Improved Dijkstra Algorithm Research Articles

Optimizing Dijkstra's Algorithm for Managing Urban Traffic Using Simulation of Urban Mobility (Sumo) Software

Among the challenges of urbanization is traffic management as a measure of growth and progress. Recent population growth has resulted in a significant increase in vehicles, causing traffic jams that are challenging for the existing transportation networks. This congestion affects other services, including public transit, airports, road maintenance, and pollution caused by emissions of CO2 and other gases. Furthermore, it doubles the amount of fuel used. This has negative consequences for society as well as economic losses. This paper focuses on an improved Dijkstra algorithm based on traffic congestion levels to address the above problems. Improved Dijkstra algorithm can provide (a) real data collected from the map via OpenStreetMap, (b) Add four features to SUMO(Simulation of Urban Mobility) simulator software (time period, rush-hour, number of vehicles, and routing algorithm), (c) it could know congestion level for roads (d) rerouting vehicles to avoid traffic congestion. Based on the simulation results and analysis presented in the paper, it was found that the proposed improved Dijkstra algorithm increased the performance of the road traffic flow by reducing the number of related vehicles in traffic congestion and average delay time for experiment scenarios.

System reliability evaluation of 12-pulse series converter station based on improved Dijkstra algorithm

System reliability evaluation of 12-pulse series converter station based on improved Dijkstra algorithm

A Heuristic Integrated Scheduling Algorithm Based on Improved Dijkstra Algorithm

In the process of the integrated scheduling of multi-variety and small-batch complex products, the process structure and attribute characteristics are often ignored, which affects the overall scheduling effect. Aiming at solving this problem, a heuristic integrated scheduling algorithm (HIS-IDA) based on the improved Dijkstra algorithm is proposed. The algorithm takes the processing time of the process itself as the path value of the preceding and the following adjacent processes. Firstly, the improved Dijkstra algorithm prioritized the scheduling of the process sequence with long longitudinal paths and realized the “longitudinal optimization” of the integrated scheduling. Secondly, the layer priority strategy is used to shorten the interval time of process processing and realize the “horizontal optimization” of integrated scheduling. On the basis of “vertical and horizontal optimization”, the idle time of the equipment is further reduced by using the process priority strategy of the leaf node, and the “idle optimization” of the integrated scheduling is realized, so as to optimize the overall effect of the integrated scheduling. The effectiveness and superiority of the algorithm are proved using comparison analysis.

Collaborative path planning and task allocation for multiple agricultural machines

Path planning and task allocation are critical concerns in multi-machine collaborative operations for unmanned farms. Nevertheless, several problems remain in the operation of agricultural machinery, such as the slow path planning algorithm, the omission of the working area, and the unreasonable scheduling of machines, resulting in low efficiency and wasted resources. Collaborative and complete coverage path planning was achieved to solve the problems of slow path planning algorithms and the omission of working areas. The farm’s electronic map was constructed using the topological map method. The improved Dijkstra algorithm based on priority queues was combined with three different complete coverage methods: the nested method, the reciprocating method, and the combination of nested and internal spiral path methods. The simulation results show that the improved Dijkstra method based on priority queues can effectively minimize the running time of the algorithm. The reciprocating method has a higher coverage index than the other two methods, with an average coverage rate of 94.73 %. To solve the problem of illogical scheduling of the same type of agricultural machines, an improved ant colony method was presented based on the whole working path to minimize the path cost. The simulation results show that the proposed method can allocate the task properly, and the path cost is reduced by 14 %–33 %. By combining the proposed path planning and task allocation methods, the whole-process path planning of a single agricultural machine and multiple agricultural machines of the same type was achieved, providing a technical solution for promoting the construction of unmanned farms.

Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm

With the upgrading of manufacturing production lines and innovations in information technology, logistics robot technology applied in factories is maturing. Rail-mounted logistics robots are suitable for precise material distribution in large production workshops with fixed routes and over long distances. However, designing an efficient path-planning algorithm is the key to realizing high efficiency in multi-robot system operations with rail logistics. Therefore, this paper proposes an improved Dijkstra algorithm that introduces real-time node occupancy and a time window conflict judgment model for global path planning and conflict coordination in multi-robot systems. More specifically, the introduction of real-time node occupancy can determine the shortest feasible routes for each task, and the introduction of the time window conflict judgment model can avoid the route conflict problem in the execution of multiple tasks, planning the shortest route without conflict. For the robot UBW positioning module, a Chan algorithm based on TDOA is proposed to realize the accurate positioning of rail-mounted logistics robots during their operation. Compared with the traditional Dijkstra algorithm, the results show that the algorithm proposed herein can plan a conflict-free and better path and dynamically adjust the on-orbit conflict in real time to avoid track congestion and efficiently complete multiple distribution tasks.

Optimization of computer-assisted teaching mode in universities based on Dijkstra’s algorithm

Abstract In the context of digitalizing big data and information, optimizing the teaching model of colleges and universities is a key concern in the education sector. This paper optimizes the shortest path planning of the Dijkstra algorithm around the Floyd algorithm and ant colony algorithm, constructs the model of a computer-aided teaching system in colleges and universities through the Dijkstra algorithm, and constructs the system model indexes from three aspects: students’ cognitive ability, students’ learning performance, and students’ learning interest. A sample of 100 college students was randomly selected, and the accuracy performance of the samples was compared by the Dijkstra algorithm and improved Dijkstra, respectively. The accuracy rate of the improved Dijkstra algorithm fluctuated from 83% to 90%, with a variable rate of 7%, which was relatively small. Then the Dijkstra algorithm was used to analyze the weighting of the cognitive ability index, learning achievement index, and learning interest index. In the cognitive ability index, the memory ability weight of 34% performed better compared with the other five, the good weight in the learning achievement index performed better compared with the other categories, and the more interesting weight in the learning interest index performed better overall compared with the other five categories. This study has guiding reference value for optimizing computer-assisted teaching mode in colleges and universities to improve teaching quality and efficiency of colleges and universities.

Transmission Line-Planning Method Based on Adaptive Resolution Grid and Improved Dijkstra Algorithm.

An improved Dijkstra algorithm based on adaptive resolution grid (ARG) is proposed to assist manual transmission line planning, shorten the construction period and achieve lower cost and higher efficiency of line selection. Firstly, the semantic segmentation network is used to change the remote sensing image into a ground object-identification image and the grayscale image of the ground object-identification image is rasterized. The ARG map model is introduced to greatly reduce the number of redundant grids, which can effectively reduce the time required to traverse the grids. Then, the Dijkstra algorithm is combined with the ARG and the neighborhood structure of the grid is a multi-center neighborhood. An improved method of bidirectional search mechanism based on ARG and inflection point-correction is adopted to greatly increase the running speed. The inflection point-correction reduces the number of inflection points and reduces the cost. Finally, according to the results of the search, the lowest-cost transmission line is determined. The experimental results show that this method aids manual planning by providing a route for reference, improving planning efficiency while shortening the duration, and reducing the time spent on algorithm debugging. Compared with the comparison algorithm, this method is faster in running speed and better in cost saving and has a broader application prospect.

Data collection system with signal optimal-routine for the mining and environmental monitoring in Vietnam

Purpose. To develop a data acquisition which is optimized for suitable purposes in monitoring/warning or decision-making assistance in the field of geography accident in mining activities or agroforestry. Methodology. In activities related to mining, agroforestry and environmental monitoring, many climate factors must be taken into account including humidity, soil moisture or level of rainfall. Those data play a key role in pre-warning or assisting in decision-making for operators who are responsible for risk warning. In this paper, we present an optimized data acquisition system which is reasonable in cost, simple and easy in installation. The system includes a sensor station (SS) and a central station to collect data from specified monitoring points. The latter one is used to gather data from SS through a new and optimized Lora WAN communication system. When sending gathered data to a cloud sever, an assistant system based on 3G module is established to warn of abnormal scenarios of the mining procedure or environmental parameters. The system is experimented, tested and implemented in the North Mountain area (Northwest) of Vietnam. Findings. A development of an acquisition system with optimized-data for alarming/warning and monitoring in the field of geography accident, mining activities and environment. The system could get sensing signal in both direct way and indirect way despite of bad weather. Originality. An improved Dijkstra algorithm is implemented to optimize and simulate the routing paths of a network. The optimization could show the best way for getting signal from sensor station indirectly to other ones, then to the central station. Practical value. A simple, reasonable-cost and easy-installation system is formed for monitoring, risk warning in the field of geography, mining, and climate.

Разработка программы построения схемы прокладки кабельных трасс на основе теории графов

This article analyzes the existing software applications used in the construction of schemes for laying cable routes. It is shown that such applications provide little functionality when optimizing route laying. In this study, we set out to create a software module based on an improved Dijkstra algorithm, which can be used in combination with the existing software. The developed module is capable of finding the shortest path when laying cable routes, taking into account logical and physical obstacles. The software module is implemented in Java and uses the concurrency module to parallelize calculations, thereby speeding up the process of calculating the optimal path.

Dynamic Path Planning Analysis of Warehouse Handling Robot

In order to further improve the operational efficiency and the flexibility of automated warehouse, a path planning algorithm for a warehouse handling robot was evaluated. Under the background of determining the operation scene and environment of multihandling robot, a path planning algorithm with time window is proposed for multihandling robot, and a two-stage path planning method is also analyzed. The results show that the route planning using the improved Dijkstra algorithm saves 7.16 meters compared with the traditional algorithm, and the task is completed 3.17 seconds earlier. Comprehensively considering the heat of the various paths to turn to the required time, using the improved Dijkstra algorithm path, handling the robot’s total running distance than the traditional algorithm saves 14.45 meters, the total run time saves 8.45 seconds, and whether in time or in the distance, crane robot handling efficiency compared with the traditional algorithm has an obvious increase, closer to reality.

Three‐stage improved algorithm based on clustering decomposition and its application in drone demand and task allocation

This paper proposes a three-stage algorithm based on clustering decomposition and task allocation—improved clustering planning algorithm (iK-iD-N), aiming at the optimization task allocation problem of drones in actual application to meet the task demand constraints. The algorithm solves the problem of the number of drones demanded and the initial delivery range of each drone by introducing dual-objective planning into the clustering decomposition. Combining improved Dijkstra algorithm (iK-D) with neighbourhood insertion algorithm into task allocation, to get high-quality solutions and solve efficiently. Compared with the existing ant colony algorithm, the iK-iD-N algorithm proposed in this paper is more efficient and can obtain the best and stable solutions while evenly distributing tasks. Then it is compared with the improved clustering algorithm combined with the basic iK-D to get better solutions of the iK-iD-N algorithm at any time, and compared with the basic clustering algorithm with the improved task allocation algorithm (K-iD-N) that iK- iD-N can get a better solution with high probability. The thesis also simulates and analyzes the impact of uncertainty requirements on the solutions based on drone demand and task allocation models, and discusses the impact of drone load capability and endurance capability constraints on the final solutions.

Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance

Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots (MRs) in both research and education. In this paper, an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra algorithm. To achieve this research objectives, first, the MR obstacle-free environment is modeled as a diagraph including nodes, edges and weights. Second, Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target point. During its movement, the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following node. For this aim, the MR was equipped with an ultrasonic sensor used as obstacle detector. If an obstacle is found, the MR updates its diagraph by excluding the corresponding node. Then, Dijkstra algorithm runs on the modified diagraph. This procedure is repeated until reaching the target point. To verify the efficiency of the proposed approach, a simulation was carried out on a hand-made MR and an environment including 9 nodes, 19 edges and 2 obstacles. The obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking sensors. This study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.

Substation Secondary Cable Path Optimization Design Based on 3D Simulation and Improved Dijkstra Algorithm

In order to accurately predict the length of secondary cable laying in substation construction project and avoid the problem of resource waste in engineering construction, a secondary cable path optimization model based on 3D simulation and Dijkstra algorithm was established. Firstly, through the application of Bentley software, the three-dimensional model of substation is established, and the workflow of BRCM cable laying software is introduced. Secondly, the common and the improved Dijkstra algorithm are used to carry out the path planning on the raster map respectively, and the experimental simulation is carried out. Finally, the simulation results show that the path length of the improved algorithm is 24.32% and 28.66% less than that of the traditional Dijkstra. It is 12.34% less than that of the ant colony algorithm, which provides some useful theoretical references for the secondary cable laying in the future substations.

Combined stochastic and discrete simulation to optimise the economics of mixed single-horizontal and multilateral well offshore oil developments

Multilateral wells promise cost savings to oil and fields as they have the potential to reduce overall drilling distances and minimize the number of slots required for the surface facility managing the well. However, drilling a multilateral well does not always increase the flow rate when compared to two single-horizontal wells due to competition in production inside the mother-bore. Here, a holistic approach is proposed to find the optimum balance between single and multilateral wells in an offshore oil development. In so doing, the integrated approach finds the highest Net Present Value (NPV) configuration of the field considering drilling, subsurface, production and financial analysis. The model employs stochastic perturbation and Markov Chain Monte-Carlo methods to solve the global maximising-NPV problem. In addition, a combination of Mixed-Integer Linear Programming (MILP), an improved Dijkstra algorithm and a Levenberg-Marquardt optimiser is proposed to solve the rate allocation problem. With the outcome from this analysis, the model suggests the optimum development including number of multilateral and single horizontal wells that would result in the highest NPV. The results demonstrate the potential for modelling to find the optimal use of petroleum facilities and to assist with planning and decision making.

A Multi-Zone Staged Indoor Emergency Evacuation Algorithm Based on Time Equalization

Most of the existing staged evacuation algorithms only consider the impact of crowd density on evacuation partition, but do not take into account the influence of the spatial distribution of occupants and the capacity of exits on the total evacuation time (TET). Therefore, a novel indoor emergency evacuation algorithm based on time equalization is proposed in this paper. All factors affecting TET such as the position and size of each group and the capacity of exits are fully considered in the proposed algorithm, which are uniformly converted into the occupancy time of each exit. An improved Dijkstra algorithm is used to generate evacuation zones according to the proximity relationship and the occupancy time of different exits. The strategy of waiting at the starting point is adopted to ensure that all evacuees are free from congestion during the escape process. In addition, the method of group merging is proposed to further increase the balance among all zones during the partitioning process. The objectives of the proposed algorithm include minimizing the TET of all evacuees, the path length of each escape group, avoiding congestion during the escape process. The experimental results show that the proposed algorithm effectively reduces TET and the path length of groups compared with existing algorithms, which improves the efficiency of evacuation and utilization of all exits and can be applied to the various distribution and density of evacuees.

A double-layer isolation mechanism for malicious nodes in wireless sensor networks

Isolation plays an important role in the security of wireless sensor networks. Existing isolation models for wireless sensor networks possess excessive energy consumption. We propose a double-layer isolation mechanism based on an improved Dijkstra algorithm. The number of “optimal” nodes is obtained based on the calculation formula. Two groups of “optimal” nodes are determined by the arrival time difference ranging algorithm and the improved Dijkstra algorithm. The first layer isolation and the second layer isolation are constructed by the obtained two groups of “optimal” nodes, respectively. According to the status of receiving and sending data, the “optimal” routing protocol and working mode are selected. Based on the cooperation between two layers of isolation, intruded nodes are isolated from a wireless sensor network. The intrusion source is detected by monitoring the intruded nodes. Simulation results show that the proposed isolation mechanism can effectively reduce the energy consumption of wireless sensor nodes. High transmission efficiency, rapid response, and enhanced security are provided for wireless sensor networks.

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.

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.

An Improved Acceleration Method Based on Multi-Agent System for AGVs Conflict-Free Path Planning in Automated Terminals

Aiming at the problem that the increasing number of automated guided vehicles (AGVs) will lead to more frequent conflicts between AGVs. In this paper, a conflict-free path planning model for multi-AGV is established, aiming to minimize the blocking rate of AGVs between the quay crane and the yard crane, considering the travel speed, operation time, and conflict distance of AGVs. An architecture of AGV’s system based on Multi-Agent System (MAS) is designed, the improved interactive protocol based on blackboard model is used as the communication method of AGV, the improved acceleration control method is combined with the AGV priority determination method based on time cost as the negotiation strategy of AGV, the improved Dijkstra algorithm calculates the conflict-free path of each AGV. By comparing the acceleration control method based on MAS with the speed control method based on MAS and the task priority control method, the effectiveness of this method for solving multiple AGVs conflict-free path planning in automated terminals is verified.

Qualitative Analysis of Hybrid Flow Installation Mechanism in Software Defined Networks (SDN)

Software Define Networking (SDN) is a new technology and in recent years it has been developed in an amazing amount. This paper addresses Dijkstra routing algorithm for routing SDN traffic and flow installation mechanism in OvS switches. Programmable networking is the main base of SDN which helps in dynamic network management because a dynamic networking is only possible when the data and control planes are separate. In SDN the control plane resides in SDN Controller, which has the responsibility of routing the traffic and installation of flows on data plane. Whenever a flow enters into a SDN switch, flow tables are generated for those flows and installed at data plane from control plane. These flows have their own properties because of their relevant protocols which are attached to them. Dijkstra is one those protocols which provides the shortest path to the packet. A flow consists of a matching pattern, actions, and counter. Based on the respective flow, data communication takes place. This project investigates three kinds of flow installation mechanisms, reactive, proactive, hybrid and also provides an extension of Dijkstra algorithm in which Dijkstra algorithm also consider the amount of traffic on a link to avoid congestion while selecting the path for a packet. Based on these three flow installation mechanisms and improved Dijkstra algorithm we have analyzed the performance of SDN by using different performance metrics. We have used Mininet and POX controller to evaluate the performance analysis in Ubuntu and Windows-based environments. Finally, we have drawn conclusions.