Finding Shortest Paths Research Articles

Reliable lifelong planning A*: Technique for re-optimizing reliable shortest paths when travel time distribution updating

Re-optimization technique is an efficient approach for solving the shortest path problem in dynamic deterministic networks, where link travel times are updated in real-time. However, existing re-optimization techniques, built on the assumption that link travel times are deterministic, cannot be used to solve reliable shortest path problems in real road networks with noticeable levels of travel time uncertainties. This study proposes a novel re-optimization technique, named reliable lifelong planning A* (RLPA*), for re-optimizing reliable shortest path finding results in dynamic stochastic networks, where link travel time distributions are updated in real-time. The proposed RLPA* technique can efficiently determine the optimal solution in dynamic stochastic networks by reusing path search results produced in the previous time instance. The proposed RLPA* technique is further utilized to solve the K reliable shortest paths problem, which is regarded as a series of reliable shortest path searches in a dynamic stochastic network. To validate the proposed algorithms, a comprehensive case study using real traffic data is conducted. The case study results demonstrated that the proposed algorithms significantly outperform the corresponding state-of-the-art algorithms on all testing networks.

Generally Applicable Q-Table Compression Method and Its Application for Constrained Stochastic Graph Traversal Optimization Problems

We analyzed a special class of graph traversal problems, where the distances are stochastic, and the agent is restricted to take a limited range in one go. We showed that both constrained shortest Hamiltonian pathfinding problems and disassembly line balancing problems belong to the class of constrained shortest pathfinding problems, which can be represented as mixed-integer optimization problems. Reinforcement learning (RL) methods have proven their efficiency in multiple complex problems. However, researchers concluded that the learning time increases radically by growing the state- and action spaces. In continuous cases, approximation techniques are used, but these methods have several limitations in mixed-integer searching spaces. We present the Q-table compression method as a multistep method with dimension reduction, state fusion, and space compression techniques that project a mixed-integer optimization problem into a discrete one. The RL agent is then trained using an extended Q-value-based method to deliver a human-interpretable model for optimal action selection. Our approach was tested in selected constrained stochastic graph traversal use cases, and comparative results are shown to the simple grid-based discretization method.

On Finding Shortest Path Over Vocational High School in Yogyakarta Based on Graph Theory Algorithm

Finding shortest path is one of problem in graph theory. This research aims to apply the Dijkstra algorithm in determining the shortest route for State Vocational School (SMK) students throughout Yogyakarta. State Vocational Schools throughout Yogyakarta have several locations spread across various regions, and determining the shortest path is crucial for the efficiency of students' time and transportation. Dijkstra's algorithm was chosen because of its ability to find the shortest path in a weighted graph, which can be applied to complex networks of school trajectories. The research method involved collecting geographic data on schools, available transportation, and distance between locations. This data is used to build a weighted graph that represents the transportation network between State Vocational Schools throughout Yogyakarta. The implementation of Dijkstra's algorithm is then carried out using python programming language. We hope that the research results can provide mapping of the shortest routes between schools, minimize student travel time, and increase transportation efficiency. The practical implications of this research include the development of an information system or application that can help students, teachers and schools in planning daily trips. Apart from that, the research results can also be a basis for developing methods for determining the shortest route in the context of other schools or similar environments.

Trust based multi objective honey badger algorithm to secure routing in vehicular ad-hoc networks

<span lang="EN-US">A vehicular ad-hoc network (VANET) is a set of intelligent vehicles that interact without any fixed infrastructure. Data transmission between each transmitter/receiver pair is accomplished using routing protocols. However, communication over the VANET is vulnerable to malicious attacks, because of the unavailability of fixed infrastructure and wireless communication. In this paper, the trust based multi objective honey badger algorithm (TMOHBA) is proposed to achieve secure routing over the VANET. The TMOHBA is optimized by incorporating different cost functions, namely, trust, end to end delay (EED), routing overhead, energy, and distance. The developed secure route discovery using the TMOHBA is used to improve the robustness against the malicious attacks, for increasing the data delivery. Moreover, the shortest path discovery is used to minimize the delay while improving the security of VANET. The TMOHBA method is evaluated using the packet delivery ratio (PDR), throughput and EED. Existing researches such as hybrid enhanced glowworm swarm optimization (HEGSO) and ad-hoc on-demand distance vector based secure protocol (AODV-SP) are used to evaluate the TMOHBA method. The PDR of the TMOHBA method for 10 malicious attacks is 90.6446% which is higher when compared to the HEGSO and AODV-SP.</span>

Ultrafast Euclidean Shortest Path Computation Using Hub Labeling

Finding shortest paths in a Euclidean plane containing polygonal obstacles is a well-studied problem motivated by a variety of real-world applications. The state-of-the-art algorithms require finding obstacle corners visible to the source and target, and need to consider potentially a large number of candidate paths. This adversely affects their query processing cost. We address these limitations by proposing a novel adaptation of hub labeling which is the state-of-the-art approach for shortest distance computation in road networks. Our experimental study conducted on the widely used benchmark maps shows that our approach is typically 1-2 orders of magnitude faster than two state-of-the-art algorithms.

Efficient Algorithm for Constructing Order K Voronoi Diagrams in Road Networks

The order k Voronoi diagram (OkVD) is an effective geometric construction to partition the geographical space into a set of Voronoi regions such that all locations within a Voronoi region share the same k nearest points of interest (POIs). Despite the broad applications of OkVD in various geographical analysis, few efficient algorithms have been proposed to construct OkVD in real road networks. This study proposes a novel algorithm consisting of two stages. In the first stage, a new one-to-all k shortest path finding procedure is proposed to efficiently determine the shortest paths to k nearest POIs for each node. In the second stage, a new recursive procedure is introduced to effectively divide boundary links within different Voronoi regions using the hierarchical tessellation property of the OkVD. To demonstrate the applicability of the proposed OkVD construction algorithm, a case study of place-based accessibility evaluation is carried out. Computational experiments are also conducted on five real road networks with different sizes, and results show that the proposed OkVD algorithm performed significantly better than state-of-the-art algorithms.

A Novel Approach for Integrated Shortest Path Finding Algorithm (ISPSA) Using Mesh Topologies and Networks-on-Chip (NOC)

A novel data dispatching or communication technique based on circulating networks of any network IP is suggested for multi data transmission in multiprocessor systems using Networks-On-Chip (NoC). In wireless communication network management have some negatives have heavy data losses and traffic of data sending data while packet scheduling and low performance in the varied network due to workloads. To overcome the drawbacks, in this method proposed system is Integrated Shortest Path Search Algorithm (ISPSA) using mesh topologies. The message is sent to IP (Internet Protocol) in the network until the specified bus accepts it. Integrated Shortest Path Search Algorithm for communication between two nodes is possible at any one moment. On-chip wireless communications operating at specific frequencies are the most capable option for overcoming metal interconnects multi-hop delay and excessive power consumption in Network-on-Chip (NoC) devices. Each node can be indicated by a pair of coordinates (level, position), where the level is the tree's vertical level and the view point is its horizontal arrangement in the sequence of left to right. The output gateway node's n nodes are linked to two nodes in the following level, with all resource nodes located at the bottommost vertical level and the constraint of this topology is its narrow bisection area. The software Xilinx 14.5 tool by using that overall performance analysis of mesh topology, each method are reduced data losses with better accuracy although the productivity of the delay is decreased by 21 % was evaluated and calculated..

Shortest Path Finding in Quantum Networks With Quasi-Linear Complexity

A fully-quantum network implies the creation of quantum entanglement between a given source node and some other destination node, with a number of quantum repeaters in between. This paper tackles the problem of quantum entanglement distribution by solving the routing problem over an infrastructure based on quantum repeaters and with a finite number of pairs of entangled qubits available in each link. The network model considers that link purification is available such that a nested purification protocol can be applied at each link to generate entangled qubits with higher fidelity than the original ones. A low-complexity multi-objective routing algorithm to find the shortest path between any two given nodes is proposed and assessed for random networks, using a fairly general path extension mechanism that can fit a large family of particular technological requirements. Different types of quantum protocols require different levels of fidelity for the entangled qubit pairs. For that reason, the proposed algorithm identifies the shortest path between two nodes that assures an end-to-end fidelity above a specified threshold. The minimum requirements for the end-to-end entanglement fidelity depend on the whole extension of the paths, and cannot be looked at as a local property of each link. Moreover, one needs to keep track not only of the shortest path, but also of longer paths holding more entangled qubits than the shorter paths in order to satisfy the fidelity criterion. Thus, standard single parameter shortest-path algorithms do not necessarily converge to the optimal solution. The problem of finding the best path in a network subject to multiple criteria (known as multi-objective routing) is, in general, an NP-hard problem due to the rapid growth of the number of stored paths. This work proposes a metric that identifies and discards paths that are objectively worse than others. By doing so, the time complexity of the proposed algorithm scales near-to-linearly with respect to the number of nodes in the network, showing that the shortest-path problem in quantum networks can be solved with a complexity very close to the one of the classical counterparts. That is analytically proved for the case where all the links of a path have the same fidelity (homogeneous model). The algorithm is also adapted to a particular type of path extension, where different links along a path can be purified to different degrees, asserting its flexibility and near-to-linearity even when heterogeneous fidelities along the sections of a path are considered.

Tuning block-parallel all-pairs shortest path algorithm for efficient multi-core implementa- tion.

Finding shortest paths in a weighted graph is one of the key problems in computer-science, which has numerous practical applications in multiple domains. This paper analyzes the parallel blocked all-pairs shortest path algorithm at the aim of evaluating the influence of the multi-core system and its hierarchical cache memory on the parameters of algorithm implementation depending on the size of the graph and the size of distance matrix’s block. It proposes a technique of tuning the block-size to the given multi-core system. The technique involves profiling tools in the tuning process and allows the increase of the parallel algorithm throughput. Computational experiments carried out on a rack server equipped with two Intel Xeon E5-2620 v4 processors of 8 cores and 16 hardware threads each have convincingly shown for various graph sizes that the behavior and parameters of the hierarchical cache memory operation don’t depend on the graph size and are determined only by the distance matrix’s block size. To tune the algorithm to the target multi-core system, the preferable block size can be found once for the graph size whose in-memory matrix representation is larger than the size of cache shared among all processor’s cores. Then this blocksize can be reused on graphs of bigger size for efficient solving the all-pairs shortest path problem

Multiplexing mechanical and translational cues on genes

The genetic code gives precise instructions on how to translate codons into amino acids. Due to the degeneracy of the genetic code—18 out of 20 amino acids are encoded for by more than one codon—more information can be stored in a basepair sequence. Indeed, various types of additional information have been discussed in the literature, e.g., the positioning of nucleosomes along eukaryotic genomes and the modulation of the translating efficiency in ribosomes to influence cotranslational protein folding. The purpose of this study is to show that it is indeed possible to carry more than one additional layer of information on top of a gene. In particular, we show how much translation efficiency and nucleosome positioning can be adjusted simultaneously without changing the encoded protein. We achieve this by mapping genes on weighted graphs that contain all synonymous genes, and then finding shortest paths through these graphs. This enables us, for example, to readjust the disrupted translational efficiency profile after a gene has been introduced from one organism (e.g., human) into another (e.g., yeast) without greatly changing the nucleosome landscape intrinsically encoded by the DNA molecule.

(Invited) Towards First-Principles Prediction of Early SEI Formation

Despite decades of work, there is still considerable uncertainty regarding the major components of the solid-electrolyte interface (SEI) and its dynamic formation mechanism as a function of electrolyte and anode composition. Here we present a new data-driven first-principles framework using a combination of high-throughput calculations, reaction networks, machine learning and microkinetic modeling. Our automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic reaction landscape, calculated with density functional theory. We explore this landscape using stochastic methods and shortest pathfinding algorithms, which yield the most likely reaction pathways which are then refined with transition state calculations and kinetic information. The results of the framework show promise in being able to automatically recover previous insights on single reaction pathways, as well as successfully predicting the early dynamics and competitive nature of the SEI formation. As examples, we present formation mechanisms of LEMC as compared to LEDC and recover the Peled-like separation of the SEI into inorganic and organic domains resulting from rich reactive competition. By conducting accelerated simulations at elevated temperature, we track SEI evolution, confirming the postulated reduction of lithium ethylene monocarbonate to dilithium ethylene monocarbonate and hydrogen gas. These findings furnish fundamental insights into the dynamics of SEI formation and demonstrate a path forward toward a predictive understanding of electrochemical passivation.

Reliable efficient cluster routing protocol based HTDE scheme for UWSN

<p>Underwater sensor networks (UWSNs) are recently recognized as a promising method for monitoring and exploring the underwater environment. Due to real-time remote data monitoring requirements, UWSN has become a preferred network to a large extent. But reliable and efficient secure data transmission to the receiver is one of the most important challenges for UWSNs, often suffer from irreplaceable batteries and high latency for long-distance communications. The most challenging task is extending network life, shortening transmission distances for each node. Thus, this paper presents the reliable efficient cluster routing (RECR) protocol, optimal shortest path finding (OSPF) algorithm and hill transformation data encryption (HTDE) algorithm for UWSNs. The proposed encryption algorithm encrypts the data from the source node, and the RECR protocol is used for reliable data delivery from source to destination. To extend the network's life, RECR employs autonomous underwater vehicle (AUV) from the sink node (SN) for data collection. The OSPF algorithm is used to find the shortest path for data transmission to avoid latency. The proposed RECR protocol, HTDE and OSPF algorithm enhance the secure data transmission efficiency, minimizing the energy consumption and improves the network life time. The proposed protocol decreases end-to-end latency, packet loss.</p>

Evaluation of Metro Lines with Swarm Intelligence Approach

Metro lines, which have become one of the most important transportation routes for today's cities, require long design and implementation processes. Their design load is mostly due to detailed engineering calculations, have to expand to meet the needs of the ever-increasing population. The behavior of slime moulds to find the shortest path they use to reach the food source has inspired models created specifically to test the accuracy of highway routes. The hypothesis of this study is that the swarm behavior of slime moulds and the routes they follow may produce the same or similar results as the existing metro lines. In this research, an answer was sought to the question of whether a metro line designed with an approach based on swarm intelligence could be compatible with the existing line. The paper aims to develop a model that guides the design processes of metro lines by using swarm intelligence and shortest path finding strategies of slime moulds in a computational model. In the model, slime moulds were represented by multi-agent systems. The metro route was produced by the model using the station locations on an existing metro line selected as the study area. The agent-based simulation model was developed in the Grasshopper Physarealm plug-in environment. In the first stage of the model, a numerical model was created using the data and parameters of an existing metro line. In the second stage, the simulation was carried out to create a new route by preserving only the station locations of the existing metro line. Finally, the existing metro route and the route developed with the simulation model based on the intelligent agent behaviour in the digital environment were compared by overlapping. The results show that the route created by slime moulds only to reach the food is almost the same as the currently designed metro line. Topography data is ignored in the model. It is seen that the model developed by using metro design criteria is an important decision support aid for designers in determining metro routes.

Preserved anatomical bypasses predict variance in language functions after stroke

The severity of post-stroke aphasia is related to damage to white matter connections. However, neural signaling can route not only through direct connections, but also along multi-step network paths. When brain networks are damaged by stroke, paths can bypass around the damage to restore communication. The shortest network paths between regions could be the most efficient routes for mediating bypasses. We examined how shortest-path bypasses after left hemisphere strokes were related to language performance. Regions within and outside of the canonical language network could be important in aphasia recovery. Therefore, we innovated methods to measure the influence of bypasses in the whole brain.Distinguishing bypasses from all residual shortest paths is difficult without pre-stroke imaging. We identified bypasses by finding shortest paths in subjects with stroke that were longer than the most reliably observed connections in age-matched control networks. We tested whether features of those bypasses predicted scores in four orthogonal dimensions of language performance derived from a principal components analysis of a battery of language tasks. The features were the length of each bypass in steps, and how many bypasses overlapped on each individual direct connection. We related these bypass features to language factors using support vector regression, a technique that extracts robust relationships in high-dimensional data analysis. The support vector regression parameters were tuned using grid-search cross-validation.We discovered that the length of bypasses reliably predicted variance in lexical production (R2 = .576) and auditory comprehension scores (R2 = .164). Bypass overlaps reliably predicted variance in Lexical Production scores (R2 = .247). The predictive elongation features revealed that bypass efficiency along the dorsal stream and ventral stream were most related to Lexical Production and Auditory Comprehension, respectively. Among the predictive bypass overlaps, increased bypass routing through the right hemisphere putamen was negatively related to lexical production ability.

Study on the Shortest Path finding of Engine Room Patrol Robots Using the A* Algorithm

Study on the Shortest Path finding of Engine Room Patrol Robots Using the A* Algorithm

FIMA: A New Fuzzy Item sets Miner Algorithm

Frequent itemsets mining is the second step of Association rules mining which is the main task of Knowledge Discovery (KDD) and data mining (DM). There are three types of itemsets; Crisp (CI), Generalized (GI), and Fuzzy itemsets (FI). FI is the most recent type. This paper presents a new algorithm to mine fuzzy itemsets from large taxonomic databases depending on fuzzy taxonomies that reflect partial belongings among data items, also it depends on Item-Transaction layout, and shortest path finding between an item and its super classes. The proposed algorithm, Fuzzy Itemsets Miner Algorithm (FIMA) deals with the three types of fuzzy itemsets; taxonomic nodes, linguistic terms, and hedges. FIMA scans the database, under mining, only once. It excludes the need for complicated data structures, prunes the pruning steps of available algorithm, and avoids the weakness of manipulating low levels values of minimum support threshold. The algorithm performs much better than the available algorithm such that it reduces the complexity of mining FIs from exponential, O(an), to linear order of magnitude O(n).

Finding the shortest path in a familiar environment: A comparison between describing and walking a path after accounting for the role of individual factors

Finding the shortest path to a destination is a refined navigation ability little explored as yet in familiar environments. The present study examined this ability when walking or describing the path, and how performance relates to individual differences. Sixty-seven undergraduates familiar with the area around their campus were asked to find the shortest path to a destination by walking there or describing it in writing. Several visuospatial tasks and questionnaires were administered. It emerged that shortest path finding performance was supported by familiarity and sense of direction. After accounting for these individual factors, participants performed better when walking than when describing a path. Overall, the results showed that retrieving spatial knowledge about familiar environments relates to individual differences and recall condition, walking a path being easier than describing it.

Moving Agents in Formation in Congested Environments

In this paper, we formalize and study the Moving Agents in Formation (MAiF) problem, that combines the tasks of finding short collision-free paths for multiple agents and keeping them in close adherence to a desired formation. Previous work includes controller-based algorithms, swarm-based algorithms, and potential-field-based algorithms. They usually focus on only one or the other of these tasks, solve the problem greedily without systematic search, and thus generate costly solutions or even fail to find solutions in congested environment. In this paper, we develop a two-phase search algorithm, called SWARM-MAPF, whose first phase is inspired by swarm-based algorithms (in open regions) and whose second phase is inspired by multi-agent path-finding (MAPF) algorithms (in congested regions). In the first phase, SWARM-MAPF selects a leader among the agents and finds a path for it that is sufficiently far away from the obstacles so that the other agents can preserve the desired formation around it. It also identifies the critical segments of the leader's path where the other agents cannot preserve the desired formation and the refinement of which has thus to be delegated to the second phase. In the second phase, SWARM-MAPF refines these segments. Theoretically, we prove that SWARM-MAPF is complete. Empirically, we show that SWARM-MAPF scales well and is able to find close-to-optimal solutions.

Data-Driven Prediction of Formation Mechanisms of Lithium Ethylene Monocarbonate with an Automated Reaction Network.

Interfacial reactions are notoriously difficult to characterize, and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid-electrolyte interphase (SEI), critical to Li-ion batteries (LIBs), exemplifies such a surface film, and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. Here we use a reaction network to investigate whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic reaction landscape, calculated with density functional theory. The shortest pathfinding algorithms are employed to explore the reaction landscape and obtain previously proposed formation mechanisms of LEMC as well as several new reaction pathways and intermediates. For example, we identify two novel LEMC formation mechanisms: one which involves LiH generation and another that involves breaking the (CH2)O-C(═O)OLi bond in LEDC. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared with LEDC, a conclusion that has direct impact on the SEI formation in Li-ion energy storage systems. Finally, the data-driven framework presented here is generally applicable to any electrochemical system and expected to improve our understanding of surface passivation.

Automatic Versus Human Navigation in Information Networks

People regularly face tasks that can be understood as navigation in information networks, where the goal is to find a path between two given nodes. In many such situations, the navigator only gets local access to the node currently under inspection and its immediate neighbors. This lack of global information about the network notwithstanding, humans tend to be good at finding short paths, despite the fact that real-world networks are typically very large. One potential reason for this could be that humans possess vast amounts of background knowledge about the world, which they leverage to make good guesses about possible solutions. In this paper we ask the question: Are human-like high-level reasoning skills really necessary for finding short paths? To answer this question, we design a number of navigation agents without such skills, which use only simple numerical features. We evaluate the agents on the task of navigating Wikipedia, a domain for which we also possess large-scale human navigation data. We observe that the agents find shorter paths than humans on average and therefore conclude that, perhaps surprisingly, no sophisticated background knowledge or high-level reasoning is required for navigating the complex Wikipedia network.