scholarly journals Optimal navigation strategy of active Brownian particles in target-search problems

2021 ◽  
Vol 155 (8) ◽  
pp. 084901
Author(s):  
Luigi Zanovello ◽  
Pietro Faccioli ◽  
Thomas Franosch ◽  
Michele Caraglio
Keyword(s):  
Search Problems ◽  
Target Search ◽  
Brownian Particles ◽  
Navigation Strategy

Using multiple searchers in constrained-path, moving-target search problems

1996 ◽  
Vol 43 (4) ◽  
pp. 463-480 ◽  
Author(s):  
Robert F. Dell ◽  
James N. Eagle ◽  
Gustavo Henrique Alves Martins ◽  
Almir Garnier Santos
Keyword(s):  
Moving Target ◽  
Search Problems ◽  
Target Search ◽  
Moving Target Search

Trapping of Brownian particles by random traps: destruction of the fluctuation slow-down

1998 ◽  
Vol 77 (5) ◽  
pp. 1277-1281
Author(s):  
Alexander M. Berezhkovskii, Veaceslav Zaloj,
Keyword(s):  
Brownian Particles ◽  
Random Traps

The Relation Between Navigation Strategy and Associative Memory

2014 ◽  
Author(s):  
Chi Ngo ◽  
Nora Newcombe ◽  
Ingrid Olson ◽  
Steven Weisberg
Keyword(s):  
Associative Memory ◽  
Navigation Strategy

The system of convective thermals as a generalized ensemble of Brownian particles

2016 ◽  
Vol 186 (2) ◽  
pp. 113-124 ◽  
Author(s):  
Alexander N. Vulfson ◽  
Oleg O. Borodin
Keyword(s):  
Brownian Particles

Best Neighbor Heuristic Search for Finding Minimum Paths in Transportation Networks

1998 ◽  
Vol 1651 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Jeffrey L. Adler
Keyword(s):  
Heuristic Search ◽  
Transportation Networks ◽  
Transportation Network ◽  
Search Problems ◽  
A Algorithm ◽  
Running Time ◽  
Search Effort ◽  
Wide Range ◽  
Path Search ◽  
Sparse Networks

For a wide range of transportation network path search problems, the A* heuristic significantly reduces both search effort and running time when compared to basic label-setting algorithms. The motivation for this research was to determine if additional savings could be attained by further experimenting with refinements to the A* approach. We propose a best neighbor heuristic improvement to the A* algorithm that yields additional benefits by significantly reducing the search effort on sparse networks. The level of reduction in running time improves as the average outdegree of the network decreases and the number of paths sought increases.

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