Breadth-first Search Algorithm Research Articles

Transcriptome analysis provides new insights into the berry size in ‘Summer Black’ grapes under a two-crop-a-year cultivation system

Southern China has high temperatures and receives concentrated rainfall; therefore, the two-crop-a-year cultivation system has been applied to grape production so as to resolve the problem of relative seasonal surplus of grape yield. However, a common issue associated with this technique is the tendency of the second season fruits to be smaller than the first season fruits. We here used the first and second season fruits of ‘Summer Black’ at different ripening stages as research materials. Phenotypic and histological analyses revealed fewer cell number occurring between 7 and 14 days post anthesis (DPA) in the second season fruits, which ultimately resulted in a smaller fruit size compared with the first season fruits. To unravel the mechanism underlying this phenomenon, first and second season fruits of four time periods (7, 14, 21, and 28 DPA) were selected for RNA-seq analysis. This analysis identified 10 431 differentially expressed genes (DEGs). These DEGs were classified into 9 clusters through GO and KEGG enrichment analyses. Then the time-ordered gene co-expression network (TO-GCN) analysis with the breadth-first search algorithm showed that DEGs in the GCN were divided into 8 levels. The DEGs of early berry development (L1–L3) were enriched in heat stress- and cell division-related pathways. The field investigation of effective accumulated temperature confirmed that the growth and development of the second season fruits were subjected to high temperature stress during 7–14 DPA. Moreover, based on the results of interactive analysis of TO-GCN and transcriptional regulation prediction of L1–L3 genes, we constructed a unique hierarchical regulatory network for the heat stress regulation of berry size. The expression level of 5 candidate genes was verified through qRT-PCR. Vitvi10g00469 (HSFB2A), Vitvi16g00982 (HSFB2A), Vitvi02g00387 (HSFB2B), Vitvi15g01542 (NTL9), and Vitvi06g00592 (DIVARICATA) were upregulated in 7–14 DPA, whereas Vitvi18g00777 (HSFB4) was downregulated in 7 DPA. These results suggest that during intense cell division, heat stress might act as a major factor causing a reduction in cell number, thereby ultimately resulting in the smaller size of the second season fruits.

Path Planning Algorithms for Smart Parking: Review and Prospects

Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively describes the principles and steps of four types of path planning algorithms: the Dijkstra algorithm (including its optimized derivatives), the A* algorithm (including its optimized derivatives), the RRT (Rapidly exploring Random Trees) algorithm (including its optimized derivatives), and the BFS (Breadth First Search) algorithm. Secondly, the Dijkstra algorithm, the A* algorithm, the BFS algorithm, and the Dynamic Weighted A* algorithm were utilized to plan the paths required for the process of smart parking. During the analysis, it was found that the Dijkstra algorithm had the drawbacks of planning circuitous paths and taking too much time in the path planning for smart parking. Although the traditional A* algorithm based on the Dijkstra algorithm had greatly reduced the planning time, the effect of path planning was still unsatisfactory. The BFS (Breadth First Search) algorithm had the shortest planning time among the four algorithms, but the paths it plans were unstable and not optimal. The Dynamic Weighted A* algorithm could achieve better path planning results, and with adjustments to the weight values, this algorithm had excellent adaptability. This review provides a reference for further research on path planning algorithms in the process of smart parking.

The influence of Distributed BFS algorithms in reducing the computational complexity

Breadth-first search (BFS) is a cornerstone of algorithmic algorithms for complex graphs. In the worst case, BFS's linear complexity is determined by the number of edges and vertices, yet a recently discovered bottom-up method can reduce this complexity to the number of vertices in the best-case scenario, which is usually much less than the number of edges. The conventional top-down approach, however, always takes the same amount of time as the lowest case. To determine the direction, directional algorithms combine top-down and bottom-up methods, adapting between them as the boundary expands. Although bottom-up methods are not always the best option, their use in combination with top-down methods produces efficient algorithms. The main focus of this research is to improve search efficiency and reduce complexity by implementing a version of top-down algorithm optimisation. A scalable distributed-memory parallelisation technique is presented in this study, which has achieved speedups that are ten times faster than the previous purely toplevel approach. In previous studies, it has been demonstrated that this technique is superior to a 1D decomposition method.

Optimizing the Implementation of the BFS and DFS algorithms using the web crawler method on the kumparan site

Efficient access to timely information is critical in today's digital era. Web crawlers, automated programs that navigate the Internet, play an important role in collecting data from websites such as Kumparan, a leading news site in Indonesia. This research shows the effectiveness of the Breadth-First Search (BFS) and Depth-First Search (DFS) algorithms in indexing Kumparan content. The results of the research show that BFS consistently indexes more files comprehensively but with longer execution times compared to DFS, which provides faster initial results but with fewer files. For example, at depth 4 BFS indexed 949 files in 886.94 seconds, while DFS indexed 470 files in 233.02 seconds. These findings highlight the balance between precision and speed when selecting a crawling algorithm tailored to the needs of a particular website. This research provides insights into optimizing web crawler technology for complex websites such as Coil and suggests avenues for further research to improve permission efficiency and adaptability across a variety of crawling scenarios.

Air volume reconstruction and sensor optimization distribution in building intelligent ventilation network

Ensuring the accuracy and reliability of ventilation parameter monitoring is pivotal for the development of intelligent ventilation systems. To attain a visual representation of airflow, solving the challenge of airflow reconstruction necessitates the strategic use of a limited number of sensors. In addressing these concerns, this article introduces an optimization approach for ventilation airflow leveraging the Breadth-First Search (BFS) algorithm. Additionally, it proposes an optimization distribution method for mine ventilation sensors, grounded in the Independent Cut Set algorithm. Research has found that compared to the traditional PSO algorithm, the BFS algorithm produces a higher optimal air volume solution when optimizing the air volume; Comparatively, the proposed algorithm exhibits significantly shorter average running times than the Particle Swarm Optimization (PSO) algorithm. It boasts the highest average convergence rate, ensuring superior accuracy, and possesses a notable capability to escape local minima, facilitating the acquisition of optimal solutions. Leveraging the independent cut set algorithm optimizes the calculation process through matrix operations. Exploiting the properties of matrices allows for a more rapid and intuitive resolution of sensor localization problems.

Research on Shortest Path BFS Strategy in Multi-AGV Scheduling System

With the increasing maturity of automated guided vehicles (AGV) technology and the widespread application of flexible manufacturing systems, enhancing the efficiency of AGVs in complex environments has become crucial. This paper analyzes the challenges of path planning and scheduling in multi-AGV systems, introduces a map-based path search algorithm, and proposes the BFS algorithm for shortest path planning. Through optimization using the breadth-first search (BFS) algorithm, efficient scheduling of multiple AGVs in complex environments is achieved. In addition, this paper validated the effectiveness of the proposed method in a production workshop experiment. The experimental results show that the BFS algorithm can quickly search for the shortest path, reduce the running time of AGVs, and significantly improve the performance of multi-AGV scheduling systems.

Performance Evaluation of Parallel Graphs Algorithms Utilizing Graphcore IPU

Recent years have been characterized by increasing interest in graph computations. This trend can be related to the large number of potential application areas. Moreover, increasing computational capabilities of modern computers allowed turning theory of graph algorithms into explorations of best methods for their actual realization. These factors, in turn, brought about ideas like creation of a hardware component dedicated to graph computation; i.e., the Graphcore Intelligent Processor Unit (IPU). Interestingly, Graphcore systems are a hardware implementation of the Bulk Synchronous Parallel paradigm, which seemed to be a mostly theoretical concept from the end of last century. In this context, the question that has to be addressed experimentally is as follows: how good are Graphcore systems in comparison with standard systems that can be used to run graph algorithms, i.e., CPUs and GPUs. To provide a partial response to this broad question, in this contribution, PageRank, Single Source Shortest Path and Breadth-First Search algorithms are used to compare the performance of IPU-deployed algorithms to other parallel architectures. Obtained results clearly show that the Graphcore IPU outperforms other devices for the studied heterogeneous algorithms and, currently, provides best-in-class execution time results for a range of graph sizes and densities.

ScalaBFS2: A High-performance BFS Accelerator on an HBM-enhanced FPGA Chip

The introduction of High Bandwidth Memory (HBM) to the FPGA chip makes it possible for an FPGA-based accelerator to leverage the huge memory bandwidth of HBM to improve its performance when implementing a specific algorithm, which is especially true for the Breadth-First Search (BFS) algorithm that demands a high bandwidth for accessing the graph data stored in memory. Different from traditional FPGA-DRAM platforms where memory bandwidth is the precious resource due to the limited DRAM channels, FPGA chips equipped with HBM have much higher memory bandwidths provided by the large quantities of HBM channels, but still a limited amount of logic (LUT, FF, and BRAM/URAM) resources. Therefore, the key to design a high-performance BFS accelerator on an HBM-enhanced FPGA chip is to efficiently use the logic resources to build as many as possible Processing Elements (PEs) and configure them flexibly to obtain as high as possible effective memory bandwidth that is useful to the algorithm from the HBM, rather than partially emphasizing the absolute memory bandwidth. To exploit as high as possible effective bandwidth from the HBM, ScalaBFS2 conducts BFS in graphs in a vertex-centric manner and proposes designs, including the independent module (HBM Reader) for memory accessing, multi-layer crossbar, and PEs that implement hybrid mode (i.e., capable of working in both push and pull modes) algorithm processing, to utilize the FPGA logic resources efficiently. Consequently, ScalaBFS2 is able to build up to 128 PEs on the XCU280 FPGA chip (produced with the 16 nm process and configured with two HBM2 stacks) of a Xilinx Alveo U280 board and achieves performance of 56.92 Giga Traversed Edges Per Second (GTEPS) by fully using its 32 HBM memory channels. Compared with the state-of-the-art graph processing system (i.e., ReGraph) built on top of the same board, ScalaBFS2 achieves 2.52x~4.40x performance speedups. Moreover, when compared with Gunrock running on an Nvidia A100 GPU that is produced with the 7 nm process and configured with five HBM2e stacks, ScalaBFS2 achieves 1.34x~2.40x speedups on absolute performance, and 7.35x~13.18x speedups on power efficiency.

Fuzzy Tree t -Spanners of Ratio Labelled Butterfly Network

Background/Objectives: The reliability and optimality of a communication system depend on the network that has a minimum distance. The minimum distance is the shortest path between the vertices. Even in error correction codes, the minimum Hamming distance ensures data integrity. Hence to have efficient routing protocols, the network is preferred to be a spanning tree that connects all the nodes of the communication system. As tree t- spanner is the optimization technique in determining minimum spanning tree, finding tree t-spanners of a ratio labelled fuzzy graph for the Butterfly network BF(n) is notion of this study. Methods: This study has introduced a new labelling called Ratio Labelling (RL) for examining the fuzziness in a crisp graph. Using the RL method, the crisp graphs were examined for the admittance of fuzziness and were thereby found, to have a tree t-spanners of ratio labelled fuzzy graphs. Findings: The quality of a network can be measured using the stretch factor of the network. The issues of network survival or link failures in a communication networks can be rectified by approximating the best flow spanner. As the optimal connectivity between the nodes is ensured by interconnection networks, the fuzzy tree t- spanners of undirected fuzzy Butterfly Network BF(n) are examined by labelling the vertices and edges using ratio labelling. The Breadth First Search (BFS) algorithm enhances the process of minimizing the spanning tree for an unweighted graph. Since the edge weightage is unique for all the edges of BF(n) under RL, searching for an edge with minimum weight by Kruskal’s algorithm or Prim’s algorithm are less meaningful. Hence, the BFS algorithm is adopted in finding the spanning tree T of Ratio Labelled BF(n). The bounds for the stretch factor t of a fuzzy tree t- spanner of ratio labelled BF(n) were obtained. Novelty: The literature survey shows that various classes of graphs were examined for the admissibility of tree t-spanners. Finding a tree t-spanner for a fuzzy graph was not found in literature, as per our knowledge. Extending the problem of finding a tree t -spanner of a ratio labelled fuzzy graph is an advancement in the field of fuzzy graphs. Keywords: Fuzzy Graph, Fuzzy distance, Ratio Labelling, Tree t- spanner, Butterfly Network

Energy-Aware Positioning Service Provisioning for Cloud-Edge-Vehicle Collaborative Network Based on DRL and Service Function Chain

In the collaborative intelligent transportation system, providing precise positioning services is costly. Reducing resource consumption and improving revenue are crucial to the development of positioning services. Therefore, a practical algorithm that combines cloud and edge network environments is necessary to improve the positioning services. Integrating network function virtualization and edge computing can provide users with more flexible and efficient services. Based on the above issues, we use the service function chain (SFC) to improve the positioning services provided in cloud-edge-vehicle collaborative networks (CEVCN). We propose a deep reinforcement learning-assisted SFC embedding algorithm and improve its performance through training. We construct a five-layer policy network to sense the environment of CEVCN and derive the optimal node selection strategy. Finally, we use the breadth-first search algorithm to solve the embedding scheme for virtual links. The simulation results show that our proposed algorithm has excellent performance. The long-term average revenue is improved by 21%, the long-term average revenue-cost ratio is improved by 13%, and the embedding rate is improved by 8%.

Formula omitted]piler: Compilers in search of compilations

Compilers pose significant challenges in their development as software products. Language developers face the complexities of ensuring efficiency, adhering to good design practices, and maintaining the overall codebase. These factors make it difficult to predict the unexpected impact of updates on existing software built on the current compiler stack. Furthermore, software created for a specific compiler often lacks reusability for other compiler environments. In this study, we propose a comprehensive framework for the uniform development of compilers that addresses these issues. Our approach involves developing compilers as a collection of small transpilation units, referred to as deltas. The transpilation infrastructure takes source code written in a particular source language and searches for a path of deltas to generate equivalent source code in the target language. By adopting this methodology, language developers can easily update their languages by introducing new deltas into the system. Existing code remains unaffected as old transpilation paths remain available. To support this framework, we have devised a metric space for efficient delta search. This metric space enables us to define a non-overestimating heuristic function, which proves valuable in solving the search problem. Leveraging the A* search algorithm, we can efficiently transpile programs from a source language to the target language. To evaluate the effectiveness of our approach, we conducted a benchmark comparison between the A* search algorithm and the simpler breadth-first search (BFS) algorithm. The benchmark consisted of over 100 transpilation searches, providing valuable insights into the performance and capabilities of this framework.

Research and Application of Power Grid Data Anomaly Tracing Method Based on Time Series Correlation

Abstract Power grid data is a “barometer” that reflects the operational trend of the power grid system, so conducting a traceability study on abnormal data generated by the power grid system is crucial to maintaining the stable operation of the power grid system and preventing further malfunctions. This article proposes a maximum time series correlation ring tracing algorithm based on time series correlation: firstly, for a large amount of time series data collected from measurement points in the power grid system, a correlation coefficient matrix is calculated, and then a time series correlation graph is constructed using graph theory knowledge. The Kruskal algorithm is used to search for the longest spanning tree in the time series correlation graph. Finally, based on the spanning tree, the breadth-first search (BFS) algorithm is further used to obtain the maximum time series correlation ring[1]. By using the time series correlation of each node within the ring and the physical topological relationship between nodes, the abnormal data can be traced back. Distinguish whether the generation of abnormal data is due to a single point failure or system failure. Most of the existing fault tracing techniques are based on machine learning algorithms, which are difficult to widely apply in high-dimensional time series data due to their high complexity.Through experiments on real power grid data, the results verify the effectiveness of this method in anomaly tracing of high-dimensional time series data. Through comparative experiments, this method is superior to machine learning model algorithms in terms of efficiency and stability. At the same time, this method does not require additional calculations of the statistical distribution of the samples to be tested, thus greatly saving computational costs.

A parallel Breadth-First Search using shared memory level-synchronization

Breadth-first search (BFS) stands as a cornerstone in graph exploration techniques, enabling systematic traversal of a provided graph. As the digital era continues to burgeon, there has been a marked upswing in the need to process vast graph-based data sets. To harness the power of such data effectively, it becomes imperative to use computational tools efficiently. Parallelizing BFS emerges as a pivotal strategy in this regard, leveraging the expansive capabilities of multiprocessor systems to maximize efficiency. This manuscript introduces a level-synchronous parallel BFS that is predicated on the shared-memory model. Recognizing the potential pitfalls of such an approach, especially regarding overhead induced by implicit barriers and critical sections, meticulous optimization techniques are infused into the model. These strategies are not mere afterthoughts; they are woven into the fabric of the design, ensuring smooth operations even when scaled. To validate the efficacy of this model, a rigorous assessment is carried out using the Graph500 benchmark. This offers insights into the performance scale of the parallel BFS algorithm, especially focusing on its speedup in relation to the number of operational threads. Concluding this exploration, the paper delineates prospective avenues for refining and further enhancing the proposed parallel implementation, aiming for even greater efficiencies in future endeavors.

Optimization of Chlorine Injection Schedule in Water Distribution Networks Using Water Age and Breadth-First Search Algorithm

Chlorine decay over time and distance travelled poses challenges in maintaining consistent chlorine levels from treatment plants to demand nodes in water distribution networks (WDNs). Many studies have focused on optimizing chlorine booster systems and addressing dosage and location. This study proposes a chlorine injection optimization model for maintaining spatial and temporal chlorine residuals within an acceptable range. First, the approach involves identifying potential pathways from the source to demand nodes using a breadth-first search (BFS) algorithm. Subsequently, the required chlorine injection to maintain a 0.2 mg/L residual chlorine level at demand nodes is estimated based on water age. Finally, a single-objective genetic algorithm optimizes the chlorine injection schedule at the source. The results demonstrated that chlorine estimation based on water age exhibited promising results with an average error below 10%. In addition, the four-interval injection scheme performed well in adapting to changing demand patterns, making the method robust to varying demand patterns. Moreover, the model could accommodate fluctuating water temperature conditions according to operating seasons. This study provides valuable insights into effectively managing chlorine levels and operations of WDNs, and paves the way for using water age for chlorine estimation.

Evaluation of Shortest Path by using Breadth-First Algorithm under Neutrosophic Environment

This paper recommends and designs concepts for evaluating the shortest path (SP) for a connected network using a modified breadth-first search algorithm in an uncertain environment. Evaluating the SPs of a network is an essential and extensively encountered optimization problem. Here we develop a new method for determining the SP in a neutrosophic environment in which the arc lengths are uncertain. Here, we use the parameters as neutrosophic numbers, and the new methodology, i.e., canonical representation of neutrosophic numbers in a neutrosophic environment, is used to convert neutrosophic edge lengths to crisp edge lengths and enhance the traditional breadth-first search algorithm. We employ this concept to accommodate the ambiguities in subjective decisions and address these issues. Here we demonstrate that the proposed algorithm has good stability and high efficiency for evaluating the SP. Finally, a numerical example is provided to explain the suggested algorithm.

Non-Centralised Balance Dispatch Strategy in Waked Wind Farms through a Graph Sparsification Partitioning Approach

A novel non-centralised dispatch strategy is presented for wake redirection to optimise large-scale offshore wind farms operation, creating a balanced control between power production and fatigue thrust loads evenly among the wind turbines. This approach is founded on a graph sparsification partitioning strategy that takes into account the impact of wake propagation. More specifically, the breadth-first search algorithm is employed to identify the subgraph based on the connectivity of the wake direction graph, while the PageRank centrality computation algorithm is utilised to determine and rank scores for the shared turbines’ affiliation with the subgraphs. By doing so, the wind farm is divided into smaller subsets of partitioned turbines, resulting in decoupling. The objective function is then formulated by incorporating penalty terms, specifically the standard deviation of fatigue thrust loads, into the maximum power equation. Meanwhile, the non-centralisation sequential quadratic programming optimisation algorithm is subsequently employed within each partition to determine the control actions while considering the objectives of the respective controllers. Finally, the simulation results of case studies prove to reduce computational costs and improve wind farm power production by balancing accumulated fatigue thrust loads over the operational lifetime as much as possible.

Cascading Delays for the High-Speed Rail Network Under Different Emergencies: A Double Layer Network Approach

High-speed rail (HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains' operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains' operation strategy and the systems' constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.

Assessment of topological boundaries for microgrids in an active power distribution network during emergencies

Microgrid (MG) operation has become an intriguing possibility to enhance the sustainability, reliability, and resilience of electricity distribution systems in emergencies. However, identifying suitable physical boundaries of MGs from an active power delivery infrastructure is a highly challenging task. This study describes a novel approach for determining the topological boundary of multiple MGs to restore power supply in a power distribution system during the unavailability of grid power supply. This method tries to enhance the self-adequacy of the MGs by increasing power flow within the MG boundaries. In this context, possible demand at the nodes of the power delivery infrastructure during an emergency is anticipated by an autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) network-based time series forecast models. A modified backward-forward sweep (MBFS) power flow analysis, tailored for islanded mode, is employed to assess the power flow status in the MGs during emergencies. The MGs are supplied with locally available dispatchable distributed generators (DDGs). A particle swarm optimization (PSO) algorithm intertwined with the MBFS power flow method has been employed to determine the boundary of MGs by identifying minimum power flow line in the shortest path between the consecutive DDGs. Further, this study demonstrates the evaluation of the shortest path between paths between the consecutive DDGs with the help of a breadth-first search (BFS) algorithm. The proposed strategy has been tested on a modified 33-bus, 69-bus and 118-bus power distribution test systems. The experimented results are verified with the results obtained from similar existing approaches to explore the opportunities in the proposed methodology.

Reward bonuses with gain scheduling inspired by iterative deepening search

This paper introduces a novel method of adding intrinsic bonuses to task-oriented reward function in order to efficiently facilitate reinforcement learning search. While various bonuses have been designed to date, this paper points out that the intrinsic bonuses can be analogous to either of the depth-first or breadth-first search algorithms in graph theory. Since these two have their own strengths and weaknesses, new bonuses that can bring out the respective characteristics are first proposed. The designed bonuses are derived as differences in key indicators that converge in the steady state based on the concepts of value disagreement and self-imitation. Then, a heuristic gain scheduling is applied to the designed bonuses, inspired by the iterative deepening search, which is known to inherit the advantages of the two search algorithms. The proposed method is expected to allow agent to efficiently reach the best solution in deeper states by gradually exploring unknown states. In three locomotion tasks with dense rewards and three simple tasks with sparse rewards, it is shown that the two types of bonus contribute to the performance improvement of the different tasks complementarily. In addition, by combining them with the proposed gain scheduling, all tasks can be accomplished with high performance.

Research on Seismic Connectivity Reliability Analysis of Water Distribution System Based on CUDA

To improve the seismic connectivity reliability (SCR) analysis efficiency of water distribution systems (WDS) based on Monte Carlo (MC) simulation, the quasi-Monte Carlo (QMC) method sampled by a low-discrepancy sequence is applied. Furthermore, a parallel algorithm combined with the breadth-first search algorithm for SCR analysis of WDS based on the QMC method and Compute Unified Device Architecture (CUDA) platform was proposed. A city WDS was taken as a computational example, the accuracy and efficiency of the traditional MC algorithm and parallel algorithm were compared, and the influence of the Sobol sequence and pseudo-random number sequence was analysed. The analysis results show that when 1,000,000 simulations are performed, the maximum error of the calculation results of the two methods is 0.2%, and the parallel method can obtain a six-fold speedup ratio compared with the serial method, indicating that the proposed parallel method is correct, meets the accuracy requirements, and helps to improve the SCR analysis efficiency. When the number of simulations is the same, the simulation results based on the Sobol sequence are more accurate than those based on the pseudo-random number sequence. The proposed parallel method also achieves a good acceleration effect in the SCR analysis of large-scale WDS.