A metric space approach to the specification of the heuristic function for the A* algorithm

1994 ◽  
Vol 24 (1) ◽  
pp. 159-166 ◽  
Author(s):  
K.M. Passino ◽  
P.J. Antsaklis
Keyword(s):  
Metric Space ◽  
Heuristic Function ◽  
A Algorithm ◽  
Space Approach

scholarly journals A Metric Space Approach on the Molecular vs. Chemical Similarity of Some Analgesic and Euphoric Compounds

2019 ◽  
Author(s):  
Demetrios Xenides ◽  
Dionisia Fostiropoulou ◽  
Dimitrios S Vlachos
Keyword(s):  
Metric Space ◽  
Clinical Findings ◽  
Space Theory ◽  
Chemical Similarity ◽  
Analgesic Drugs ◽  
Structural Indices ◽  
Space Approach

<p>There is a relentless effort on gaining information on the reason why some compounds could cause similar effects though they are or not structural similar. That is the chemical similarity that plays an equally important role and we approach it via metric space theory on a set of analgesic drugs and euphoric compounds. The findings of the present study are in agreement to these obtained via traditional structural indices moreover are in accord with clinical findings.</p>

scholarly journals Analyzing Tie-Breaking Strategies for the A* Algorithm

2018 ◽  
Author(s):  
Augusto B. Corrêa ◽  
André G. Pereira ◽  
Marcus Ritt
Keyword(s):  
State Space ◽  
Standard Method ◽  
State Of The Art ◽  
The State ◽  
Heuristic Function ◽  
A Algorithm ◽  
Minimum Number ◽  
Previous State

For a given state space and admissible heuristic function h there is always a tie-breaking strategy for which A* expands the minimum number of states [Dechter and Pearl, 1985]. We say that these strategies have optimal expansion. Although such a strategy always exists it may depend on the instance, and we currently do not know a tie-breaker that always guarantees optimal expansion. In this paper, we study tie-breaking strategies for A*. We analyze common strategies from the literature and prove that they do not have optimal expansion. We propose a novel tie-breaking strategy using cost adaptation that has always optimal expansion. We experimentally analyze the performance of A* using several tie-breaking strategies on domains from the IPC and zero-cost domains. Our best strategy solves significantly more instances than the standard method in the literature and more than the previous state-of-the-art strategy. Our analysis improves the understanding of how to develop effective tie-breaking strategies and our results also improve the state-of-the-art of tie-breaking strategies for A*.

A Note on the Connection Between the Primal-Dual and the A* Algorithm

2010 ◽  
Vol 1 (1) ◽  
pp. 73-85 ◽  
Author(s):  
Xugang Ye ◽  
Shih-Ping Han ◽  
Anhua Lin
Keyword(s):  
Network Flow ◽  
Feasible Solution ◽  
Weighted Graph ◽  
Dual Algorithm ◽  
Heuristic Function ◽  
Network Flow Problems ◽  
A Algorithm ◽  
Flow Problems ◽  
Primal Dual ◽  
Initial Feasible Solution

The primal-dual algorithm for linear programming is very effective for solving network flow problems. For the method to work, an initial feasible solution to the dual is required. In this article, we show that, for the shortest path problem in a positively weighted graph equipped with a consistent heuristic function, the primal-dual algorithm will become the well-known A* algorithm if a special initial feasible solution to the dual is chosen. We also show how the improvements of the dual objective are related to the A* iterations.

Path Planning for Ground Simulation Objiect Based on A* Algorithm

2012 ◽  
Vol 229-231 ◽  
pp. 2019-2024 ◽  
Author(s):  
Zhi Qiang Zhao ◽  
Zhi Hua Liu ◽  
Jia Xin Hao
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Optimal Path ◽  
Planning Method ◽  
Heuristic Function ◽  
A Algorithm

In the process of ground simulation object maneuver simulation in large-scale operation simulation, an efficient path planning method based on A*algorithm is proposed. By means of introducing all kind of geography factors and security factors into heuristic function, the plan reaching method solves the problem of finding an optimal path under acquiring enemy's situation and terrain data. Experiment results show that it has effectively raised path planning speed of A* algorithm and the scheme is practical and feasible.

scholarly journals An Efficient and Robust Improved A* Algorithm for Path Planning

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2213
Author(s):  
Huanwei Wang ◽  
Xuyan Qi ◽  
Shangjie Lou ◽  
Jing Jing ◽  
Hongqi He ◽  
...  
Keyword(s):  
Path Planning ◽  
Function Optimization ◽  
Mobile Robot Navigation ◽  
Test Results ◽  
Heuristic Function ◽  
Excellent Performance ◽  
A Algorithm ◽  
Algorithm Efficiency ◽  
Bidirectional Search ◽  
Start Node

Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the conventional A* algorithm and the subsequent improved algorithms still have some limitations in terms of robustness and efficiency. These limitations include slow algorithm efficiency, weak robustness, and collisions when robots are traversing. In this paper, we propose an improved A*-based algorithm called EBHSA* algorithm. The EBHSA* algorithm introduces the expansion distance, bidirectional search, heuristic function optimization and smoothing into path planning. The expansion distance extends a certain distance from obstacles to improve path robustness by avoiding collisions. Bidirectional search is a strategy that searches for a path from the start node and from the goal node at the same time. Heuristic function optimization designs a new heuristic function to replace the traditional heuristic function. Smoothing improves path robustness by reducing the number of right-angle turns. Moreover, we carry out simulation tests with the EBHSA* algorithm, and the test results show that the EBHSA* algorithm has excellent performance in terms of robustness and efficiency. In addition, we transplant the EBHSA* algorithm to a robot to verify its effectiveness in the real world.

Sign in / Sign up

Export Citation Format

Share Document