Shortest Path Faster Algorithm Research Articles

Research on the Intelligent Planning of Mine Fire Evacuation Routes Based on a Multifactor Coupling Analysis

Efficient evacuation route planning during underground coal mine fires is essential to minimize casualties. This study addresses current shortcomings by proposing a real-time method that integrates a multifactor coupling analysis and the optimized multilayer perceptron regressor-shortest path faster algorithm (MSPFA). This research aims to enhance evacuation route planning by overcoming factors such as inadequate consideration, low accuracy, and information lag in existing methods. This study improves the shortest path faster algorithm (SPFA) for dynamic route planning, mitigates the impact of fixed walking speed parameters using the particle swarm algorithm, and selects the optimal model (MLPRegressor) through the Bootstrap algorithm for estimating personnel walking speeds. Validated through smoke-spread experiments, the MSPFA algorithm dynamically adjusts evacuation routes, preventing toxic passages. Visualization via drawing interchange format (DXF) successfully enhances route comprehension. The MSPFA algorithm outperforms the Dijkstra algorithm with a runtime of 78.5 msand a personnel evacuation time of 3344.74 s. This research establishes a theoretical foundation for intelligent evacuation decision making in underground fire disasters. By introducing the MSPFA algorithm, it provides crucial technical support, significantly reducing the risk of casualties during emergencies.

An Optimised Shortest Path Algorithm for Network Rotuting & SDN

Network routing algorithms form the backbone of data transmission in modern network architectures, with implications for efficiency, speed, and reliability. This research aims to critically investigate and compare three prominent routing algorithms: Bellman-Ford, Shortest Path Faster Algorithm (SPFA), and our novel improved variant of Bellman-Ford, the Space-efficient Cost-Balancing Bellman-Ford (SCBF). We evaluate the performance of these algorithms in terms of time and space complexity, memory utilization, and routing efficacy, within a simulated network environment. Our results indicate that while Bellman-Ford provides consistent performance, both SPFA and SCBF present improvements in specific scenarios with the SCBF showing notable enhancements in space efficiency. The innovative SCBF algorithm provides competitive performance and greater space efficiency, potentially making it a valuable contribution to the development of network routing protocols. Further research is encouraged to optimize and evaluate these algorithms in real-world network conditions. This study underscores the continuous need for algorithmic innovation in response to evolving network demands.

Hierarchical eco-driving control for plug-in hybrid electric vehicles under multiple signalized intersection scenarios

With the development of vehicle-to-everything (V2X) and autonomous driving technologies, plug-in hybrid electric vehicles (PHEVs) enable to absorb surrounding information to enhance economic driving. This study proposes a dynamic inverse hierarchical optimization method, which incorporates traffic-signal phase and timing as well as road data, to plan economic velocity and improve PHEV energy consumption. The hierarchical control framework determines the desired speed and arrival time in the upper layer using the shortest path faster algorithm. The lower level accounts for multi-objective velocity planning based on particle swarm optimization and Pareto theory. The inverse layering method solves the economic velocity optimization problem. With the support of V2X and autonomous driving technologies, the method enhances energy efficiency and computational efficiency in PHEVs through dynamic inverse hierarchical optimization. Simulation results highlight that the proposed algorithm leads to 7.43% improvement in energy consumption economy and the reduced calculation time, compared to the existing solutions. The hardware-in-the-loop experiments validate the real-time applicability of the proposed algorithm.

Retinal cross-section motion correction in three-dimensional retinal optical coherence tomography.

Motion correction is an important issue in ophthalmic optical coherence tomography (OCT), and can improve the ability of data sets to reflect the physiological structures of tissues and make visualization and subsequent analysis easier. In this study, we present a novel method to correct the cross-sectional motion artifacts in retinal OCT volumes. Motion along the x-direction (fast-scan direction) is corrected through the normalized cross-correlation algorithm, while axial motion compensation is performed using the polynomial fitting method on the inner segment/outer segment (IS/OS) layer segmented by the shortest path faster algorithm (SPFA). The results of volunteers with central serous chorioretinopathy demonstrate that the proposed method effectively corrects motion artifacts in OCT volumes and may have potential application value in the evaluation of ophthalmic diseases such as diabetic retinopathy, glaucoma and age-related macular degeneration.

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.

Path Planning in Multiple-AUV Systems for Difficult Target Traveling Missions: A Hybrid Metaheuristic Approach

Multiple autonomous underwater vehicles (AUVs) are popular for challenging submarine missions. In this article, we focus on the multi-AUV path planning for a common class of missions that need to traverse lots of mission targets in large and complex environments. Given that AUVs are often launched from a movable surface vehicle, e.g., a ship, a multi-AUV target traveling problem is formulated with a requirement of surface point location. Thereafter, a hybrid metaheuristic approach is developed by sequentially performing a cube-based environment modeling, cost map building, voyage planning, and detailed trajectory planning. Specifically, the shortest path faster algorithm (SPFA) is adopted to build a cost map among targets and candidate surface points, and the A* search is utilized for trajectory planning. The optimality of both SPFA and A* can indeed be guaranteed. Thus, voyage planning becomes critical and an algorithm namely DE-C-ACO is proposed by combining the ant colony optimization (ACO) and the differential evolution (DE) with a cluster-based adjustment strategy, i.e., DE-C. DE-C and ACO evolve in parallel for surface point location and voyage generation, respectively. Experiments based on realistic bathymetries are conducted and the results validate the effectiveness and efficiency of the proposed DE-C-ACO.

Solving a Large-Scale Multi-Depot Vehicle Scheduling Problem in Urban Bus Systems

This study proposes an improved model and algorithm for the large-scale multi-depot vehicle scheduling problem (MDVSP) with departure-duration restrictions. In this study, the time-space network is applied to model the large-scale MDVSP. Considering that crews usually change shifts in the depot, departure-duration restrictions are added to the classic set-partitioning model to ensure that buses return to the depot when crews reach their working time limits. By embedding a preliminary exploring tactic to the shortest path faster algorithm (SPFA), researchers developed an improved large neighborhood search (LNS) algorithm to solve large-scale instances of MDVSP with departure-duration restrictions. The proposed methodology is applied to a real-life case in China and several test instances. The results show that the improved LNS algorithm can achieve very good performance in computational efficiency without deteriorating solution quality, which is important for large-scale systems. More specifically, the total cost of the improved LNS algorithm is approximately equal to branch-and-price, but the computational time is much shorter in the case study. For test instances with different number of timetabled trips (500, 1000, 1500, and 2000), the Quality Gap (QG) is very small, approximately 0.35%, 0.38%, 0.63%, and 0.93%, while the Efficiency Ratio (ER) reaches up to 2.89, 2.98, 3.65, and 3.79, respectively.

Efficient vehicles path planning algorithm based on taxi GPS big data

Abstract The driving thinking of taxi drivers is always hidden in a large amount of taxis GPS data. An efficient driving stratagem derived from taxi drivers is provided for private car drivers. The five million pieces of taxis GPS data in Nanjing, China are analyzed: firstly, the data preprocessing is conducted for the reduction measuring error of GPS data with the expurgation of the static point, the drifting point, and the relatively independent point; then, the road intersections through the regional extreme points are found to restore map with the following three algorithms: the path selection algorithm based on probability, the improved Prim path selection algorithm, and the improved Prim path selection algorithm based on probability; at last, the SPFA (Shortest Path Faster Algorithm) is applied to the measurement of the road map gained from the previous three algorithms for optimal path planning with 40 pairs of starting points and termination points, and making a comparison of the road length among three methods. Through the experimental comparison, the third method namely the improved Prim path selection algorithm based on probability which proved to be more optimal than others two methods produces an efficient driving route more accurately.

Dynamic shortest path association for multiple object tracking in video sequence

Persistently tracking multiple objects in cluttered environments is very challenging. We present a tracking association approach based on the shortest path faster algorithm. We first formulate the multiple object tracking as an integer programming problem of the flow network. Under this framework, the integer assumption is relaxed to a standard linear programming problem. Therefore, the global optimal solution can quickly be obtained using the fast dynamic shortest path algorithm, which highlights the dynamic programming characteristic of the shortest path, thus faster, algorithm. The proposed method avoids the difficulties of integer programming; more importantly, it has a lower worst-case complexity than competing methods but a better tracking accuracy and robustness in complex environments. Simulation results show that our proposed algorithm takes less time than other methods and can operate in real time.

Multiple object tracking using the shortest path faster association algorithm.

To solve the persistently multiple object tracking in cluttered environments, this paper presents a novel tracking association approach based on the shortest path faster algorithm. First, the multiple object tracking is formulated as an integer programming problem of the flow network. Then we relax the integer programming to a standard linear programming problem. Therefore, the global optimum can be quickly obtained using the shortest path faster algorithm. The proposed method avoids the difficulties of integer programming, and it has a lower worst-case complexity than competing methods but better robustness and tracking accuracy in complex environments. Simulation results show that the proposed algorithm takes less time than other state-of-the-art methods and can operate in real time.

An Improved Bellman-Ford Algorithm Based on SPFA

In this paper, we proposed an efficient algorithm based on SPFA(shortest path faster algorithm), which is an improved the Bellman-Ford algorithm. The Bellman-Ford algorithm can be used on graphs with negative edge weights unlike Dijkstra's algorithm. And SPFA algorithm used a queue to store the nodes, to avoid redundancy, though the Bellman-Ford algorithm takes a long time to update the nodes table. In this improved algorithm, an adjacency list is also used to store each vertex of the graph, applying dynamic optimal approach. And a queue is used to store the data. The improved algorithm can find the optimal path by continuous relaxation operation to the new node. Simulations to compare the efficiencies for Dijkstra's algorithm, SPFA algorithm and improved Bellman-Ford were taken. The result shows that Dijkstra's algorithm, SPFA algorithm have almost same efficiency on the random graphs, the improved algorithm, although the improved algorithm is not desirable, on grid maps the proposed algorithm is very efficient. The proposed algorithm has reduced two-third times processing time than SPFA algorithm.