Finding Shortest Paths Research Articles

Finding Optimal Algorithm in Artificial Intelligence

Present days many artificial intelligence search algorithms are plays a important to figure out the problem of shortest path finding. The paper presents the detailed study of heuristic search and blind search techniques. The paper focus additional in the direction of blind search strategies such as Breadth First Search, Depth First Search, and Uniform Cost Search and informed explore strategies like A*, and Best First Search. The paper consist of effective of search procedure, their qualities, and demerits, where these algorithms are applicable, also at last comparison of search techniques based on complexity, optimality and completeness are presented in tabular structure.

IADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV

Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.

The Constrained Reliable Shortest Path Problem in Stochastic Time-Dependent Networks

Finding Shortest Paths That Are Reliable and Satisfy Constraints on Time-Dependent Weights

Iteratively reweighted least squares and slime mold dynamics: connection and convergence

We present a connection between two dynamical systems arising in entirely different contexts: the Iteratively Reweighted Least Squares (IRLS) algorithm used in compressed sensing and sparse recovery to find a minimum $$\ell _1$$ -norm solution in an affine space, and the dynamics of a slime mold (Physarum polycephalum) that finds the shortest path in a maze. We elucidate this connection by presenting a new dynamical system – Meta-Algorithm – and showing that the IRLS algorithms and the slime mold dynamics can both be obtained by specializing it to disjoint sets of variables. Subsequently, and building on work on slime mold dynamics for finding shortest paths, we prove convergence and obtain complexity bounds for the Meta-Algorithm that can be viewed as a “damped” version of the IRLS algorithm. A consequence of this latter result is a slime mold dynamics to solve the undirected transshipment problem that computes a $$(1+\varepsilon )-$$ approximate solution in time polynomial in the size of the input graph, maximum edge cost, and $$\frac{1}{\varepsilon }$$ – a problem that was left open by the work of (Bonifaci V et al. [10] Physarum can compute shortest paths. Kyoto, Japan, pp. 233–240).

Dijkstra and Bidirectional Dijkstra on Determining Evacuation Routes

Determination of the best path or often called the shortest path finding is a method that has many benefits and can be applied in various cases and fields of science. In previous studies, authors have developed the shortest path finding program using various algorithms, such as held-karp, iterative deepening search, bidirectional depth first search and depth limited search. In this research author implements shortest path finding on information system for determining fire disaster evacuation route using Dijkstra and bidirectional Dijkstra. The program was developed based on web using vuetify.js framework. Both algorithms were tested on several datasets and the results will be compared to find out the strengths and weaknesses. Based on the research, the algorithm was successfully developed based on the web using the vuetify.js framework and successfully tested on several datasets, it is known that 40% of the test results show that bidirectional Dijkstra is better than Dijkstra, 10% vice versa and 50% get the same results from both algorithms. From this comparison it can be concluded that the bidirectional Dijkstra algorithm is better than Dijkstra. This research succeeded in proving that the bidirectional Dijkstra algorithm can produce a better route than the standard or classic Dijkstra algorithm.

Phototropic algorithm for global optimisation problems

Problem solving and decision-making have a vital role to play in both technical and non-technical fields. Some decisions are simple while others require more effort and time to solve. This article introduces a new problem solving technique called Phototropic optimization algorithm, inspired from the optimised growth pattern in plants. It has been observed that the stem tips of a plant always grow towards sunlight. In this algorithm, the underlying hormonal mechanism of phototropism is emulated to solve computational problems. This phenomenon has indicated strong prospects of algorithmic efficiency and invites further research into prospective computational applications. Phototropic algorithm is developed as an optimization technique to solve real time application such as shortest path finding problems, travelling salesman problem, finding congestion in a network or any similar problem seen around. A prototype on finding the minimal distance between any two nodes in the physical network is modelled here. The asymptotic time complexity analysis shows the algorithm routes packages in O (n log n). Comparison with the traditional algorithms gives sufficient evidence for the efficiency of this proposal. This can be implemented over Software Defined Networks (SDN) for increasing system capabilities in route analytics and functionalities. Extension of this optimization algorithm is useful to solve various real time problems.

Optimal Path Solution Using Dijkstra's Algorithm for Practical 21 bus system

This paper presents the Shortest Path Finding algorithm to identify the optimal path for a distribution system during restoration using Dijkstra's method. The objective is to reduce the power losses and obtain an efficient restoration plan after an extensive outage of the system. The power losses of the distribution system are calculated using Forward-Backward sweep load flow method. The proposed method has been implemented for a practical 21 bus distribution system using MATLAB programming .

An efficient shortest path routing on the hypercube with blocking/faulty nodes

SummaryWe investigate fault‐tolerant shortest path problem in the hypercube between two nodes where some nodes are faulty (or blocked) and thus cannot be used in routing. Previously, several similar problems were studied where proposed algorithms are distributed and local‐information‐based, that is, each node in the network knows only its neighbor's status (faulty or not) and they also look for optimal or near‐optimal paths. There have been studies that established some sufficient conditions for these paths to exist. Since these conditions are only sufficient, there could be shortest paths that will be missed by these conditions. We study the problem under the assumption that for two given nodes, a source node s, a target node t, only s requires to have a global information of the network in order to find a shortest path to t, should it exist. A shortest path is defined as the Hamming distance between s and t. This problem can be solved by trivial algorithms. The first is to try all possible paths. In an n‐dimensional hypercube with 2n vertices, this method would cost at least n! time. Another method is to perform a standard shortest path finding algorithm, which would require at least 2n time. A routing algorithm has been previously developed which is efficient in certain situations. However, in the worst case, its running time could be exponential in the hypercube dimension. We propose an efficient algorithm with running time of O(n3m2), polynomial in n, the hypercube dimension, and m, number of blocking nodes. We gain our efficiency by reducing the routing problem to a permutation problem which can be solved using inclusion‐exclusion principle. We finally use dynamic programming technique to optimally count the terms. With the proposed algorithm, not only can we find a shortest path, if such a path does exist, but we can also count all possible shortest paths.

Truncated Tree Based Hybrid Beamforming for Multi-Panel MIMO

Multi-panel MIMO is a practical antenna array structure for massive MIMO, but the optimal hybrid beamforming design has a complexity exponential to the number of antenna panels. In this letter, we propose a truncated tree based hybrid beamforming design with linear complexity for multi-panel MIMO transmission. We convert the hybrid beamforming design problem into the shortest path finding problem that is visualized by tree data structure. To reduce complexity, the tree is truncated by deleting the intermediate paths that are less likely to be the shortest path. By this means, the truncated tree based design reduces exponential time complexity to linear time complexity. Numerical results show that the proposed truncated tree based design far outperforms the heuristic max power gain beamforming design and achieves near optimal performance with a proper truncation ratio.

An IoT System for Social Distancing and Emergency Management in Smart Cities Using Multi-Sensor Data

Smart cities need technologies that can be really applied to raise the quality of life and environment. Among all the possible solutions, Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) have the potentialities to satisfy multiple needs, such as offering real-time plans for emergency management (due to accidental events or inadequate asset maintenance) and managing crowds and their spatiotemporal distribution in highly populated areas (e.g., cities or parks) to face biological risks (e.g., from a virus) by using strategies such as social distancing and movement restrictions. Consequently, the objective of this study is to present an IoT system, based on an IoT-WSN and on algorithms (Neural Network, NN, and Shortest Path Finding) that are able to recognize alarms, available exits, assembly points, safest and shortest paths, and overcrowding from real-time data gathered by sensors and cameras exploiting computer vision. Subsequently, this information is sent to mobile devices using a web platform and the Near Field Communication (NFC) technology. The results refer to two different case studies (i.e., emergency and monitoring) and show that the system is able to provide customized strategies and to face different situations, and that this is also applies in the case of a connectivity shutdown.

A Generic Approach on How to Formally Specify and Model Check Path Finding Algorithms: Dijkstra, A* and LPA*

The paper describes how to formally specify three path finding algorithms in Maude, a rewriting logic-based programming/specification language, and how to model check if they enjoy desired properties with the Maude LTL model checker. The three algorithms are Dijkstra Shortest Path Finding Algorithm (DA), A* Algorithm and LPA* Algorithm. One desired property is that the algorithms always find the shortest path. To this end, we use a path finding algorithm (BFS) based on breadth-first search. BFS finds all paths from a start node to a goal node and the set of all shortest paths is extracted. We check if the path found by each algorithm is included in the set of all shortest paths for the property. A* is an extension of DA in that for each node [Formula: see text] an estimation [Formula: see text] of the distance to the goal node from [Formula: see text] is used and LPA* is an incremental version of A*. It is known that if [Formula: see text] is admissible, A* always finds the shortest path. We have found a possible relaxed sufficient condition. The relaxed condition is that there exists the shortest path such that for each node [Formula: see text] except for the start node on the path [Formula: see text] plus the cost to [Formula: see text] from the start node is less than the cost of any non-shortest path to the goal from the start. We informally justify the relaxed condition. For LPA*, if the relaxed condition holds in each updated version of a graph concerned including the initial graph, the shortest path is constructed. Based on the three case studies for DA, A* and LPA*, we summarize the formal specification and model checking techniques used as a generic approach to formal specification and model checking of path finding algorithms.

Observer of changes in the forest of the shortest paths on dynamic graphs of transport networks

The purpose of the work is the development of basic data structures, speed-efficient and memoryefficient algorithms for tracking changes in predefined decisions about sets of shortest paths on transport networks, notifications about which are received by autonomous coordinated transport agents with centralized or collective control. A characteristic feature of transport operations is the independence and asynchrony of the emergence of perturbations of optimal solutions, as well as the lack of global influence of individual perturbations on the set of all processes on the network. This clearly determines the feasibility of realizing the idea of reoptimizing existing solutions in real time as information is received about disturbances in the structure and parameters of the transport network, various restrictions on the use of existing shortest paths. In contrast to the classical problems of finding shortest paths on static or dynamic graphs, it is proposed to supplement the set of situations controlled by the observer by taking into account the associations of shortest path trees with agents that actually use such paths. This will improve the responsiveness of agent notification processes for timely switching to a new path. The space of search states is a dynamically generated bipartite sparse graph of the transport network, represented by a list of arcs. The basic algorithm for finding the shortest paths uses Dijkstra's scheme, but implements a bootstrapping method to generate the search result. The compactness of the representation of the observed forest of shortest paths is achieved by mapping individual trees of such a forest onto the projection of tree vertices in memory, where the position of each vertex corresponds to the distance from the tree root. The proposed version of the construction of the search procedure is based on the mechanisms existing in database management systems for creating different relational representations of the physical data model. This eliminates the need to solve technological problems of complexing heterogeneous models of dynamic transport networks, memory allocation. As a result, the specification of various rules for the logistics of transport operations is simplified, since such operations in terms of object-oriented models are easily determined by polymorphic classes of transitions between nodes of the transport network.

Design of High Speed Reconfigurable Distributed Life Time Efficient Routing Algorithm in Wireless Sensor Network

Designing an energy-efficient routing makes the Wireless Sensor Networks (WSN) more effective and attractive for different applications. The WSN communication system power consumption mainly depends on three aspects such as routing cost computation, signal interference, and routing distance. All three factors are equally important in order to improve the network performance. The system reliability and deployment cost depends on the energy efficiency of the WSN. The energy related cost assignment and shortest paths identification are used in existing routing techniques. In the existing routing techniques maximum achievable lifetime and optimal link cost are low. Hence greatest possible performance can be achieved in distributed routing algorithm by finding shortest path. Maximum lifetime and best cost link can be generally obtained using distributed shortest path routing algorithm. In this paper high speed reconfigurable distributed Lifetime-Efficient Routing algorithm is designed to provide route selection outline with low complexity and obtain better performance compared to existing routing algorithm.

A minimum barrier distance for multivariate images with applications

Distance transforms and the saliency maps they induce are widely used in image processing, computer vision, and pattern recognition. The minimum barrier distance (MBD) has proved to provide accurate results in this context. Recently, Géraud et al. have presented a fast-to-compute alternative definition of this distance, called the Dahu pseudo-distance. This distance is efficient, powerful, and have many important applications. However, it is restricted to grayscale images. In this article we revisit this pseudo-distance. First, we offer an extension to multivariate image. We call this extension the vectorial Dahu pseudo-distance . We provide an efficient way to compute it. This new version is not only able to deal with color images but also multi-spectral and multi-modal ones. Besides, through our benchmarks, we demonstrate how robust and competitive the vectorial Dahu pseudo-distance is, compared to other MB-based distances. This shows that this distance is promising for salient object detection, shortest path finding, and object segmentation. Secondly, we combine the Dahu pseudo-distance with the geodesic distance to take into account spatial information from the image. This combination of distances provides efficient results in many applications such as segmentation of thin elements or path finding in images.

Unifying Offline and Online Multi-Graph Matching via Finding Shortest Paths on Supergraph.

This paper addresses the problem of multiple graph matching (MGM) by considering both offline batch mode and online setting. We explore the concept of cycle-consistency over pairwise matchings and formulate the problem as finding optimal composition path on the supergraph, whose vertices refer to graphs and edge weights denote score function regarding consistency and affinity. By our theoretical study we show that the offline and online MGM on supergraph can be converted to finding all pairwise shortest paths and single-source shortest paths respectively. We adopt the Floyd algorithm [1] and shortest path faster algorithm (SPFA) [2] , [3] to effectively find the optimal path. Extensive experimental results show our methods surpass state-of-the-art MGM methods, including CAO [4] , MISM [5], IMGM [6] , and many other recent methods in offline and online settings. Source code will be made publicly available.

EFFICIENCY COMPARISON OF PATHFINDING ALGORITHMS A* AND DIJKSTRA’S IN TWO DIMENSIONAL GRID

The shortest path finding algorithms are applied more and more in different industries. There are many pathfinding algorithms, but the most of them differ from their modified versions. The goal of this work was to create A* and Dijkstra’s pathfinding algorithms and compare their execution time.

Reliable shortest path finding in stochastic time-dependent road network with spatial-temporal link correlations: A case study from Beijing

In view of the time-dependent characteristic of travel times in road networks and the travel time reliability (TTR) requirements by different travelers, it is complicated and time-consuming to determine the reliable shortest path (RSP) in large-scale road networks. To search the RSP in stochastic and time-dependent (STD) network with spatial-temporal correlated link travel times, an efficient path finding algorithm is presented. First, the fitting test results based on floating car data show that it is more appropriate to characterize the travel time distributions (TTDs) of links using lognormal distributions. In order to quantify spatial-temporal correlations between links, correlation coefficients of link travel times are calculated. Also, influences of spatial distance (counted by the number of links), temporal distance (counted by the number of time intervals) and road type on link correlations is analyzed. Afterwards, the dynamic moment-matching method (DMM) is used to calculate the approximate path TTD when correlated link travel times are considered. Accounting for different travelers' risk tolerance, a dynamic-moment-matching-based A* algorithm (STCRSP-DMA*) is proposed to provide personalized path navigation for individual travelers. Last, numerical case studies based on abundant floating car data as well as a subsistent road network in Beijing are conducted to demonstrate the applicability and the computational advantage of the devised algorithm in solving RSP searching problems.

Knowledge of familiar environments: Assessing modalities and individual visuo-spatial factors

Familiarity enables us to form elaborate mental representations of environments, which are usually assessed with tasks that involve managing spatial information (such as pointing and locating landmarks). The present study also examines the role of familiarity using a “field” task that involved finding the shortest way to a destination, and the contribution of individual visuo-spatial factors (a set of abilities, preferences and strategies). Undergraduates more or less familiar with their university campus (45 in each group) performed pointing and landmark-locating (spatial information managing) tasks, and a shortest path finding task, and were administered several visuo-spatial measures. The results showed that familiarity had no effect on spatial information managing performance, but did influence shortest path finding. Individual visuo-spatial factors variously supported pointing accuracy, and shortest path finding performance. These results broaden our knowledge of how individual factors (familiarity and visuo-spatial abilities, preferences and strategies) jointly support the knowledge of an environment.

A Shortest Path Finding Time-Based Accelerator Core With Built-in Gravity Control and Buffer Zone for Smooth 3-D Navigation

A mixed-signal time-based 65-nm application specific integrated circuit is developed for solving shortest-path problems in 3-D. Previous path planning ASICs have been restricted to 2-D maps due to computational complexity or physical architecture limitations. Our time-based, asynchronous, one-shot architecture has been coupled with a novel dual axis interleaving strategy to solve the multidimensional shortest path problem in a simple, energy efficient manner. Additional features include circuit-based solutions for obstacle blockage avoidance and gravity. The efficacy of the proposed ASIC is evaluated on a drone navigation application, 3-D Voronoi diagrams, and a physical optics experiment. The chip is twice as energy efficient as prior 2-D work while containing $5\times $ more vertices and $7.5\times $ additional edge connections.

Routing Protocol based on Floyd-Warshall Algorithm Allowing Maximization of Throughput

Routing protocol based on Floyd-Warshall algorithm which allows maximization of throughput is proposed. The metric function in the proposed routing protocol is throughput including not only send packets but also retransmission packets in order for improving effectiveness and efficiency of the network in concern. Through simulation studies, it is found that the proposed routing protocol is superior to the conventional Open Shortest Path First: OSPF based on Dijkstra algorithm for shortest path determination from the point of view of maximizing throughput. A routing protocol for Virtual Private Network (VPN) in Autonomous System (AS) based on maximizing throughput is proposed. Through a comparison between the proposed protocol and the existing protocols, OSPF (Widely used), it is also found that the required time for transmission of packets from one node to another node of the proposed protocol is 56.54% less than that of the OSPF protocol.