Bidirectional Search Algorithm Research Articles

PDHF: Effective phishing detection model combining optimal artificial and automatic deep features

Currently, the increasing number of high-volume phishing attacks is among the largest threats to networking environments on a daily basis. During such a severe attack, researchers prefer to extract numerous features to improve the accuracy of phishing detection. However, the redundant features that may exist in the extracted feature set may be adapted by phishing attackers, not only degrading detection performance but also shortening the effective time of the constructed detection models. To address these problems, this study proposes phishing detection based on hybrid features (PDHF), a novel phishing detection model based on a combination of optimal artificial and automatic deep learning features. The optimal artificial phishing features are obtained by removing redundant features based on the newly designed feature importance evaluation index and an improved bidirectional search algorithm. To extend the effective time of phishing detection, deep features are learned from URLs using a one-dimensional character convolutional neural network (CNN) and a disorderly quantized attention mechanism. The experimental results show that PDHF outperforms many state-of-the-art methods and achieves an accuracy of 0.9965, precision of 0.9942, recall of 0.9940, and F1-score of 0.9941. These results can help in the development of a security plug-in for clients, browsers, and various instant messaging tools that run on network edges, personal computers, smartphones, and other personal terminals.

Finding strong components using depth-first search

We survey three algorithms that use depth-first search to find the strong components of a directed graph in linear time: (1) Tarjan’s algorithm; (2) a cycle-finding algorithm; and (3) a bidirectional search algorithm.

Software Defined Network-Based Multi Service Routing Algorithm for Spatial Information Network

In order to solve the problems of unbalanced utilization of link resources due to the different capabilities of nodes in the spatial information network and the difficulty of satisfying the quality of service(QoS) with the change of task requirements, this paper proposes a multi-service routing algorithm for a spatial information network based on Software Defined Network(SDN), which divides the network transmission services into three categories: delay sensitive, bandwidth sensitive and packet loss rate sensitive. The link state information between satellite nodes is collected by an SDN controller, and then the topology-based bidirectional search algorithm is used to establish a “Source-Destination” set of routes between satellite nodes, the algorithm counts the delay, bandwidth, and packet loss rate of each route in the set, and selects routes that meet the needs of different business classes for data forwarding. The results show that the routing cost of this algorithm is lower than other algorithms, and it has better throughput performance.

Conflict-tolerant and conflict-free multi-agent meeting

In the Multi-Agent Meeting problem (MAM), the task is to find the optimal meeting location for multiple agents, as well as a path for each agent to that location. Among all possible meeting locations, the optimal meeting location has the minimum cost according to a given cost function. Two cost functions are considered in this research: (1) the sum of all agents paths' costs to the meeting location (SOC) and (2) the cost of the longest path among them (MKSP). MAM has many real-life applications, such as choosing a gathering point for multiple traveling agents (humans, cars, or robots).In this paper, we divide MAM into two variants. In its basic version, MAM allows multiple agents to occupy the same location, i.e., it is conflict tolerant. For MAM, we introduce MM*, a Multi-Directional Heuristic Search algorithm, that finds the optimal meeting location under different cost functions. MM* generalizes the Meet in the Middle (MM) bidirectional search algorithm to the case of finding an optimal meeting location for multiple agents. Several admissible heuristics are proposed for MM*, and experiments demonstrate the benefits of MM*.As agents may be embodied in the world, a solution to MAM may contain conflicting paths, where more than one agent occupies the same location at the same time. The second variant of the MAM problem is called Conflict-Free Multi-Agent Meeting (CF-MAM), where the task is to find the optimal meeting location for multiple agents (as in MAM) as well as conflict-free paths (in the same manner as the prominent Multi-Agent Path Finding problem (MAPF)) to that location. For optimally solving CF-MAM, we introduce two novel algorithms, which combine MAM and MAPF solvers. We prove the optimality of both algorithms and compare them experimentally, showing the pros and cons of each algorithm.1

Comparative Analysis of Shortest Route Problem Algorithms for Emergency Response Services

Emergency response services require a fast and shortest possible route when responding to emergency situations that include road accidents, crimes, the occurrence of fires, etc. In this regard, an efficient and robust shortest route locator is essentially important. This study primarily aims to analyze the performance of the various shortest route problem algorithms, including the regular Dijkstra’s algorithm, the Bidirectional search algorithm, and the Multidirectional Dijkstra’s algorithm (MDA). The implementations of these algorithms are evaluated and compared in order to determine which algorithm can be an excellent choice to be adopted by such emergency response services. The results showed that the MDA has efficient performance in both light and heavy traffic situations in terms of speed and response times.

Iterative Deepening Dynamically Improved Bounds Bidirectional Search

This paper presents a new bidirectional search algorithm to solve the shortest path problem. The new algorithm uses an iterative deepening technique with a consistent heuristic to improve lower bounds on path costs. The new algorithm contains a novel technique of filtering nodes to significantly reduce the memory requirements. Computational experiments on the pancake problem, sliding tile problem, and Rubik’s cube show that the new algorithm uses significantly less memory and executes faster than A* and other state-of-the-art bidirectional algorithms. Summary of Contribution: Quickly solving single-source shortest path problems on graphs is important for pathfinding applications and is a core problem in both artificial intelligence and operations research. This paper attempts to solve large problems that do not easily fit into the available memory of a desktop computer, such as finding the optimal shortest set of moves to solve a Rubik’s cube, and solve them faster than existing algorithms.

A Lane Level Bi-Directional Hybrid Path Planning Method Based on High Definition Map

A global reference path generated by a path search algorithm based on a road-level driving map cannot be directly used to complete the efficient autonomous path-following motion of autonomous vehicles due to the large computational load and insufficient path accuracy. To solve this problem, this paper proposes a lane-level bidirectional hybrid path planning method based on a high-definition map (HD map), which effectively completes the high-precision reference path planning task. First, the global driving environment information is extracted from the HD map, and the lane-level driving map is constructed. Real value mapping from the road network map to the driving cost is realized based on the road network information, road markings, and driving behavior data. Then, a hybrid path search method is carried out for the search space in a bidirectional search mode, where the stopping conditions of the search method are determined by the relaxation region in the two search processes. As the search process continues, the dimension of the relaxation region is updated to dynamically adjust the search scope to maintain the desired search efficiency and search effect. After the completion of the bidirectional search, the search results are evaluated and optimized to obtain the reference path with the optimal traffic cost. Finally, in an HD map based on a real scene, the path search performance of the proposed algorithm is compared with that of the simple bidirectional Dijkstra algorithm and the bidirectional BFS search algorithm. The results show that the proposed path search algorithm not only has a good optimization effect, but also has a high path search efficiency.

Multi-Directional Search

In the Multi-Agent Meeting (MAM) problem, the task is to find a meeting location for multiple agents, as well as a path for each agent to that location. In this paper, we introduce MM*, a Multi-Directional Search algorithm that finds the optimal meeting location under different cost functions. MM* generalizes the Meet in the Middle (MM) bidirectional search algorithm to the case of finding optimal meeting locations for multiple agents. A number of admissible heuristics are proposed and experiments demonstrate the benefits of MM*.

GBFHS: A Generalized Breadth-First Heuristic Search Algorithm

Recently there has been renewed interest in bidirectional heuristic search. New algorithms, e.g., MM, MMe, and NBS, have been introduced which seem much closer to refuting the accepted wisdom that "any front-to-end bidirectional heuristic search algorithm will likely be dominated by unidirectional heuristic search or bidirectional brute-force search". However, MM and MMe can still be dominated by their bidirectional brute-force versions, i.e., they can show a "hump-in-the-middle". We introduce a novel general breadth-first heuristic search algorithm, GBFHS, that unifies both unidirectional and bidirectional search into a single algorithm. It uses knowledge of the edge cost in unit cost domains to stop on first-collision in unidirectional search and in bidirectional search, unlike MM, MMe, and NBS. With no heuristic it expands fewer nodes bidirectionally than Nicholson's blind bidirectional search algorithm. GBFHS expands substantially fewer nodes than MM0, MM, MMe, and NBS. Additionally, GBFHS does not show a "hump-in-the-middle". GBFHS run bidirectionally is not dominated by bidirectional brute-force search, likewise, GBFHS run unidirectionally is not dominated by A*.

Enriching Non-Parametric Bidirectional Search Algorithms — Extended Abstract

NBS is a non-parametric bidirectional search algorithm, proved to expand at most twice the number of node expansions required to verify the optimality of a solution. We introduce new variants of NBS that are aimed at finding all optimal solutions. We then introduce an algorithmic framework that includes NBS as a special case. Finally, we introduce DVCBS, a new algorithm in this framework that aims to further reduce the number of expansions. Unlike NBS, DVCBS does not have any worst-case bound guarantees, but in practice it outperforms NBS in verifying the optimality of solutions.

Target-Value Search Revisited (Extended Abstract)

This paper addresses the Target-Value Search (TVS) problem, which is the problem of finding a path between two nodes in a graph whose cost is as close as possible to a given target value T. This problem has been previously addressed only for directed acyclic graphs. In this work we develop the theory required to solve this problem optimally for any type of graphs. We modify traditional heuristic search algorithms for this setting, and propose a novel bidirectional search algorithm that is specifically suited for TVS. The benefits of this bidirectional search algorithm are discussed both theoretically and experimentally on several domains. A longer version of this work was accepted to IJCAI-2013 (Linares Lopez et al. 2013)

The control of machining errors of tool path for unequal diameter flank milling of globoidal cam

The profile error is inevitable in the machining process of globoid cam. In order to tackle the problem of normal vector anisotropy in unequal diameter machining, the offset point optimization algorithm is served to optimize the tool position according to the error transfer relationship of equidistant surfaces and the characteristics of unequal diameter tool path generation. In this paper, a bidirectional search algorithm is devised to minimize the deviation between unequal diameter machining tool path surface and fitting ruled surface. The optimal numerical solution with high accuracy is obtained by genetic algorithm. And then the end points of the tool axis are regarded as the initial data. Inverse interpolation of the NURBS (Non-Uniform Rational B-Spline) interpolation is utilized to reconstruct the ruled surface of the flank milling tool path. The simulation results verify the effectiveness of the proposed approach. Compared with the existing methods, the presented one reduces the machining error greatly.

Dynamically improved bounds bidirectional search

This paper presents a bidirectional search algorithm that dynamically improves the bounds during its execution. It has the property that it always terminates on or before the forward search meets the backward search. Computational experiments on the pancake problem, the sliding tile puzzle, and the topspin problem demonstrate that it is capable of solving problems using significantly fewer node expansions than A⁎ or state-of-the-art bidirectional algorithms such as MMϵ and GBFHS.

Recognition of Blocking Categories for UWB Positioning in Complex Indoor Environment.

The recognition of non-line-of-sight (NLOS) state is a prerequisite for alleviating NLOS errors and is crucial to ensure the accuracy of positioning. Recent studies only identify the line-of-sight (LOS) state and the NLOS state, but ignore the contribution of occlusion categories to spatial information perception. This paper proposes a bidirectional search algorithm based on maximum correlation, minimum redundancy, and minimum computational cost (BS-mRMRMC). The optimal channel impulse response (CIR) feature set, which can identify NLOS and LOS states well, as well as the blocking categories, are determined by setting the constraint thresholds of both the maximum evaluation index, and the computational cost. The identification of blocking categories provides more effective information for the indoor space perception of ultra-wide band (UWB). Based on the vector projection method, the hierarchical structure of decision tree support vector machine (DT-SVM) is designed to verify the recognition accuracy of each category. Experiments show that the proposed algorithm has an average recognition accuracy of 96.7% for each occlusion category, which is better than those of the other three algorithms based on the same number of CIR signal characteristics of UWB.

Vehicle Routing Problem with Multiple Time Windows and Batch Splitting Based on Inferior First Bidirectional Search Algorithm

Considering continuous scheduling and full use of time in vehicle routing problem, we proposed a vehicle routing problem model with multi-time windows based on batch splitting, constructed an inferior-first bidirectional search algorithm (IFBSA) and established a quantitative measurement benchmark of “inferiority level” using Gaussian kernel function. We designed 30 examples based on some actual road network data and chose nearest neighbour search algorithm (NNSA) for comparing. Our results showed that, IFBSA reduced vehicles in 20 examples and got superior results in 23 examples for all examples compared with NNSA.

Enriching Non-Parametric Bidirectional Search Algorithms

NBS is a non-parametric bidirectional search algorithm proven to expand at most twice the number of node expansions required to verify the optimality of a solution. We introduce new variants of NBS that are aimed at finding all optimal solutions. We then introduce an algorithmic framework that includes NBS as a special case. Finally, we introduce DVCBS, a new algorithm in this framework that aims to further reduce the number of expansions. Unlike NBS, DVCBS does not have any worst-case bound guarantees, but in practice it outperforms NBS in verifying the optimality of solutions.

A shortlist-based bidirectional local search for the stable marriage problem

ABSTRACTThis paper proposes a shortlist-based bidirectional local search algorithm to find an approximate solution for either the egalitarian or the sex-equal matching of the stable marriage problem. Our approach simultaneously searches forward from the man-optimal matching and backwards from the woman-optimal matching until the search frontiers meet. By using a shortlist-based breakmarriage strategy to rapidly generate all the stable neighbour matchings of all k-best stable matchings, the forward local search finds the solutions while moving towards the woman-optimal matching and the backward local search finds the solutions while moving towards the man-optimal matching. The experiments demonstrate that our proposed algorithm is efficient for finding an approximate solution to the egalitarian or sex-equal matching of the stable marriage problem.

Bidirectional implementation of Markov/CCMT for dynamic reliability analysis with application to digital I&C systems

The paper presents a framework of Markov/CCMT search engine platform for dynamic system reliability modeling and analysis. The search engine platform implemented with a versatile Markov/CCMT algorithm that supports multiple search modes including forward-tracing, backtracking and bidirectional search. The scalability and practicality of the proposed bidirectional search algorithm is demonstrated with a Boiling Water Reactor feedwater control system. The comparisons of multiway search analysis indicate that the complete and optimal solutions can be assured in the implementation of Markov/CCMT algorithm using breadth first search. The bidirectional search algorithm implemented with forward-backward integration by allowing the use of pruning criterion is well supported for search efficiency improvement.

Reduction of the asymptotic complexity of the assignment problem

The assignment problem belongs to an extensive class of managerial decision-making tasks. One of the urgent tasks related to this class is the problem of effectively combining various streams (resources). The main purpose of the method modifications is to reduce the asymptotic complexity and to consider the specifics of the subject area. The article describes the solution of the assignment problem on a bipartite sparse graph. The main purpose of the method modifications is to reduce the asymptotic complexity and to consider the specifics of the subject area. A modification of the Hungarian method is proposed. The method is based on the successive shortest path algorithm and Dijkstra’s bidirectional search algorithm. The modification led to a decrease in the asymptotic complexity of the problem.

Inverse kinematics of mobile manipulator using bidirectional particle swarm optimization by manipulator decoupling

This paper presents the solution to inverse kinematics of a mobile manipulator operating in both obstacle free environment and cluttered workspace. This work conceives an optimization problem using bidirectional particle swarm optimization method to obtain the inverse kinematics solution of the mobile manipulator. The bidirectional search algorithm helps to search for the solution faster than conventional unidirectional search. The novel manipulator decoupling technique proposed in this paper enables the system to carry out bidirectional search to find inverse kinematics. Collision detection and obstacle avoidance tasks are undertaken using the algorithm proposed in this work to avoid line type obstacles. A multi-objective optimization problem is formulated with minimum total joint movement as another objective function and collision avoidance as constraint. Numerical are experiments performed on a four degree of freedom manipulator mounted over a mobile base to illustrate the efficacy of the proposed method.