An anticipation mechanism for the shortest path problem based on<i>Physarum polycephalum</i>

Wang, Qing; Lu, Xi; Deng, Yong; Chu, Xiao

doi:10.1080/03081079.2014.997532

Cited by 11 publications

(8 citation statements)

References 30 publications

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“…We recall that the original Physarum solver can be applied only on undirected graphs. A generalization to directed graphs was given in [14], which essentially replaces Eq. (6) by the following:…”

Section: Variants Of the Physarum Modelmentioning

confidence: 99%

“…The mathematical model of Physarum was defined in [12,13]. The Physarum model was extended in [14] for solving the shortest path tree problem in directed graphs. Recently, Zhang et al [15] and Gao et al [16] accelerated Physarum solver in order to solve single-source, single-target shortest path problems efficiently.…”

Section: Introductionmentioning

confidence: 99%

“…The potential difference between node i and node j corresponds to the difference of the pressures (p i − p j ). Therefore, a mapping from the Physarum network to an electrical circuit is constructed by Eqs (14),(15),. and (16):…”

mentioning

confidence: 99%

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A hybrid single-source shortest path algorithm

Arslan¹,

Manguoğlu²

2019

Turk J Elec Eng & Comp Sci

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The single-source shortest path problem arises in many applications, such as roads, social applications, and computer networks. Finding the shortest path is challenging, especially for graphs that contain a large number of vertices and edges. In this work, we propose a novel hybrid method that first sparsifies a given graph by removing most edges that cannot form the shortest path tree and then applies a classical shortest path algorithm to the sparser graph. Removing all the edges that cannot form the shortest path tree would be expensive since it is equivalent to solving the original problem. Therefore, we propose an iterative bioinspired algorithm, namely the Physarum algorithm, as the first stage to sparsify the graph. We prove that the resulting sparser graph always contains the shortest path tree of the original graph. Next, a state-of-the-art algorithm such as Dijkstra's is applied to find the single-source shortest path on the resulting graph. The proposed method is therefore a two-stage hybrid algorithm and it computes the single-source shortest path exactly. We compare the accuracy and solution time of the proposed hybrid method against state-of-the-art implementation of Dijkstra's algorithm and the BFS algorithm on directed weighted and unweighted graphs, respectively, as a baseline. The results show that the proposed hybrid method achieves a significant speed improvement compared to the baseline.

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Section: Variants Of the Physarum Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid single-source shortest path algorithm

Arslan¹,

Manguoğlu²

2019

Turk J Elec Eng & Comp Sci

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“…On the other hand, the proposed preconditioned iterative method requires O(|E|) time per iteration to approach the solution [28]. In the Physarum-related algorithms [11,12,14,29], a direct solver was used to solve such systems and the direct solver is infeasible when the graph size is large. Therefore, the results of these algorithms are shown only using small graphs.…”

Section: Algorithm 2 Dynamic Physarum Solvermentioning

confidence: 99%

Dynamic Physarum Solver: a bio-inspired shortest path method of dynamically changing graphs

Arslan¹

2019

Turk J Elec Eng & Comp Sci

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In dynamic graphs, edge weights of the graph change with time and solving the shortest path problem in such graphs is an important real-world problem. The studies in the literature require excessive computational time for computing the dynamic shortest path since determining changing edge weights is difficult especially when the graph size becomes large. In this paper, we propose a dynamic bio-inspired algorithm for finding the dynamic shortest path for large graphs based on Physarum Solver, which is a shortest path algorithm for static graphs. The proposed method is evaluated using three different large graph models representing diverse real-life applications. The effect of changing edge weights on the solution time is evaluated for each graph model separately and compared against ∆-stepping, which is the most representative implementation of Dijkstra’s algorithm. Experimental results show that the proposed method easily adapts edge weight changes and computes the dynamic shortest path efficiently.

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“…However, the more common type is fixation shorter route from the single node for all nodes which lie on the graph. Dijkstra's algorithm applications are the following: i) it is applied as a subroutine in excellent algorithms; ii) Dijkstra's algorithm is used for reducing hops number from computer to another and for enhancement to transfer the file between the computers inside the network lead to occupy all time [1]; iii) by Dijkstra's algorithm, it could provide both the address of the origin node and that of the destination node for the remote server to determine the shortest route [4]; iv) Dijkstra's algorithm is used as high efficiency in traffic information systems wherever it calculates the shorter road; and v) the algorithm uses Google Maps for determining the shortest route between 2 points [29]. Dijkstra's algorithm disadvantages are: i) It spends extended time in the necessary resources.…”

Section: Introductionmentioning

confidence: 99%

Flight-schedule using Dijkstra's algorithm with comparison of routes findings

Salem

Mijwil

Abdulqader

et al. 2022

IJECE

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<span>The Dijkstra algorithm, also termed the shortest-route algorithm, is a model that is categorized within the search algorithms. Its purpose is to discover the shortest-route, from the beginning node (origin node) to any node on the tracks, and is applied to both directional and undirected graphs. However, all edges must have non-negative values. The problem of organizing inter-city flights is one of the most important challenges facing airplanes and how to transport passengers and commercial goods between large cities in less time and at a lower cost. In this paper, the authors implement the Dijkstra algorithm to solve this complex problem and also to update it to see the shortest-route from the origin node (city) to the destination node (other cities) in less time and cost for flights using simulation environment. Such as, when graph nodes describe cities and edge route costs represent driving distances between cities that are linked with the direct road. The experimental results show the ability of the simulation to locate the most cost-effective route in the shortest possible time (seconds), as the test achieved 95% to find the suitable route for flights in the shortest possible time and whatever the number of cities on the tracks application.</span>

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An anticipation mechanism for the shortest path problem based onPhysarum polycephalum

Cited by 11 publications

References 30 publications

A hybrid single-source shortest path algorithm

A hybrid single-source shortest path algorithm

Dynamic Physarum Solver: a bio-inspired shortest path method of dynamically changing graphs

Flight-schedule using Dijkstra's algorithm with comparison of routes findings

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