Traditional Dijkstra Algorithm Research Articles

Low Latency Extended Dijkstra Algorithm with Multiple Linear Regression for Optimal Path Planning of Multiple AGVs Network

Dijkstra algorithms are typically used to find the shortest path from a source node to a destination node. It is widely used in various applications due to its reliability and less complexity. This paper presents the extended Dijkstra Algorithm with lower latency and consumes less computing memory intended for implementation in many AGVs networks for effective decentralized task distribution path planning. This paper proposed linear regression normalization across the node network in Dijkstra architecture to reduce computing time and memory consumption. The issue addressed through this optimization focused on reducing the possibilities of collision between AGVs and deadlock. The extended Dijkstra algorithm significantly reduces computing time compared to the traditional Dijkstra algorithm. In addition, the proposed solutions suggest better AGV routing for collision avoidance and deadlock prevention possibilities.

Optimal Transshipment Route Planning Method Based on Deep Learning for Multimodal Transport Scenarios

The optimal path problem is an important topic in the current geographic information system (GIS) and computer science fields. The Dijkstra algorithm is a commonly used method to find the shortest path, which is usually used to find the least cost path from a single source. Based on the analysis and research of the traditional Dijkstra algorithm, this paper points out the problems of the Dijkstra algorithm and optimizes it to improve its storage capacity and operation efficiency. Then, combined with the traffic elements, a new network-based optimal path planning method is established. However, the existing network is far from actual operation in terms of the expansion of the transportation network, the uncertainty of the transportation environment, and the differences in the transportation area. Therefore, this paper proposes an optimal transshipment path planning method based on deep learning, which is oriented to multimodal transportation scenarios. This paper mainly introduces the intelligent transportation system and intelligent navigation system, and then conducts in-depth research on optimal path planning. This paper also uses the deep neural network algorithm to optimize the calculation, and finally analyzes its use and application. Simulation experiments were also performed to analyze the relationship between energy consumption, emissions, speed, load cost, and other factors under the optimal path. The final experimental results show that within the range of the emission limit of [100,200], the emission is 50%, the emission is less than 100%, but the emission is higher than 75%. In [100,200], 75% of the loading rate emits no less than 100%. In [200,300], the 50% and 100% emissions are the same. This also means that the emissions are the same but the paths are not necessarily the same.

Optimal Evacuation Route Planning of Urban Personnel at Different Risk Levels of Flood Disasters Based on the Improved 3D Dijkstra’s Algorithm

In the event of a flood, the choice of evacuation routes is vital for personnel security. This is particularly true when road factors play an important role in evacuation time. In this study, the traditional Dijkstra algorithm for route planning is improved, and the evacuation model is improved from 2D to 3D. At the same time, the Lasso regression method is adopted to take the road factors into account in the pedestrian speed, and the location of shelter is selected and optimized through the calculation results, and then based on the improved 3D Dijkstra’s algorithm, an optimal evacuation route method in different flood disasters risk levels is proposed, which can make pedestrians reach the shelters within the shortest time. After taking into account road factors (road width, slope, non-motorized lane width, and pedestrian density), through the calculation of the pedestrian speed formula, the estimated evacuation time of pedestrians is obtained. By combining available shelters with evacuation routes, the optimized algorithm improves the evacuation efficiency facing different risk levels of flood disasters. The results show that when residents are confronted with flood disasters of once-in-20-year, once-in-50-year, and once-in-100-year, the proposed optimization algorithm can save 7.59%, 11.78%, and 17.78% of the evacuation time. Finally, according to the verification of the actual effect in Meishan Town, the proposed method of optimal evacuation route planning can effectively reduce the evacuation time of pedestrians, evaluate, and optimize the location of existing shelter, and provide suggestions for urban road reconstruction.

Research and Application of Intelligent Layout Design Algorithm for 3D Pipeline of Nuclear Power Plant

The pipeline layout design of nuclear power plant is to find the optimal route to meet the objectives and constraints in the 3D routing space. However, due to the intensive equipment and complex structure of the nuclear power plant, various types of pipeline systems, complex layout constraints, and a large number of pipelines, even for experienced designers, pipeline layout is a difficult and time-consuming task. In order to solve the problem of the automatic layout of pipeline in 3D routing space of nuclear power plant, a pipeline automatic routing method combining Dijkstra algorithm in large space and improved A∗algorithm in local space is proposed in this paper. Firstly, the method identifies the key vertices of each room in the nuclear power plant, constructs the topological routing map, and determines the preliminary passage area of the pipeline through the traditional Dijkstra algorithm. Secondly, the space of the layout area is divided into 3D grids, and then the items in the area are identified and preprocessed. Finally, the 3D pipeline routing environment is established through AABB-OBB hybrid collision detection technology. On this basis, the improved method of A∗evaluation function is given to satisfy the pipeline layout constraints and improve the search efficiency. Through experiments, the effectiveness of this method is proved. This method can quickly and automatically route the nuclear power pipelines that meet the requirements of the engineering, which greatly improves the efficiency of 3D pipeline layout design for the nuclear power plant.

Design and Implementation of the Optimization Algorithm in the Layout of Parking Lot Guidance

The information guidance system for parking spaces in large- and medium-sized parking lots is not efficient at present. It tends to be difficult to find an empty parking space in parking lots in big cities. One of the problems is the large amount of calculation in the traditional Dijkstra algorithm. In this paper, an improved Dijkstra algorithm is presented and optimized to find the best parking path with the purpose of looking for the nearest free parking space based on the layout model in parking lot parking guidance. The experiments show that the improved Dijkstra algorithm can find the optimal parking space and the optimal parking path and improve the parking efficiency.

A Coupling Path Analysis Method for High Power Microwave Interaction With Electronic Systems

In order to analyze the electromagnetic coupling path of real complex electronic system in high power microwave more conveniently and effectively, based on the electromagnetic topology theory, a signal flow graph drawing and simplification method based on coupling port is proposed, and the assignment principle of coupling path are given. The new method can satisfy the construction of signal flow graph in high power microwave environment with multiple interference sources and different bandwidths. Furthermore, aiming at the bidirectional coupling problem of signal flow graph and the shortest path calculation problem under different limit encountered in practical engineering application, this letter proposes an improved Floyd algorithm combined with double-sweep algorithm, which can solve the deficiency of traditional Dijkstra algorithm and Ford algorithm in solving the above engineering problems. Finally, the coupling path analysis method under the action of multiple interference sources is briefly discussed. The method proposed in this letter is more suitable for practical engineering, and it’s the base of the further electromagnetic protection work.

The Path Optimization Algorithm of Car Navigation System considering Node Attributes under Time-Invariant Network

Vehicle path planning plays a key role in the car navigation system. In actual urban traffic, the time spent at intersections accounts for a large proportion of the total time and cannot be ignored. Therefore, studying the shortest path planning problem considering node attributes has important practical significance. In this article, we study the vehicle path planning problem in time-invariant networks, with the minimum travel time from the starting node to the destination node as the optimization goal (including node time cost). Based on the characteristics of the problem, we construct the mathematical model. We propose a Reverse Order Labeling Algorithm (ROLA) based on the traditional Dijkstra algorithm to solve the problem; the correctness of the proposed algorithm is proved theoretically, and we analyse and give the time complexity of the ROLA and design a calculation example to verify the effectiveness of the algorithm. Finally, through extensive simulation experiments, we compare the performance of the proposed ROLA with several other existing algorithms. The experimental results show that the proposed algorithm has good stability and high efficiency.

AGV Path Planning based on Improved Dijkstra Algorithm

Aiming at the path planning problem of Automated Guided Vehicle (AGV) in intelligent storage, an improved Dijkstra algorithm that combines eight-angle search method and Dijkstra algorithm for path optimization is proposed. The grid method is used to model the storage environment, and the improved Dijkstra algorithm is used to optimize the route of the AGV. The simulation test of the AGV path planning process with Matlab shows that the AGV can effectively avoid obstacles by using the traditional Dijkstra algorithm and the improved Dijkstra algorithm, and then search for a collision-free optimized path from the start point to the end point; and the traditional Dijkstra algorithm In comparison, the path length planned by the improved Dijkstra algorithm is shorter and the turning angle is less, indicating that the improved algorithm is correct, feasible and effective, and has a strong global search ability.

A New Algorithm Based on Dijkstra for Vehicle Path Planning Considering Intersection Attribute

Vehicle path planning is a key issue for car navigation systems. When path planning, considering the time spent at intersections is more in line with the actual situation, so it is of practical significance to study the path planning problem take account intersection attributes. In this article, we study the problem in a deterministic network, taking the minimization of travel time from the origin to the destination as the optimization goal. For this purpose, we construct a mathematical model for the problem. This paper proposes a reverse labeling Dijkstra algorithm (RLDA) based on traditional Dijkstra algorithm to solve the problem, it is proved that the correctness of the RLDA algorithm theoretically, and analyze that the RLDA algorithm has a lower polynomial time complexity. Finally, we selected the actual road network as the simulation experiment object to verify the effectiveness of the algorithm searching for the optimal path. And select 10 groups of networks of different sizes and conduct extensive experiments to compare the convergence efficiency and calculation speed between RLDA and PSO, GA, ACO, NNA, OPABRL. The statistical results show that the convergence rate of the RLDA algorithm is better than that of ACO, NNA, and GA. When the number of network nodes is less than 350, the algorithm has the smallest running time.

Dynamic emergency route planning for major chemical accidents: Models and application

Combining scenario construction with the characteristics of individual emergency behavior is necessary for the emergency route planning of major chemical accidents. We investigated this challenging decision problem and constructed a multi-indicator emergency risk assessment method that considers the evacuation speed of different population types and health consequences caused by various risk components. We also designed a modified Dijkstra algorithm to solve this dynamic multi-objective route planning problem. The comparative experiment results demonstrated that the proposed algorithm performs relatively better than the traditional Dijkstra algorithm. Finally, we performed extensive case studies where our simulation results demonstrate that the proposed model provides reliable and practical emergency route planning services for various personnel types under different accident scenarios. Compared with the commonly used single-dimensional assessment method, this comprehensive and informative assessment of the emergency risks faced by the population in different regions could serve as a useful reference for the formulation and implementation of emergency plans in case of major chemical accidents.

Application of Improved Dijkstra Algorithm in Coastal Tourism Route Planning

Chen, Y., 2020. Application of improved Dijkstra algorithm in coastal tourism route planning. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 251–254. Coconut Creek (Florida), ISSN 0749-0208.With the development of computer science and geographic information science, there are more and more applications for GIS. Shortest path analysis is a key issue in GIS geographic network analysis. One of the classical algorithms for calculating the shortest path is the Dijkstra algorithm, which is used in many projects to solve the shortest path problem. However, when the traditional Dijkstra algorithm solves the shortest path between nodes, a large number of nodes outside the identified nodes are calculated, which affects the speed of the algorithm. The Dijkstra algorithm is a classical algorithm for solving the shortest path in the embedded GIS system. By analyzing the Dijkstra algorithm, changing the storage structure, and examining graphing methods, the binary ordering tree algorithm based on the rectangular restricted area reduces the memory storage space. Shortening the query time requires optimizing the shortest path calculation process; the actual data test also shows the effectiveness of the algorithm.

Surface Optimal Path Planning Using an Extended Dijkstra Algorithm

Extensive studies have been conducted on the Dijkstra algorithm owing to its bright prospect. However, few of them have studied the surface path planning of mobile robots. Currently, some application fields (e.g., wild ground, planet ground, and game scene) need to solve the optimal surface path. This paper proposes an extended Dijkstra algorithm. We utilize the Delaunay triangulation to model the surface environment. Based on keeping the triangle side length unchanged, the triangle mesh on the surface is equivalently converted into a triangle on the two-dimensional plane. Through this transformation, we set up the two-dimensional developable passable channel of the surface and solve the optimal route on this channel. Traversing all the two-dimensional developable and passable paths of the surface, we can get the shortest route among all the optimal paths. Then the inverse transformation from the two-dimensional plane coordinates to the corresponding surface coordinates obtains the surface optimal path. The simulation results show that, compared with the traditional Dijkstra algorithm, this method improves the accuracy of the surface optimization path in single-robot single-target and multi-robot multi-target path planning tasks.

A Virtual Network Resource Allocation Model Based on Dynamic Resource Pricing

The key to network virtualization technology is virtual network mapping, which has been proven to be an NP-hard problem. At present, the methods to solve the problem of virtual network mapping still have the following defects. Most of the existing literature is limited to static virtual network (VN) mapping and static linear resource pricing, which rely on peak allocation and don't meet the user dynamic resource requirements. Therefore, this paper proposes a virtual network resource allocation model based on dynamic resource pricing named GSO-RBFDM. Firstly, group search optimization (GSO) is used to optimize the node mapping scheme during the network mapping process to reduce the cost of network mapping. Secondly, a dynamic nonlinear resource pricing model is established, and genetic algorithm (GA) is used to more accurately search a low-cost network mapping path instead of the traditional Dijkstra algorithm. Finally, virtual network dynamic modeling is performed according to the user dynamic resource requirements, and radial basis function (RBF) is used to predict resource requirements to realize the dynamic resource allocation to users. Simulation results show that, compared with traditional virtual network mapping algorithms, GSO-RBFDM can not only realize dynamic resource allocation, but also show good performance in terms of acceptance rate, network cost, link pressure and average network revenue.

Information-Aware Secure Routing in Wireless Sensor Networks.

Secure routing is crucial for wireless sensor networks (WSNs) because they are vulnerable to various attacks. In this paper, we propose a new secure routing protocol for WSNs in the presence of malicious nodes. For each relay node in the route, associated information such as its trust value and status is considered in the protocol. The trust value is defined as the attack probability of the node according to previous packet-forwarding behaviors, and the status is a hybrid metric that combines the residual energy and distance to the sink node. Therefore, the route generated by the protocol is secure against malicious attacks and globally optimal according to the associated information. We used an improved variant of the Dijkstra algorithm to generate the secure route for WSNs in the presence of malicious nodes. Compared with the Reputation-Based Mechanism to Stimulate Cooperation (RBMSC) model in the same simulation environment, the proposed model can maintain a higher delivery ratio, which verifies the effectiveness of the proposed model on the basis of global optimization. Furthermore, compared with the traditional Dijkstra algorithm, the packet loss ratio in the improved Dijkstra algorithm is lower because it can more effectively avoid malicious nodes, thus verifying the effectiveness of the improved algorithm.

A vehicle path planning method based on a dynamic traffic network that considers fuel consumption and emissions

The rapidly increasing and widespread use of vehicles has intensified fuel consumption and environmental pollution. Big data on urban dynamic traffic flow can be used to improve the economics and environmental impact of vehicle travel by effectively reducing fuel usage and pollution. In this study, a fuel consumption and emissions measurement model of vehicles coupled with a dynamic traffic network were established based on a large dataset of real-world vehicle experiments. This study improved upon the traditional Dijkstra algorithm used for path planning and then, the improved algorithm was combined with a vehicle fuel consumption and emissions measurement model. An optimal path simulation analysis was performed in MATLAB based on road networks generated by ArcGIS and different optimization targets were assessed including the shortest time, shortest distance, least fuel consumption, and lowest emissions. The results show that factors such as the road type and traffic environment at intersections can greatly affect fuel consumption and emissions. Large differences in path planning results were observed depending on the optimization target. The proposed economic and environmental protection model for vehicle path planning based on a dynamic traffic network can effectively reduce fuel consumption and emissions during travel, thus, providing a new method to improve urban environmental pollution in China.

Dijkstra algorithm for shortest path problem under interval-valued Pythagorean fuzzy environment

Pythagorean fuzzy set as an extension of fuzzy set has been presented to handle the uncertainty in real-world decision-making problems. In this work, we formulate a shortest path (SP) problem in an interval-valued Pythagorean fuzzy environment. Here, the costs related to arcs are taken in the form of interval-valued Pythagorean fuzzy numbers (IVPFNs). The main contributions of this paper are fourfold: (1) the interval-valued Pythagorean fuzzy optimality conditions in directed networks are described to design of solution algorithm. (2) To do this, an improved score function is used to compare the costs between different paths with their arc costs represented by IVPFNs. (3) Based on these optimality conditions and the improved score function, the traditional Dijkstra algorithm is extended to find the cost of interval-valued Pythagorean fuzzy SP (IVPFSP) and corresponding IVPFSP. (4) Finally, a small sized telecommunication network is provided to illustrate the potential application of the proposed method.

Optimization of dangerous goods transport in urban zone

Due to the specificity of the transport of dangerous goods, as well as the obligations arising from the legislation regulating this field, all the actors of this process are obliged to take special measures in order to avoid undesired consequences. Special attention is paid to the planning of the transport of dangerous goods. One of the most important planning elements is choosing a route for the transport of dangerous goods in urban areas. In order to take preventive measures, risk assessment is carried out on the routes and the minimum risk route is defined. In this paper, a new model for selection of the routes for the transport of dangerous goods (hazmat) on the network of urban roads is proposed. The model is based on a multi-criteria risk analysis and the traditional Dijkstra algorithm (D-R model). The D-R model is a new approach for minimizing the cost and a variety of risk criteria in hazmat routing, which adequately takes into account and minimizes a number of risks on potential routes. The model is based on route selection based on the absolute risk size. The proposed routing model was tested in a real case and in a real urban hazmat routing problem, in Serbia.

A GPU-based solution for fast calculation of the betweenness centrality in large weighted networks

Betweenness, a widely employed centrality measure in network science, is a decent proxy for investigating network loads and rankings. However, its extremely high computational cost greatly hinders its applicability in large networks. Although several parallel algorithms have been presented to reduce its calculation cost for unweighted networks, a fast solution for weighted networks, which are commonly encountered in many realistic applications, is still lacking. In this study, we develop an efficient parallel GPU-based approach to boost the calculation of the betweenness centrality (BC) for large weighted networks. We parallelize the traditional Dijkstra algorithm by selecting more than one frontier vertex each time and then inspecting the frontier vertices simultaneously. By combining the parallel SSSP algorithm with the parallel BC framework, our GPU-based betweenness algorithm achieves much better performance than its CPU counterparts. Moreover, to further improve performance, we integrate the work-efficient strategy, and to address the load-imbalance problem, we introduce a warp-centric technique, which assigns many threads rather than one to a single frontier vertex. Experiments on both realistic and synthetic networks demonstrate the efficiency of our solution, which achieves 2.9× to 8.44× speedups over the parallel CPU implementation. Our algorithm is open-source and free to the community; it is publicly available throughhttps://dx.doi.org/10.6084/m9.figshare.4542405. Considering the pervasive deployment and declining price of GPUs in personal computers and servers, our solution will offer unprecedented opportunities for exploring betweenness-related problems and will motivate follow-up efforts in network science.

Study on Search Algorithm of Passenger Travel Route in Railway Transport Network

Study on search algorithm of passenger travel route in railway transport network is of great importance in evaluation on railway operation benefit. Based on features of large-scale network and railway passenger transportation organization, the paper firstly innovatively puts forward the generation method of alternative route set in multiple constraint conditions. K short routes is proposed based on layering network to generate the alternative route set of passenger flow and hybrid genetic algorithm to generate the alternative route set of passenger flow in format of origindestination matrix in order to overcome the shortage of traditional Dijkstra algorithm. Then reasonable routes of passenger flow are selected from the alternative route set. Finally, the algorithm by MATLAB programming can search reasonable route set of 589 passenger flows within 2 minutes in the railway network constituted by 521 stations which verifies the convergence of the algorithm.

COMPARISON OF DIJKSTRA ALGORITHM WITH ANT COLONY OPTIMIZATION ALGORITHM USING RANDOM TOPOLOGY IN ALL-OPTICAL NETWORK USING RWA

All-optical networks are gaining higher and higher importance in the transmission of multimedia data. Routing and wavelength assignment is the critical issue that arises in optical networks across big infrastructures in corporate software industry. The randomness of data signals using optical networks can address specific issues to wit, blocking probability, one among the QoS parameters (which is to be reduced). Optimization techniques can be invariably applied in this scenario wherein Ant Colony Optimization (ACO) is one such method that proved very effective in solving the blocking probability issue. Routing and wavelength assignment (RWA) provides an important solution in optical networks. In this paper a comparison has been done between the results of ACO and traditional Dijkstra algorithm, in which ACO algorithm shows better performance in terms of blocking probability. Simulation results show that ACO algorithm has very low blocking probability compared with the traditional Dijkstra algorithm when the traffic load is small.