Path Search Research Articles

Effective anti-submarine decision support system based on heuristic rank-based Dijkstra and adaptive threshold partitioning mechanism

Submarines possess strong covert striking capabilities, making anti-submarine warfare (ASW) a global naval priority. The Hidden Markov Anti-Submarine Model (HMASM) finds crucial applications in dynamic and uncertain ASW. The model delineates ASW into two phases: partitioning and search path planning. Current partitioning algorithms often include cells leading to redundant and competitive search, determined empirically. Additionally, in HMASM, the path planning algorithms based on genetic algorithm (GA) are time-consuming and exhibits unstable performance. This paper proposes a novel solution to these issues. For partitioning, a Reassigned k-Nearest Neighbour (RKNN) algorithm is introduced, identifying and reallocating units causing repeated and competing searches. For search path planning, heuristic rules for Dijkstra's cost function and goal point selection transform the NP-hard problem of maximizing the expected number of detections (ED) into a deterministic algorithm-compatible form. A heuristic rank-based selection model, Rank-Based Dijkstra under the Hidden Markov Model (HMM-R-Dijkstra), considering distance and probability, is added to Dijkstra. Furthermore, an Adaptive Threshold Partitioning (ATP) dynamically monitors searcher exploration by setting variables and thresholds to determine optimal partitioning timing, preventing untimely and excessive partitioning. Combining RKNN, HMM-R-Dijkstra, and ATP forms R-HRD-ATP, optimizing all parameters using parallel structures. Through three comparative experiments, R-HRD-ATP's performance steadily improves. Experiments comparing R-HRD-ATP to GA, Sparrow Search Algorithm (SSA)-GA, and Ant Colony Optimization (ACO)-GA reveal performance enhancements of 25–70.99% and time savings of 56–295 times for our model. Importantly, no parameter adjustments are required. The success of R-HRD-ATP indicates that its heuristic rules can support the establishment of robust applications of deterministic path planning algorithms in HMASM.

Physical Prior Mean Function-Driven Gaussian Processes Search for Minimum-Energy Reaction Paths with a Climbing-Image Nudged Elastic Band: A General Method for Gas-Phase, Interfacial, and Bulk-Phase Reactions.

The climbing-image nudged elastic band (CI-NEB) method serves as an indispensable tool for computational chemists, offering insight into minimum-energy reaction paths (MEPs) by delineating both transition states (TSs) and intermediate nonstationary structures along reaction coordinates. However, executing CI-NEB calculations for reactions with extensive reaction coordinate spans necessitates a large number of images to ensure a reliable convergence of the MEPs and TS structures, presenting a computationally demanding optimization challenge, even with mildly costly electronic-structure methods. In this study, we advocate for the utilization of physically inspired prior mean function-based Gaussian processes (GPs) to expedite MEP exploration and TS optimization via the CI-NEB method. By incorporating reliable prior physical approximations into potential energy surface (PES) modeling, we demonstrate enhanced efficiency in multidimensional CI-NEB optimization with surrogate-based optimizers. Our physically informed GP approach not only outperforms traditional nonsurrogate-based optimizers in optimization efficiency but also on-the-fly learns the reaction path valley during optimization, culminating in significant advancements. The surrogate PES derived from our optimization exhibits high accuracy compared to true PES references, aligning with our emphasis on leveraging reliable physical priors for robust and efficient posterior mean learning in GPs. Through a systematic benchmark study encompassing various reaction pathways, including gas-phase, bulk-phase, and interfacial/surface reactions, our physical GPs consistently demonstrate superior efficiency and reliability. For instance, they outperform the popular fast inertial relaxation engine optimizer by approximately a factor of 10, showcasing their versatility and efficacy in exploring reaction mechanisms and surface reaction PESs.

Enhancing Autonomous Underwater Vehicle Decision Making through Intelligent Task Planning and Behavior Tree Optimization

The expansion of underwater scenarios and missions highlights the crucial need for autonomous underwater vehicles (AUVs) to make informed decisions. Therefore, developing an efficient decision-making framework is vital to enhance productivity in executing complex tasks within tight time constraints. This paper delves into task planning and reconstruction within the AUV control decision system to enable intelligent completion of intricate underwater tasks. Behavior trees (BTs) offer a structured approach to organizing the switching structure of a hybrid dynamical system (HDS), originally introduced in the computer game programming community. In this research, an intelligent search algorithm, MCTS-QPSO (Monte Carlo tree search and quantum particle swarm optimization), is proposed to bolster the AUV’s capacity in planning complex task decision control systems. This algorithm tackles the issue of the time-consuming manual design of control systems by effectively integrating BTs. By assessing a predefined set of subtasks and actions in tandem with the complex task scenario, a reward function is formulated for MCTS to pinpoint the optimal subtree set. The QPSO algorithm is then leveraged for subtree integration, treating it as an optimal path search problem from the root node to the leaf node. This process optimizes the search subtree, thereby enhancing the robustness and security of the control architecture. To expedite search speed and algorithm convergence, this paper recommends reducing the search space by pre-grouping conditions and states within the behavior tree. The efficacy and superiority of the proposed algorithm are validated through security and timeliness evaluations of the BT, along with comparisons with other algorithms for automatic AUV decision control behavior tree design. Ultimately, the effectiveness and superiority of the proposed algorithm are corroborated through simulations on a multi-AUV complex task platform, showcasing its practical applicability and efficiency in real-world underwater scenarios.

Charting the evolutionary conceptual pathway of analytic network process research: A main path analysis

AbstractThis study delves into the flow and structure of knowledge prevalent in Analytic Network Process (ANP) literature. It traces the evolutionary pathway of ANP research, spotlighting seminal works and unfolding the development of topics over time. Analysing 4583 ANP literature records from 1986 to 2022, this research utilizes two pivotal data sources: Web of Science (WoS) and Dimensions. This main path analysis (MPA) approach is coupled with techniques such as citation analysis, social network analysis, and centrality measures to identify most influential papers, as well as using a search path algorithm (SPC) to provide higher weights to papers that serve as bridges connecting different research paths rather than simply counting all citations equally. Key works emerged as focal points in both datasets, including notable contributions from Lee, Kheybari and Büyüközkan, with the Dimensions dataset uniquely highlighting Saaty as the initial conceptual knowledge source of the ANP. The centrality measures unearthed persistent bridge roles undertaken by key contributors such as Saaty and Sarkis over different time periods. Furthermore, a detailed topic analysis discerned eight distinctive thematic clusters among the two data sources: analytical network process, network process, fuzzy analytic, evaluation laboratory, supply chain, quality function, hierarchy process, and information systems. These clusters emerged, providing a comprehensive map of ANP topics and their evolution. The study offers a comprehensive framework for scholars to navigate the expansive domain of ANP research. Identifying pivotal works and emergent themes provides direction for future research endeavours. It allows practitioners to gain invaluable insights into the foundational and transformative works in the ANP realm, enhancing their understanding and application of the methodology. This paper proposes of a dual methodological strategy: First, by using traditional academic data sources (WoS) along with a more encompassing Dimensions database; and second, by using a SPC algorithm to weight MPA papers based on their connecting role among different research paths. At the discipline level, this study carves out a novel vantage point in understanding the ANP research landscape. It offers an integrated portrayal of the field's conceptual evolution, identifying specific cluster topics as an indispensable resource for scholars, practitioners, and students.

Development of A Turn Label Based Optimal Path Search Algorithm

Development of A Turn Label Based Optimal Path Search Algorithm

A Chlorophyll-a Concentration Inversion Model Based on Backpropagation Neural Network Optimized by an Improved Metaheuristic Algorithm

Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks tend to become stuck in local optima, and their prediction accuracy fluctuates significantly, thus posing restrictions to their accuracy and stability in the inversion process. Studies have found that metaheuristic optimization algorithms can significantly improve these shortcomings by optimizing the initial parameters (weights and biases) of BP neural networks. In this paper, the adaptive nonlinear weight coefficient, the path search strategy “Levy flight” and the dynamic crossover mechanism are introduced to optimize the three main steps of the Artificial Ecosystem Optimization (AEO) algorithm to overcome the algorithm’s limitation in solving complex problems, improve its global search capability, and thereby improve its performance in optimizing BP neural networks. Relying on Google Earth Engine and Google Colaboratory (Colab), a model for the inversion of Chl-a concentration in the coastal waters of Hong Kong was built to verify the performance of the improved AEO algorithm in optimizing BP neural networks, and the improved AEO algorithm proposed herein was compared with 17 different metaheuristic optimization algorithms. The results show that the Chl-a concentration inversion model based on a BP neural network optimized using the improved AEO algorithm is significantly superior to other models in terms of prediction accuracy and stability, and the results obtained via the model through inversion with respect to Chl-a concentration in the coastal waters of Hong Kong during heavy precipitation events and red tides are highly consistent with the measured values of Chl-a concentration in both time and space domains. These conclusions can provide a new method for Chl-a concentration monitoring and water quality management for coastal waters.

A new method for unmanned aerial vehicle path planning in complex environments

To solve the problems of UAV path planning, such as low search efficiency, uneven path, and inability to adapt to unknown environments, this paper proposes A double-layer optimization A* and dynamic window method for UAV path planning. Firstly, the neighboring node clip-off rule is defined to optimize the node expansion mode of the A* algorithm, and the obstacle coverage model is designed to dynamically adjust the heurizing function of the A* algorithm to improve the path search efficiency. Then, the Bresenham algorithm is adopted for collision detection and critical path nodes are extracted to significantly reduce the number of path turning points. Secondly, a new tracking index is proposed to optimize the evaluation function of the dynamic window method to make the local path fit the global path further. By detecting the dangerous distance, the dynamic adaptive method of evaluation function weight is designed to improve the fixed weight of the dynamic window method. Finally, the key turning point of optimizing the A* algorithm is taken as the temporary target point to improve the DWA algorithm, and the local part follows the global part, and the fusion of the two algorithms is realized. Simulation results show that the proposed method can significantly improve the efficiency and smoothness of mobile robot path planning, enhance the real-time obstacle avoidance and adaptive ability of unknown environments, and better meet the requirements of complex planning tasks.

Omnidirectional AGV Path Planning Based on Improved Genetic Algorithm

To address the issues with traditional genetic algorithm (GA) path planning, which often results in redundant path nodes and local optima, we propose an Improved Genetic Algorithm that incorporates an ant colony algorithm (ACO). Firstly, a new population initialization method is proposed. This method adopts a higher-quality random point generation strategy to generate random points centrally near the start and end of connecting lines. It combines the improved ACO algorithm to connect these random points quickly, thus greatly improving the quality of the initial population. Secondly, path smoothness constraints are proposed in the adaptive function. These constraints reduce the large-angle turns and non-essential turns, improving the smoothness of the generated path. The algorithm integrates the roulette and tournament methods in the selection stage to enhance the searching ability and prevent premature convergence. Additionally, the crossover stage introduces the edit distance and a two-layer crossover operation based on it to avoid ineffective crossover and improve convergence speed. In the mutation stage, we propose a new mutation method and introduce a three-stage mutation operation based on the idea of simulated annealing. This makes the mutation operation more effective and efficient. The three-stage mutation operation ensures that the mutated paths also have high weights, increases the diversity of the population, and avoids local optimality. Additionally, we added a deletion operation to eliminate redundant nodes in the paths and shorten them. The simulation software and experimental platform of ROS (Robot Operating System) demonstrate that the improved algorithm has better path search quality and faster convergence speed. This effectively prevents the algorithm from maturing prematurely and proves its effectiveness in solving the path planning problem of AGV (automated guided vehicle).

Searching for the fine particulate matter (PM 2.5) pollutant emission source using a drone

In this research, we propose a method with three search algorithms, namely Greedy, Dynamic, and Hybrid, to exploit the ability of a drone to efficiently and accurately locate air pollution (i.e., particulate matter) emission sources. Currently, most of the existing stationed environment sensor systems can provide continuous monitoring information of air quality such as PM 2.5 (Particulate Metter 2.5) or CO2 (carbon dioxide) but are unable to pinpoint the location of the emission source. The proposed method utilizes the PM2.5 concentration information provided by the existing sensing system to initialize the search plan by limiting a search area. Based on the initial sensing information, a drone with an onboard air quality sensor will adjust its searching direction and distance to an intermediate point. After an intermediate location point is reached, the drone will pause and sense the PM2.5 concentration at the current location. Next, the drone continues to adjust the search path with its searching direction and distance based on the sensed PM 2.5 concentration level until the emission source is located. Utilizing the information provided by both an existing sensing system and an onboard sensor, the drone is able to make correct decisions when searching for pollution sources. In the experiments, three proposed algorithms and two common search methods are compared under different settings. Experiment results showed our proposed method is able to achieve a location estimation error below 2 m within 20 min.

A computational investigation on the photochemistry of the popular spin‐trap agent N‐tert‐butyl‐α‐phenylnitrone (PBN) and thermal isomerization pathways of its photoproduct oxaziridine

AbstractComputational investigation on the low‐lying photo‐excited states of N‐tert‐butyl‐α‐phenylnitrone (PBN), a well‐known spin‐trap agent, has revealed its photo‐product (oxaziridine) formation channel. The S0‐S2 vertical excitation in PBN is subsequently followed by a non‐radiative decay pathway through S2/S1 and S0/S1 conical intersections (CIs) with CNO‐kinked structures, situated around 23 kcal/mol and 45 kcal/mol below the vertically excited S2 state, respectively. The reverse photo‐process of PBN formation involves photo‐excitation of oxaziridine to its S2 and S3 photo‐excited states. The forward photo‐isomerization leads to the trans‐oxaziridine with a backside CNO kink (trans‐OXB) while the reverse path studied by us, connects its front‐side CNO‐kinked analogue (trans‐OXF) with the PBN. Our search for the reverse thermal reaction paths from these two oxazirdines has led to their corresponding transition states, one at 35 kcal/mol and the other at 27 kcal/mol above trans‐OXF and trans‐OXB geometries, respectively. They lead to two different isomers (E and Z) of PBN which supports the reported nature of products from the trans‐oxaziridine in this thermal reaction. The inversion path of the chiral nitrogen atom of this N‐tert‐butyl‐oxaziridine (barrier 21 kcal/mol) has also been tracked. This reaction path has been compared with that of the N‐methyl (barrier 30 kcal/mol) and N‐acyl (barrier 10.5 kcal/mol) oxaziridine analogues.

Abundance estimation of plains zebras via search–encounter sampling

Spatial capture–recapture (SCR) using search–encounter methods estimate population abundance from encounters of individually recognisable animals along an a priori-designated search path. We applied search–encounter SCR methods and photographic sampling to estimate the abundance of plains zebras (Equus quagga) at Telperion and Ezemvelo nature reserves, South Africa. We analysed encounter data by comparing four hazard-function models for the detection process. The abundance estimate under three models was just above 1000 animals (95% credible intervals c. 960, 1220) versus 811 (719, 917) for the remaining model. The former estimates were broadly similar to aerial counts conducted around the same time. Standard deviation in locations around individual activity centres (σmove\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma _{move}$$\\end{document}) was c. 0.8 km, with little difference between models. In situations where structured surveys are not possible, the approach presented here has the potential to estimate abundance from opportunistic animal encounters (e.g. generated via citizen science schemes) within an SCR framework.

Research on the Application of Computer Big Data Technology in Smart Travel

This paper mainly focuses on the algorithms related to local path planning and path tracking control of unmanned vehicles in the process of obstacle avoidance. By introducing the temporal dimension as a reference, the perceptual results are projected onto the 3D spatio-temporal navigation map by combining the multi-target behavior prediction and other means; by increasing the temporal dimension, the static obstacles and dynamic obstacles are unified into the same parameter space. Under this parameter space, the front-end A* path search initializes the unified B spline curve control points, designs the trajectory cost function and performs nonlinear optimization to generate a spatio-temporal trajectory that satisfies the safety collision-free and vehicle motion constraints (speed and acceleration limits), thus transforming the decision and planning problem under the two-dimensional fence dynamic physical space into a static scene decision and planning problem under the three-dimensional spatio-temporal space. Through simulation verification, the whole process of the proposed trajectory planning method takes 51.27ms on average, which meets the driving requirements of driverless cars. In addition, by adjusting the search conditions of the A* algorithm, its overall planning efficiency is improved by 27.86% compared with the search speed of the traditional algorithm. The actual feeling and data results from the real vehicle experiments show its good tracking effect, which verifies the effectiveness and practicality of the algorithm proposed in this paper.

A hybrid path-following approach for constrained nonlinear-buckling optimization of variable stiffness composite shells with shape imperfections

Nonlinear buckling optimization of variable stiffness (VS) composite shells is computationally expensive due to frequent nonlinear analyses. Although the generalized path-following method can serve to reduce the redundant computation, it is weakened by two kinds of discontinuity. First, the design variables may not always be searched continuously in the constrained optimization. Second, the critical buckling states may not be continuous at singular points on the search path. To address this issue, a hybrid path-following approach is proposed to trace the critical buckling states in the nonlinear-buckling optimization with constraints on the realizability of the lamination parameters and the amplitude range of geometric imperfections. Firstly, the optimal search direction is determined with the sensitivity analysis of the critical buckling equations in the isogeometric continuum shell model. Then, a continuous following strategy is adopted to provide a proper initial estimate for the Newton iterations at a disconnected point. Moreover, an investigation into the singularity of the critical buckling equations is conducted to identify the discontinuous points of the critical states in a line-search. Upon detection of the singularity, a segment of path in state space is inserted to recover the continuity. Numerical experiments show that the continuity of the critical states in the optimization is well maintained, which leads to a more efficient optimization for the VS shell with shape imperfections.

Вопросы теории и практики стимулирования бизнеса посредством института страховых взносов

Due to the need to develop certain types of commercial and industrial activities, the state is on the path of permanent search and optimization of tools and methods for such development. A special place among them is given to tax incentives as a system that provides attractive business conditions, including by reducing insurance premium rates. As a result, there is an increasing need for scientific substantiation of the processes of using tax incentives for business. The article examined the rules establishing the use of benefits in relation to individual payers of insurance premiums, which made it possible to discover the peculiarities of tax incentives for these subjects. The positions available in legal science on the issues of work were also studied. The conclusion about the need for further legislative reform of tax incentives for individual business entities is substantiated. You should also take into account the long-term results of applying benefits on insurance premiums associated with the further development of the IT technology market and the increase in the number of small and medium-sized businesses.

Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment

High-dimensional vector similarity search (HVSS) is gaining prominence as a powerful tool for various data science and AI applications. As vector data scales up, in-memory indexes pose a significant challenge due to the substantial increase in main memory requirements. A potential solution involves leveraging disk-based implementation, which stores and searches vector data on high-performance devices like NVMe SSDs. However, implementing HVSS for data segments proves to be intricate in vector databases where a single machine comprises multiple segments for system scalability. In this context, each segment operates with limited memory and disk space, necessitating a delicate balance between accuracy, efficiency, and space cost. Existing disk-based methods fall short as they do not holistically address all these requirements simultaneously. In this paper, we present Starling, an I/O-efficient disk-resident graph index framework that optimizes data layout and search strategy within the segment. It has two primary components: (1) a data layout incorporating an in-memory navigation graph and a reordered disk-based graph with enhanced locality, reducing the search path length and minimizing disk bandwidth wastage; and (2) a block search strategy designed to minimize costly disk I/O operations during vector query execution. Through extensive experiments, we validate the effectiveness, efficiency, and scalability of Starling. On a data segment with 2GB memory and 10GB disk capacity, Starling can accommodate up to 33 million vectors in 128 dimensions, offering HVSS with over 0.9 average precision and top-10 recall rate, and latency under 1 millisecond. The results showcase Starling's superior performance, exhibiting 43.9x higher throughput with 98% lower query latency compared to state-of-the-art methods while maintaining the same level of accuracy.

An Improved A-Star Path Planning Algorithm Based on Mobile Robots in Medical Testing Laboratories.

In the blood sample management pipeline environment, we have innovatively improved the traditional A-star algorithm to enhance the efficiency of mobile robots. This study employs a grid environmental modeling approach to accurately simulate medical testing laboratories. On the grid map, we utilize an 8-neighbor search method for path planning to accommodate the complex structure within the laboratory. By introducing an improved evaluation function and a bidirectional search strategy, we have successfully reduced the number of search nodes and significantly improved path search efficiency. Additionally, we eliminate redundant nodes in the path, smooth the path using cubic uniform B-spline curves, remove unnecessary inflection points, and further optimize the motion trajectory of the robot. The experimental results of the path planning simulation under different scenarios and specifications show that the improved A-star algorithm has higher search efficiency and traverses fewer nodes compared to the traditional A-star algorithm and the bidirectional A-star algorithm. Overall, the simulation experiments verify the feasibility of the improved A-star algorithm, which can better meet the needs of actual mobile robots in real medical testing laboratories.

Electrical Engineering and its Aspects in the Modern Concept of Technical Education

This paper focuses on some aspects in the concept of teaching electrical engineering in technical education. It is presented the issues of possible approaches and tools allowing to implement the teaching of electrical engineering in a demonstrative and entertaining way using contemporary means based on digital technologies. In this framework, the possibilities of motivating students are also presented and some findings from selected scientific papers, domestic and foreign research are also summarized.
 The introductory part presents the issues related to the definition of subject or subject didactics of electrical engineering as a prerequisite for successful teaching by teachers. The next part is devoted to a research investigation focused on the application of new approaches to teaching electrical engineering in school practice, the possibility of linking virtual or augmented reality in a demonstrative approach to teaching, the use of online applications to solve electrical engineering problems and thus linking the practical form of electrical engineering with the theory presented in the classroom with the support of digital technologies.
 The conclusion then summarizes the possibilities associated with the teaching of electrical engineering and the search for new paths that are common to both students preparing for the teaching profession and the students who will one day be guided by these teachers through the secrets of electrical engineering as one of the most important fields of human activity.

Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree

In the domain of mobile robotic navigation, the real-time generation of low-cost, executable reference trajectories is crucial. This paper propounds an innovative path planning strategy, termed Dynamic Bridging Rapidly Exploring Random Tree (DBR-RRT), which endeavors to enable safe and expedited path navigation. Initially, a heuristic discrimination method is engaged in the path search phase, whereby the issue of sluggish search velocity is tackled by evaluating whether sampled points reside at “bridging locations” within a free space, and by assessing the spatial–geometric relationships between proximate obstacles and auxiliary points. Subsequently, by leveraging extended speed, additional sampling points are generated in the vicinity of existing points to augment the search’s efficacy. Ultimately, the path is optimized and pruned by synthesizing the local curvature of the sampling points and the proximity to obstacles, assigning varied priorities to nodes, thus ensuring that the path’s quality and smoothness is upheld.

A dynamic weight–assignment load balancing approach for workflow scheduling in edge-cloud computing using ameliorated moth flame and rock hyrax optimization algorithms

As the geographically distributed cloud infrastructure continues to grow in scale and the intricacy of workflow applications increases, there is a growing threat to the operational efficiency of the system. This poses the risk of resource inefficiencies and elevated energy consumption. Load balancing becomes a critical concern for this category of applications, particularly when they encounter overwhelming demand surges and resource outages. Numerous comprehensive methods still overlook certain aspects, such as the inclusion of multiple objectives, addressing local minimum problems, and optimizing resource utilization to its fullest potential. Our research focus is on exploring cloud-deployed three-tier applications to address these challenges. This paper presents a new approach to load balancing, introducing a novel weight assignment algorithm. The algorithm effectively distributes tasks among virtual machines using three carefully optimized algorithms. To begin with, we convert the user tasks into the directed acyclic graph (DAG) structured workflow, organizing them according to their execution sequence. The iterative critical path search algorithm, implemented with a layer-based priority assigning technique, addresses the critical path challenge. This process involves finding a critical path, dividing tasks into distinct layers, classifying them based on their computational complexity and priorities, and distinguishing between critical and non-critical tasks. Following the classification process, the tasks are organized and prioritized using an improved Moth Flame Optimization approach based on multiple criteria, which consider criticality, priority, and makespan. This approach significantly reduces scheduling latency and delays. Finally, task allocation is proposed using the high convergence rock hyrax optimization algorithm with a divide-and-conquer strategy, enabling effective exploration and exploitation of overcrowded resources for improved load balancing. The CloudSim simulation tool is used to carry out the simulation, where it assesses various performance aspects such as latency, bandwidth, completion time, throughput, energy usage, instances of not meeting QoS violations, and overhead.

AFL++: A Vulnerability Discovery and Reproduction Framework

Directed greybox fuzzing can mainly be used for vulnerability mining and vulnerability replication. However, there are still some issues with existing directional fuzzing tools. One is that after providing problematic changes or patches, it is not possible to quickly target and discover the problem. Secondly, it is difficult to break through the magic byte path, making it difficult to mine deep vulnerabilities. This article proposes a new vulnerability mining and repair framework: American Fuzz Lop Plus (AFL++). Firstly, we utilize alias analysis to enhance inter-procedural control flow graphs and redefine the distance calculation formula to obtain more accurate distances. Secondly, the Newton interpolation method is used for the energy initialization of each seed to prevent test cases from being filtered out due to low energy. A heuristic energy scheduling algorithm is proposed to judiciously schedule the energy of seeds. During the path exploration phase, by adjusting the seed energy, shorter-distance seeds quickly reach the target; with increasing time, seeds tend to explore deeper paths. We then represent the symbolic distance by the number of instructions passed to reach the target and investigate the shortest path search strategy to achieve path pruning, alleviating the problem of path explosion. Finally, based on the above methods, we implement the AFL++ prototype system, integrating directed greybox fuzzing with symbolic execution technology for vulnerability discovery. By interleaving directed symbolic execution and directed greybox fuzzing, the efficiency of vulnerability discovery and reproduction is effectively enhanced.