An optimal bidirectional search algorithm

1994 ◽  
pp. 394-394 ◽  
Author(s):  
Jürgen Eckerle
Keyword(s):  
Search Algorithm ◽  
Bidirectional Search

scholarly journals A Unifying View on Individual Bounds and Heuristic Inaccuracies in Bidirectional Search

2020 ◽  
Vol 34 (03) ◽  
pp. 2327-2334
Author(s):  
Vidal Alcázar ◽  
Pat Riddle ◽  
Mike Barley
Keyword(s):  
Lower Bound ◽  
Heuristic Search ◽  
Search Algorithm ◽  
Substantial Improvement ◽  
Full Potential ◽  
The Past ◽  
Heuristic Search Algorithms ◽  
Bidirectional Search ◽  
Order Of Magnitude ◽  
The Cost

In the past few years, new very successful bidirectional heuristic search algorithms have been proposed. Their key novelty is a lower bound on the cost of a solution that includes information from the g values in both directions. Kaindl and Kainz (1997) proposed measuring how inaccurate a heuristic is while expanding nodes in the opposite direction, and using this information to raise the f value of the evaluated nodes. However, this comes with a set of disadvantages and remains yet to be exploited to its full potential. Additionally, Sadhukhan (2013) presented BAE∗, a bidirectional best-first search algorithm based on the accumulated heuristic inaccuracy along a path. However, no complete comparison in regards to other bidirectional algorithms has yet been done, neither theoretical nor empirical. In this paper we define individual bounds within the lower-bound framework and show how both Kaindl and Kainz's and Sadhukhan's methods can be generalized thus creating new bounds. This overcomes previous shortcomings and allows newer algorithms to benefit from these techniques as well. Experimental results show a substantial improvement, up to an order of magnitude in the number of necessarily-expanded nodes compared to state-of-the-art near-optimal algorithms in common benchmarks.

Research on Vehicle Path Planning Base on Intelligent Transport System

2014 ◽  
Vol 513-517 ◽  
pp. 1822-1825
Author(s):  
Rui Wang ◽  
Na Wang
Keyword(s):  
Time Complexity ◽  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Algorithm Design ◽  
Intelligent Transport System ◽  
Dijkstra Algorithm ◽  
Intelligent Transport ◽  
Bidirectional Search ◽  
Search Speed ◽  
Vehicle Path

Typical shortest path is Dijkstra algorithm, its time complexity is O (n2). A map of the citys road network has many nodes, if we use the Dijkstra algorithm, the time complexity of the algorithm is too high and the speed of resolution this problem is slow. In order to change this situation, we are discussed from the aspects of algorithm design, puts forward the improved bidirectional search algorithm. Practice has proved that, the improved algorithm can improve the search speed and it suitable for intelligent transportation system.

Iterative Deepening Dynamically Improved Bounds Bidirectional Search

2021 ◽  
Author(s):  
John A. Pavlik ◽  
Edward C. Sewell ◽  
Sheldon H. Jacobson
Keyword(s):  
Shortest Path ◽  
Operations Research ◽  
Search Algorithm ◽  
Desktop Computer ◽  
Shortest Path Problems ◽  
Tile Problem ◽  
Bidirectional Search ◽  
Rubik's Cube ◽  
Rubik’S Cube ◽  
Core Problem

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.

scholarly journals Enriching Non-Parametric Bidirectional Search Algorithms

2019 ◽  
Vol 33 ◽  
pp. 2379-2386
Author(s):  
Shahaf S. Shperberg ◽  
Ariel Felner ◽  
Nathan R. Sturtevant ◽  
Solomon E. Shimony ◽  
Avi Hayoun
Keyword(s):  
Search Algorithm ◽  
Search Algorithms ◽  
Optimal Solutions ◽  
Worst Case ◽  
Bidirectional Search ◽  
Algorithmic Framework ◽  
Special Case ◽  
Non Parametric

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.

scholarly journals Recognition of Blocking Categories for UWB Positioning in Complex Indoor Environment

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4178
Author(s):  
Yaguang Kong ◽  
Chuang Li ◽  
Zhangping Chen ◽  
Xiaodong Zhao
Keyword(s):  
Spatial Information ◽  
Recognition Accuracy ◽  
Search Algorithm ◽  
Computational Cost ◽  
Space Perception ◽  
Wide Band ◽  
Line Of Sight ◽  
Support Vector ◽  
Indoor Space ◽  
Bidirectional Search

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.

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