A Survey and Classification of A* Based Best-First Heuristic Search Algorithms

Rios, Luis Henrique Oliveira; Chaimowicz, Luiz

doi:10.1007/978-3-642-16138-4_26

Cited by 26 publications

(13 citation statements)

References 19 publications

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“…As mentioned already above, one of our core contributions on the algorithmic side is an A * algorithm for solving the JSOCMSR problem. A * is one of the standard algorithms in the field of Artificial Intelligence for path planning and, more generally, finding shortest paths in possibly huge graphs [10,24]. In particular, our approach belongs to the class of Anytime A * algorithms, which are complete but also can be stopped at almost any time and are then likely to yield a reasonable approximate solution.…”

Section: Related Workmentioning

confidence: 99%

Job sequencing with one common and multiple secondary resources: An A⁎/Beam Search based anytime algorithm

Horn

Raidl

Blum

2019

Artificial Intelligence

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We consider a sequencing problem that arises, for example, in the context of scheduling patients in particle therapy facilities for cancer treatment. A set of non-preemptive jobs needs to be scheduled, where each job requires two resources: (1) a common resource that is shared by all jobs and (2) a secondary resource, which is shared with only a subset of the other jobs. While the common resource is only required for a part of the job's processing time, the secondary resource is required for the whole duration. The objective is to minimize the makespan. First we show that the tackled problem is NP-hard and provide three different lower bounds for the makespan. These lower bounds are then exploited in a greedy construction heuristic and a novel exact anytime A * algorithm, which uses an advanced diving mechanism based on Beam Search and Local Search to find good heuristic solutions early. For comparison we also provide a basic Constraint Programming model solved with the ILOG CP optimizer. An extensive experimental evaluation on two types of problem instances shows that the approach works even for large instances with up to 2000 jobs extremely well. It typically yields either optimal solutions or solutions with an optimality gap of less than 1%.

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Section: Related Workmentioning

confidence: 99%

Job sequencing with one common and multiple secondary resources: An A⁎/Beam Search based anytime algorithm

Horn

Raidl

Blum

2019

Artificial Intelligence

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“…For further analysis of general performance improvements on search algorithms, please refer to Rios & Chaimowicz [7].…”

Section: Related Workmentioning

confidence: 99%

Speeding Up Learning in Real-Time Search through Parallel Computing

Marques

Chaimowicz

Ferreira

2011

2011 23rd International Symposium on Computer Architecture and High Performance Computing

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Real-time search algorithms solve the problem of path planning, regardless the size and complexity of the maps, and the massive presence of entities in the same environment. In such methods, the learning step aims to avoid local minima and improve the results for future searches, ensuring the convergence to the optimal path when the same planning task is solved repeatedly. However, performing search in a limited area due to real-time constraints makes the run to convergence a lengthy process. In this work, we present a parallelization strategy that aims to reduce the time to convergence, maintaining the real-time properties of the search. The parallelization technique consists on using auxiliary searches without the real-time restrictions present in the main search. In addition, the same learning is shared by all searches. The empirical evaluation shows that even with the additional cost required to coordinate the auxiliary searches, the reduction in time to convergence is significant, showing gains from searches occurring in environments with fewer local minima to larger searches on complex maps, where performance improvement is even better.

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“…A* algorithm is powerful in computing the shortest path of a node to another node. Its heuristic approach makes it better than other shortest path algorithm [22]- [24]. With this, a* algorithm is a good choice to used in a mobile application that uses GIS.…”

Section: Introductionmentioning

confidence: 99%

Implementation of tourism circuit concept in an android-based tourist navigation application through A* algorithm

Asor

Catedrilla²,

Leon³

et al. 2021

IJEECS

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Circuit tourism allows for inclusive growth of tourism since it allows for multi-site visits. Normally three or more tourist attractions are linked together in a tour of the tourist’s choosing. The main objective of this study is to develop an android-based application for searching tourist spots in Los Baños, Laguna, Philippines that is also capable of recommending succeeding places to visit using A* algorithm. Upon development of the application, this paper proves that A* algorithm can be used to recommend another tourist spot by analyzing the distance of the tourist spots near the user, and the most convenient routes through which they can be reached. Based on backward and forward compatibility testing, the application is operating system friendly, since it can be used in lower versions of android. Further, it is shown in the users’ evaluation that the application is acceptable to the locals of Los Baños, Laguna, Philippines. The application also contributes to advertising other tourist spots in the said municipality, specially those who were not so popular to the tourists.

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A Survey and Classification of A* Based Best-First Heuristic Search Algorithms

Cited by 26 publications

References 19 publications

Job sequencing with one common and multiple secondary resources: An A⁎/Beam Search based anytime algorithm

Job sequencing with one common and multiple secondary resources: An A⁎/Beam Search based anytime algorithm

Speeding Up Learning in Real-Time Search through Parallel Computing

Implementation of tourism circuit concept in an android-based tourist navigation application through A* algorithm

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