path Only (BPO) optimization. We further propose and study cache management schemes to be used in conjunction with the CCS and BPO algorithms. The performance metrics used in the analyses are: (1) routing table convergence time after BGPSEC peering reset or router reboot events and (2) peak-second signature verification workload. Both analytical modeling and detailed trace-driven simulation were performed. Results show that the BPO algorithm is 330% to 628% faster than the unoptimized algorithm for routing table convergence in a typical Internet core-facing provider edge router.
Sum of All-Pairs Shortest Path Distances in a Planar Graph in Subquadratic Time
DEFF Research Database (Denmark)
Wulff-Nilsen, Christian
2008-01-01
We consider the problem of computing the Wiener index of a graph, defined as the sum of distances between all pairs of its vertices. It is an open problem whether the Wiener index of a planar graph can be found in subquadratic time. We solve this problem by presenting an algorithm with O(n^2*log...
DRUG: An Energy-Efficient Data-Centric Routing Protocol for Wireless Sensor Networks
Sahoo, B. P. S.; Puthal, Deepak
2014-01-01
In general, sensor nodes are deployed in left unattended area. In such situation feeding energy to the batteries or replacing the batteries is difficult or even sometimes impossible too. Therefore, prolonging the network lifetime is an important optimization goal in this aspect. In this paper, we propose a new Energy-efficient Datacentric RoUtinG protocol called DRUG. In this paper, we propose an adaptive Data centric approach to find an optimal routing path from source to sink when the senso...
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.
International Nuclear Information System (INIS)
Wang Jun; Liu Mingzhe; Li Zhe; Li Lei; Shi Rui; Tuo Xianguo
2015-01-01
The quantitative elemental content analysis is difficult due to the uniform effect, particle effect and the element matrix effect, etc, when using energy dispersive X-ray fluorescence (EDXRF) technique. In this paper, a hybrid approach of genetic algorithm (GA) and back propagation (BP) neural network was proposed without considering the complex relationship between the concentration and intensity. The aim of GA optimized BP was to get better network initial weights and thresholds. The basic idea was that the reciprocal of the mean square error of the initialization BP neural network was set as the fitness value of the individual in GA, and the initial weights and thresholds were replaced by individuals, and then the optimal individual was sought by selection, crossover and mutation operations, finally a new BP neural network model was created with the optimal initial weights and thresholds. The calculation results of quantitative analysis of titanium and iron contents for five types of ore bodies in Panzhihua Mine show that the results of classification prediction are far better than that of overall forecasting, and relative errors of 76.7% samples are less than 2% compared with chemical analysis values, which demonstrates the effectiveness of the proposed method. (authors)
Scripted Mobile Network Routing in a Contested Environment
National Research Council Canada - National Science Library
Otto, Anthony R
2008-01-01
Mobile wireless network protocols currently run on optimistic routing algorithms, adjusting node connectivity only when the chosen connectivity metrics, such as signal strength, pass beyond minimum thresholds...
Efficient distribution of toy products using ant colony optimization algorithm
Hidayat, S.; Nurpraja, C. A.
2017-12-01
CV Atham Toys (CVAT) produces wooden toys and furniture, comprises 13 small and medium industries. CVAT always attempt to deliver customer orders on time but delivery costs are high. This is because of inadequate infrastructure such that delivery routes are long, car maintenance costs are high, while fuel subsidy by the government is still temporary. This study seeks to minimize the cost of product distribution based on the shortest route using one of five Ant Colony Optimization (ACO) algorithms to solve the Vehicle Routing Problem (VRP). This study concludes that the best of the five is the Ant Colony System (ACS) algorithm. The best route in 1st week gave a total distance of 124.11 km at a cost of Rp 66,703.75. The 2nd week route gave a total distance of 132.27 km at a cost of Rp 71,095.13. The 3rd week best route gave a total distance of 122.70 km with a cost of Rp 65,951.25. While the 4th week gave a total distance of 132.27 km at a cost of Rp 74,083.63. Prior to this study there was no effort to calculate these figures.
A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning.
Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen
2012-01-01
Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.
Adaptive autonomous Communications Routing Optimizer for Network Efficiency Management, Phase I
National Aeronautics and Space Administration — Maximizing network efficiency for NASA's Space Networking resources is a large, complex, distributed problem, requiring substantial collaboration. We propose the...
A polynomial time algorithm for solving the maximum flow problem in directed networks
International Nuclear Information System (INIS)
Tlas, M.
2015-01-01
An efficient polynomial time algorithm for solving maximum flow problems has been proposed in this paper. The algorithm is basically based on the binary representation of capacities; it solves the maximum flow problem as a sequence of O(m) shortest path problems on residual networks with nodes and m arcs. It runs in O(m"2r) time, where is the smallest integer greater than or equal to log B , and B is the largest arc capacity of the network. A numerical example has been illustrated using this proposed algorithm.(author)
Routing Optimization of Intelligent Vehicle in Automated Warehouse
Directory of Open Access Journals (Sweden)
Yan-cong Zhou
2014-01-01
Full Text Available Routing optimization is a key technology in the intelligent warehouse logistics. In order to get an optimal route for warehouse intelligent vehicle, routing optimization in complex global dynamic environment is studied. A new evolutionary ant colony algorithm based on RFID and knowledge-refinement is proposed. The new algorithm gets environmental information timely through the RFID technology and updates the environment map at the same time. It adopts elite ant kept, fallback, and pheromones limitation adjustment strategy. The current optimal route in population space is optimized based on experiential knowledge. The experimental results show that the new algorithm has higher convergence speed and can jump out the U-type or V-type obstacle traps easily. It can also find the global optimal route or approximate optimal one with higher probability in the complex dynamic environment. The new algorithm is proved feasible and effective by simulation results.
DEFF Research Database (Denmark)
Mozaffari, Ahmad; Gorji-Bandpy, Mofid; Samadian, Pendar
2013-01-01
Optimizing and controlling of complex engineering systems is a phenomenon that has attracted an incremental interest of numerous scientists. Until now, a variety of intelligent optimizing and controlling techniques such as neural networks, fuzzy logic, game theory, support vector machines...... and stochastic algorithms were proposed to facilitate controlling of the engineering systems. In this study, an extended version of mutable smart bee algorithm (MSBA) called Pareto based mutable smart bee (PBMSB) is inspired to cope with multi-objective problems. Besides, a set of benchmark problems and four...... well-known Pareto based optimizing algorithms i.e. multi-objective bee algorithm (MOBA), multi-objective particle swarm optimization (MOPSO) algorithm, non-dominated sorting genetic algorithm (NSGA-II), and strength Pareto evolutionary algorithm (SPEA 2) are utilized to confirm the acceptable...
Multipath diffuse routing over heterogeneous mesh networks of web devices and sensors
Vitale, G.; Stassen, M.L.A.; Colak, S.B.; Pronk, V.; Macq, B.; Quisquater, J.-.J.
2002-01-01
In this paper we present a new data flow algorithm based on physical diffusion for dense device networks needed for future ubiquitous communications. This diffuse data routing concept is based on multi-path signal propagation aided with adaptive beam-forming methods. Adaptation for beam-forming at
Multiobjective optimization of building design using genetic algorithm and artificial neural network
Energy Technology Data Exchange (ETDEWEB)
Magnier, L.; Zhou, L.; Haghighat, F. [Concordia Univ., Centre for Building Studies, Montreal, PQ (Canada). Dept. of Building, Civil and Environmental Engineering
2008-07-01
This paper addressed the challenge of designing modern buildings that are energy efficient, affordable, environmentally sound and comfortable for occupants. Building optimization is a time consuming process when so many objectives must be met. In particular, the use of genetic algorithm (GA) for building design has limitations due to the high number of simulations required. This paper presented an efficient approach to overcome the limitations of GA for building design. The approach expanded the GA methodology to multiobjective optimization. The GA integrating neural network (GAINN) approach first uses a simulation-based artificial neural network (ANN) to characterize building behaviour, and then combines it with a GA for optimization. The process was shown to provide fast and reliable optimization. GAINN was further improved by integrating multiobjective evolutionary algorithms (MOEAs). Two new MOEAs named NSGAINN and PLAGUE were designed for the proposed methodology. The purpose of creating a new MOEA was to take advantage of GAINN fast evaluations. This paper presented bench test results and compared them with with NSGA-2. A previous case study using GAINN methodology was re-optimized with the newly developed MOEA. The design to be optimized was a ventilation system of a standard office room in the summer, with 2 occupants and 4 underfloor air distribution diffusers. The objectives included thermal comfort, indoor air quality, and energy conservation for cooling. The control variables were temperature of the air supply, speed of air supply, distance from the diffuser to the occupant, and the distance from the return grill to the contaminant source. The results showed that the newly presented GAINN methodology was better in both convergence and range of choices compared to a weighted sum GA. 13 refs., 2 tabs., 9 figs.
Directory of Open Access Journals (Sweden)
M. I. Fursanov
2014-01-01
Full Text Available This article reflects algorithmization of search methods of effective replacement of consumer transformers in distributed electrical networks. As any electrical equipment of power systems, power transformers have their own limited service duration, which is determined by natural processes of materials degradation and also by unexpected wear under different conditions of overload and overvoltage. According to the standards, adapted by in the Republic of Belarus, rated service life of power transformers is 25 years. But it can be situations that transformers should be better changed till this time – economically efficient. The possibility of such replacement is considered in order to increase efficiency of electrical network operation connected with its physical wear and aging.In this article the faults of early developed mathematical models of transformers replacement were discussed. Early such worked out transformers were not used. But in practice they can be replaced in one substation but they can be successfully used in other substations .Especially if there are limits of financial resources and the replacement needs more detail technical and economical basis.During the research the authors developed the efficient algorithm for determining of optimal location of transformers at substations of distributed electrical networks, based on search of the best solution from all sets of displacement in oriented graph. Suggested algorithm allows considerably reduce design time of optimal placement of transformers using a set of simplifications. The result of algorithm’s work is series displacement of transformers in networks, which allow obtain a great economic effect in comparison with replacement of single transformer.
Directory of Open Access Journals (Sweden)
Rong Chai
2017-01-01
Full Text Available In recent years, heterogeneous radio access technologies have experienced rapid development and gradually achieved effective coordination and integration, resulting in heterogeneous networks (HetNets. In this paper, we consider the downlink secure transmission of HetNets where the information transmission from base stations (BSs to legitimate users is subject to the interception of eavesdroppers. In particular, we stress the problem of joint user association and power allocation of the BSs. To achieve data transmission in a secure and energy efficient manner, we introduce the concept of secrecy energy efficiency which is defined as the ratio of the secrecy transmission rate and power consumption of the BSs and formulate the problem of joint user association and power allocation as an optimization problem which maximizes the joint secrecy energy efficiency of all the BSs under the power constraint of the BSs and the minimum data rate constraint of user equipment (UE. By equivalently transforming the optimization problem into two subproblems, that is, power allocation subproblem and user association subproblem of the BSs, and applying iterative method and Kuhn-Munkres (K-M algorithm to solve the two subproblems, respectively, the optimal user association and power allocation strategies can be obtained. Numerical results demonstrate that the proposed algorithm outperforms previously proposed algorithms.
APPLYING ARTIFICIAL NEURAL NETWORK OPTIMIZED BY FIREWORKS ALGORITHM FOR STOCK PRICE ESTIMATION
Directory of Open Access Journals (Sweden)
Khuat Thanh Tung
2016-04-01
Full Text Available Stock prediction is to determine the future value of a company stock dealt on an exchange. It plays a crucial role to raise the profit gained by firms and investors. Over the past few years, many methods have been developed in which plenty of efforts focus on the machine learning framework achieving the promising results. In this paper, an approach based on Artificial Neural Network (ANN optimized by Fireworks algorithm and data preprocessing by Haar Wavelet is applied to estimate the stock prices. The system was trained and tested with real data of various companies collected from Yahoo Finance. The obtained results are encouraging.
Approximation Algorithm for a Heterogeneous Vehicle Routing Problem
Directory of Open Access Journals (Sweden)
Jungyun Bae
2015-08-01
Full Text Available This article addresses a fundamental path planning problem which aims to route a collection of heterogeneous vehicles such that each target location is visited by some vehicle and the sum of the travel costs of the vehicles is minimal. Vehicles are heterogeneous as the cost of traveling between any two locations depends on the type of the vehicle. Algorithms are developed for this path planning problem with bounds on the quality of the solutions produced by the algorithms. Computational results show that high quality solutions can be obtained for the path planning problem involving four vehicles and 40 targets using the proposed approach.
Directory of Open Access Journals (Sweden)
R. Ravi
2011-03-01
Full Text Available As Internet usage grows exponentially, network security issues become increasingly important. Network security measures are needed to protect data during transmission. Various security controls are used to prevent the access of hackers in networks. They are firewall, virtual private networks and encryption algorithms. Out of these, the virtual private network plays a vital role in preventing hackers from accessing the networks. A Virtual Private Network (VPN provides end users with a way to privately access information on their network over a public network infrastructure such as the internet. Using a technique called “Tunneling”, data packets are transmitted across a public routed network, such as the internet that simulates a point-to-point connection. Virtual private networks provide customers with a secure and low-cost communication environment. The basic structure of the virtual circuit is to create a logical path from the source port to the destination port. This path may incorporate many hops between routers for the formation of the circuit. The final, logical path or virtual circuit acts in the same way as a direct connection between the two ports. The K-Cost Optimized Delay Satisfied Virtual Private Networks Tree Provisioning Algorithm connects VPN nodes using a tree structure and attempts to optimize the total bandwidth reserved on the edges of the VPN tree that satisfies the delay requirement. It also allows sharing the bandwidth on the links to improve the performance. The proposed KCDVT algorithm computes the optimal VPN Tree. The performance analysis of the proposed algorithm is carried out in terms of cost, the number of nodes, and the number of VPN nodes, delay, asymmetric ratio and delay with constraints with Breadth First Search Algorithm. The KCDVT performs better than the Breadth First Search Algorithm.
Joint optimization of green vehicle scheduling and routing problem with time-varying speeds
Zhang, Dezhi; Wang, Xin; Ni, Nan; Zhang, Zhuo
2018-01-01
Based on an analysis of the congestion effect and changes in the speed of vehicle flow during morning and evening peaks in a large- or medium-sized city, the piecewise function is used to capture the rules of the time-varying speed of vehicles, which are very important in modelling their fuel consumption and CO2 emission. A joint optimization model of the green vehicle scheduling and routing problem with time-varying speeds is presented in this study. Extra wages during nonworking periods and soft time-window constraints are considered. A heuristic algorithm based on the adaptive large neighborhood search algorithm is also presented. Finally, a numerical simulation example is provided to illustrate the optimization model and its algorithm. Results show that, (1) the shortest route is not necessarily the route that consumes the least energy, (2) the departure time influences the vehicle fuel consumption and CO2 emissions and the optimal departure time saves on fuel consumption and reduces CO2 emissions by up to 5.4%, and (3) extra driver wages have significant effects on routing and departure time slot decisions. PMID:29466370
2013-03-05
... Business Networks Services, Inc., Senior Analysts-Order Management, Voice Over Internet Protocol, Small And... Management, Voice Over Internet Protocol, Small And Medium Business, San Antonio, TX; Amended Certification... Business Networks Services, Inc., Senior Analysts-Order Management, Voice Over Internet Protocol, Small and...
Effects of node buffer and capacity on network traffic
International Nuclear Information System (INIS)
Ling Xiang; Ding Jian-Xun; Hu Mao-Bin
2012-01-01
In this paper, we study the optimization of network traffic by considering the effects of node buffer ability and capacity. Two node buffer settings are considered. The node capacity is considered to be proportional to its buffer ability. The node effects on network traffic systems are studied with the shortest path protocol and an extension of the optimal routing [Phys. Rev. E 74 046106 (2006)]. In the diagrams of flux—density relationships, it is shown that a nodes buffer ability and capacity have profound effects on the network traffic
Zamora Ramos, Ernesto
Artificial Intelligence is a big part of automation and with today's technological advances, artificial intelligence has taken great strides towards positioning itself as the technology of the future to control, enhance and perfect automation. Computer vision includes pattern recognition and classification and machine learning. Computer vision is at the core of decision making and it is a vast and fruitful branch of artificial intelligence. In this work, we expose novel algorithms and techniques built upon existing technologies to improve pattern recognition and neural network training, initially motivated by a multidisciplinary effort to build a robot that helps maintain and optimize solar panel energy production. Our contributions detail an improved non-linear pre-processing technique to enhance poorly illuminated images based on modifications to the standard histogram equalization for an image. While the original motivation was to improve nocturnal navigation, the results have applications in surveillance, search and rescue, medical imaging enhancing, and many others. We created a vision system for precise camera distance positioning motivated to correctly locate the robot for capture of solar panel images for classification. The classification algorithm marks solar panels as clean or dirty for later processing. Our algorithm extends past image classification and, based on historical and experimental data, it identifies the optimal moment in which to perform maintenance on marked solar panels as to minimize the energy and profit loss. In order to improve upon the classification algorithm, we delved into feedforward neural networks because of their recent advancements, proven universal approximation and classification capabilities, and excellent recognition rates. We explore state-of-the-art neural network training techniques offering pointers and insights, culminating on the implementation of a complete library with support for modern deep learning architectures
Coordinated Platoon Routing in a Metropolitan Network
Energy Technology Data Exchange (ETDEWEB)
Larson, Jeffrey; Munson, Todd; Sokolov, Vadim
2016-10-10
Platooning vehicles—connected and automated vehicles traveling with small intervehicle distances—use less fuel because of reduced aerodynamic drag. Given a network de- fined by vertex and edge sets and a set of vehicles with origin/destination nodes/times, we model and solve the combinatorial optimization problem of coordinated routing of vehicles in a manner that routes them to their destination on time while using the least amount of fuel. Common approaches decompose the platoon coordination and vehicle routing into separate problems. Our model addresses both problems simultaneously to obtain the best solution. We use modern modeling techniques and constraints implied from analyzing the platoon routing problem to address larger numbers of vehicles and larger networks than previously considered. While the numerical method used is unable to certify optimality for candidate solutions to all networks and parameters considered, we obtain excellent solutions in approximately one minute for much larger networks and vehicle sets than previously considered in the literature.
Directory of Open Access Journals (Sweden)
Jing Li
2017-01-01
Full Text Available The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS model and improved particle swarm optimization (PSO algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.
Genetic algorithms used to optimize an artificial neural network design used in neutron spectrometry
International Nuclear Information System (INIS)
Arteaga A, T.; Ortiz R, J. M.; Vega C, H. R.
2016-10-01
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
Routing in opportunistic networks
Dhurandher, Sanjay; Anpalagan, Alagan; Vasilakos, Athanasios
2013-01-01
This book provides a comprehensive guide to selected topics, both ongoing and emerging, in routing in OppNets. The book is edited by worldwide technical leaders, prolific researchers and outstanding academics, Dr. Isaac Woungang and co-editors, Dr. Sanjay Kumar Dhurandher, Prof. Alagan Anpalagan and Prof. Athanasios Vasilakos. Consisting of contributions from well known and high profile researchers and scientists in their respective specialties, the main topics that are covered in this book include mobility and routing, social-aware routing, context-based routing, energy-aware routing, incentive-aware routing, stochastic routing, modeling of intermittent connectivity, in both infrastructure and infrastructure-less OppNets. Key Features: Discusses existing and emerging techniques for routing in infrastructure and infrastructure-less OppNets. Provides a unified covering of otherwise disperse selected topics on routing in infrastructure and infrastructure-less OppNets. Includes a set of PowerPoint slides and g...
Heuristic algorithms for solving of the tool routing problem for CNC cutting machines
Chentsov, P. A.; Petunin, A. A.; Sesekin, A. N.; Shipacheva, E. N.; Sholohov, A. E.
2015-11-01
The article is devoted to the problem of minimizing the path of the cutting tool to shape cutting machines began. This problem can be interpreted as a generalized traveling salesman problem. Earlier version of the dynamic programming method to solve this problem was developed. Unfortunately, this method allows to process an amount not exceeding thirty circuits. In this regard, the task of constructing quasi-optimal route becomes relevant. In this paper we propose options for quasi-optimal greedy algorithms. Comparison of the results of exact and approximate algorithms is given.
Directory of Open Access Journals (Sweden)
Rasoul Rajabpour
2017-01-01
Full Text Available Recent decades have witnessed growing applications of metaheuristic techniques as efficient tools for solving complex engineering problems. One such method is the JPSO algorithm. In this study, innovative modifications were made in the nature of the jump algorithm JPSO to make it capable of coping with graph-based solutions, which led to the development of a new algorithm called ‘G-JPSO’. The new algorithm was then used to solve the Fletcher-Powell optimal control problem and its application to optimal control of pumps in water distribution networks was evaluated. Optimal control of pumps consists in an optimum operation timetable (on and off for each of the pumps at the desired time interval. Maximum number of on and off positions for each pump was introduced into the objective function as a constraint such that not only would power consumption at each node be reduced but such problem requirements as the minimum pressure required at each node and minimum/maximum storage tank heights would be met. To determine the optimal operation of pumps, a model-based optimization-simulation algorithm was developed based on G-JPSO and JPSO algorithms. The model proposed by van Zyl was used to determine the optimal operation of the distribution network. Finally, the results obtained from the proposed algorithm were compared with those obtained from ant colony, genetic, and JPSO algorithms to show the robustness of the proposed algorithm in finding near-optimum solutions at reasonable computation costs.
Optimal sensor placement for leak location in water distribution networks using genetic algorithms.
Casillas, Myrna V; Puig, Vicenç; Garza-Castañón, Luis E; Rosich, Albert
2013-11-04
This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach.
Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms
Directory of Open Access Journals (Sweden)
Luis E. Garza-Castañón
2013-11-01
Full Text Available This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs. The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach.
Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms
Casillas, Myrna V.; Puig, Vicenç; Garza-Castañón, Luis E.; Rosich, Albert
2013-01-01
This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach. PMID:24193099
Use of graph algorithms in the processing and analysis of images with focus on the biomedical data.
Zdimalova, M; Roznovjak, R; Weismann, P; El Falougy, H; Kubikova, E
2017-01-01
Image segmentation is a known problem in the field of image processing. A great number of methods based on different approaches to this issue was created. One of these approaches utilizes the findings of the graph theory. Our work focuses on segmentation using shortest paths in a graph. Specifically, we deal with methods of "Intelligent Scissors," which use Dijkstra's algorithm to find the shortest paths. We created a new software in Microsoft Visual Studio 2013 integrated development environment Visual C++ in the language C++/CLI. We created a format application with a graphical users development environment for system Windows, with using the platform .Net (version 4.5). The program was used for handling and processing the original medical data. The major disadvantage of the method of "Intelligent Scissors" is the computational time length of Dijkstra's algorithm. However, after the implementation of a more efficient priority queue, this problem could be alleviated. The main advantage of this method we see in training that enables to adapt to a particular kind of edge, which we need to segment. The user involvement has a significant influence on the process of segmentation, which enormously aids to achieve high-quality results (Fig. 7, Ref. 13).
Directory of Open Access Journals (Sweden)
R Goudarzi
2018-03-01
Full Text Available Introduction The demand of pre-determined optimal coverage paths in agricultural environments have been increased due to the growing application of field robots and autonomous field machines. Also coverage path planning problem (CPP has been extensively studied in robotics and many algorithms have been provided in many topics, but differences and limitations in agriculture lead to several different heuristic and modified adaptive methods from robotics. In this paper, a modified and enhanced version of currently used decomposition algorithm in robotics (boustrophedon cellular decomposition has been presented as a main part of path planning systems of agricultural vehicles. Developed algorithm is based on the parallelization of the edges of the polygon representing the environment to satisfy the requirements of the problem as far as possible. This idea is based on "minimum facing to the cost making condition" in turn, it is derived from encounter concept as a basis of cost making factors. Materials and Methods Generally, a line termed as a slice in boustrophedon cellular decomposition (BCD, sweeps an area in a pre-determined direction and decomposes the area only at critical points (where two segments can be extended to top and bottom of the point. Furthermore, sweep line direction does not change until the decomposition finish. To implement the BCD for parallelization method, two modifications were applied in order to provide a modified version of the boustrophedon cellular decomposition (M-BCD. In the first modification, the longest edge (base edge is targeted, and sweep line direction is set in line with the base edge direction (sweep direction is set perpendicular to the sweep line direction. Then Sweep line moves through the environment and stops at the first (nearest critical point. Next sweep direction will be the same as previous, If the length of those polygon's newly added edges, during the decomposition, are less than or equal to the
Optimal groundwater remediation using artificial neural networks and the genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Rogers, Leah L. [Stanford Univ., CA (United States)
1992-08-01
An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ``recycle`` or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models.
Optimal groundwater remediation using artificial neural networks and the genetic algorithm
International Nuclear Information System (INIS)
Rogers, L.L.
1992-08-01
An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ''recycle'' or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models
Poulter, Benjamin; Goodall, Jonathan L.; Halpin, Patrick N.
2008-08-01
SummaryThe vulnerability of coastal landscapes to sea level rise is compounded by the existence of extensive artificial drainage networks initially built to lower water tables for agriculture, forestry, and human settlements. These drainage networks are found in landscapes with little topographic relief where channel flow is characterized by bi-directional movement across multiple time-scales and related to precipitation, wind, and tidal patterns. The current configuration of many artificial drainage networks exacerbates impacts associated with sea level rise such as salt-intrusion and increased flooding. This suggests that in the short-term, drainage networks might be managed to mitigate sea level rise related impacts. The challenge, however, is that hydrologic processes in regions where channel flow direction is weakly related to slope and topography require extensive parameterization for numerical models which is limited where network size is on the order of a hundred or more kilometers in total length. Here we present an application of graph theoretic algorithms to efficiently investigate network properties relevant to the management of a large artificial drainage system in coastal North Carolina, USA. We created a digital network model representing the observation network topology and four types of drainage features (canal, collector and field ditches, and streams). We applied betweenness-centrality concepts (using Dijkstra's shortest path algorithm) to determine major hydrologic flowpaths based off of hydraulic resistance. Following this, we identified sub-networks that could be managed independently using a community structure and modularity approach. Lastly, a betweenness-centrality algorithm was applied to identify major shoreline entry points to the network that disproportionately control water movement in and out of the network. We demonstrate that graph theory can be applied to solving management and monitoring problems associated with sea level rise
Feature Selection of Network Intrusion Data using Genetic Algorithm and Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Iwan Syarif
2016-12-01
Full Text Available This paper describes the advantages of using Evolutionary Algorithms (EA for feature selection on network intrusion dataset. Most current Network Intrusion Detection Systems (NIDS are unable to detect intrusions in real time because of high dimensional data produced during daily operation. Extracting knowledge from huge data such as intrusion data requires new approach. The more complex the datasets, the higher computation time and the harder they are to be interpreted and analyzed. This paper investigates the performance of feature selection algoritms in network intrusiona data. We used Genetic Algorithms (GA and Particle Swarm Optimizations (PSO as feature selection algorithms. When applied to network intrusion datasets, both GA and PSO have significantly reduces the number of features. Our experiments show that GA successfully reduces the number of attributes from 41 to 15 while PSO reduces the number of attributes from 41 to 9. Using k Nearest Neighbour (k-NN as a classifier,the GA-reduced dataset which consists of 37% of original attributes, has accuracy improvement from 99.28% to 99.70% and its execution time is also 4.8 faster than the execution time of original dataset. Using the same classifier, PSO-reduced dataset which consists of 22% of original attributes, has the fastest execution time (7.2 times faster than the execution time of original datasets. However, its accuracy is slightly reduced 0.02% from 99.28% to 99.26%. Overall, both GA and PSO are good solution as feature selection techniques because theyhave shown very good performance in reducing the number of features significantly while still maintaining and sometimes improving the classification accuracy as well as reducing the computation time.
Pan, Indranil; Das, Saptarshi; Gupta, Amitava
2011-01-01
An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS). The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz. the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared. The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Wang Jiangfeng; Sun Zhixin; Dai Yiping; Ma Shaolin
2010-01-01
Supercritical CO 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 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 2 power cycle and exergy destruction in each component. It is also shown that the optimum thermodynamic parameters of supercritical CO 2 power cycle can be predicted with good accuracy using artificial neural network under variable waste heat conditions.
Optimal Paths in Gliding Flight
Wolek, Artur
Underwater gliders are robust and long endurance ocean sampling platforms that are increasingly being deployed in coastal regions. This new environment is characterized by shallow waters and significant currents that can challenge the mobility of these efficient (but traditionally slow moving) vehicles. This dissertation aims to improve the performance of shallow water underwater gliders through path planning. The path planning problem is formulated for a dynamic particle (or "kinematic car") model. The objective is to identify the path which satisfies specified boundary conditions and minimizes a particular cost. Several cost functions are considered. The problem is addressed using optimal control theory. The length scales of interest for path planning are within a few turn radii. First, an approach is developed for planning minimum-time paths, for a fixed speed glider, that are sub-optimal but are guaranteed to be feasible in the presence of unknown time-varying currents. Next the minimum-time problem for a glider with speed controls, that may vary between the stall speed and the maximum speed, is solved. Last, optimal paths that minimize change in depth (equivalently, maximize range) are investigated. Recognizing that path planning alone cannot overcome all of the challenges associated with significant currents and shallow waters, the design of a novel underwater glider with improved capabilities is explored. A glider with a pneumatic buoyancy engine (allowing large, rapid buoyancy changes) and a cylindrical moving mass mechanism (generating large pitch and roll moments) is designed, manufactured, and tested to demonstrate potential improvements in speed and maneuverability.
MultiPaths Revisited - A novel approach using OpenFlow-enabled devices
Al-Shabibi, Ali; Martin, Brian
2011-06-11
This thesis presents novel approaches enhancing the performance of computer networks using multipaths. Our enhancements take the form of conges