shortest path
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Hala Khankhour ◽  
Otman Abdoun ◽  
Jâafar Abouchabaka

<span>This article presents a new approach of integrating parallelism into the genetic algorithm (GA), to solve the problem of routing in a large ad hoc network, the goal is to find the shortest path routing. Firstly, we fix the source and destination, and we use the variable-length chromosomes (routes) and their genes (nodes), in our work we have answered the following question: what is the better solution to find the shortest path: the sequential or parallel method?. All modern systems support simultaneous processes and threads, processes are instances of programs that generally run independently, for example, if you start a program, the operating system spawns a new process that runs parallel elements to other programs, within these processes, we can use threads to execute code simultaneously. Therefore, we can make the most of the available central processing unit (CPU) cores. Furthermore, the obtained results showed that our algorithm gives a much better quality of solutions. Thereafter, we propose an example of a network with 40 nodes, to study the difference between the sequential and parallel methods, then we increased the number of sensors to 100 nodes, to solve the problem of the shortest path in a large ad hoc network.</span>

2022 ◽  
Vol 11 (1) ◽  
pp. 66
Shenghua Xu ◽  
Yang Gu ◽  
Xiaoyan Li ◽  
Cai Chen ◽  
Yingyi Hu ◽  

The internal structure of buildings is becoming increasingly complex. Providing a scientific and reasonable evacuation route for trapped persons in a complex indoor environment is important for reducing casualties and property losses. In emergency and disaster relief environments, indoor path planning has great uncertainty and higher safety requirements. Q-learning is a value-based reinforcement learning algorithm that can complete path planning tasks through autonomous learning without establishing mathematical models and environmental maps. Therefore, we propose an indoor emergency path planning method based on the Q-learning optimization algorithm. First, a grid environment model is established. The discount rate of the exploration factor is used to optimize the Q-learning algorithm, and the exploration factor in the ε-greedy strategy is dynamically adjusted before selecting random actions to accelerate the convergence of the Q-learning algorithm in a large-scale grid environment. An indoor emergency path planning experiment based on the Q-learning optimization algorithm was carried out using simulated data and real indoor environment data. The proposed Q-learning optimization algorithm basically converges after 500 iterative learning rounds, which is nearly 2000 rounds higher than the convergence rate of the Q-learning algorithm. The SASRA algorithm has no obvious convergence trend in 5000 iterations of learning. The results show that the proposed Q-learning optimization algorithm is superior to the SARSA algorithm and the classic Q-learning algorithm in terms of solving time and convergence speed when planning the shortest path in a grid environment. The convergence speed of the proposed Q- learning optimization algorithm is approximately five times faster than that of the classic Q- learning algorithm. The proposed Q-learning optimization algorithm in the grid environment can successfully plan the shortest path to avoid obstacle areas in a short time.

2022 ◽  
Vol 0 (0) ◽  
Rahul Deo Shukla ◽  
Ajay Pratap ◽  
Raghuraj Singh Suryavanshi

Abstract Optical packet switching has gained lot of momentum in last decade due to the advantages of optical fiber over copper cables. Optical switching is beneficial in optical networks which form connections of links and switching nodes. In these high speed networks minimum delay and high throughput are two important parameters which are considered. To minimize network delay shortest path algorithm is used for route selections. In previous studies while choosing shortest path distance among various nodes is considered. In this work we have shown that it is necessary to consider both distance and number of hops while choosing path from source to destination to minimize power per bit used for the transmission.

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Chaozhi Tang ◽  
Yuling Zhang ◽  
Zihan Zhai ◽  
Xiaofeng Zhu ◽  
Chaowei Wang ◽  

In recent years, functional magnetic resonance technology has discovered that abnormal connections in different brain regions of the brain may serve as the pathophysiological mechanism of mental illness. Exploring the mechanism of information flow and integration between different brain regions is of great significance for understanding the pathophysiological mechanism of mental illness. This article aims to analyze the mechanism of depression by comparing human brain images of normal people and patients with depression and conduct research. Fluoxetine, a selective 5-HT reuptake inhibitor (SSRI) widely used in clinical practice, can selectively inhibit 5-HT transporter and block the reuptake of 5-HT by the presynaptic membrane. The effect of 5-HT is prolonged and increased, thereby producing antidepressant effects. It has low affinity for adrenergic, histaminergic, and cholinergic receptors and has a weaker effect, resulting in fewer adverse reactions. This paper uses the comparative experiment method and the Welch method and uses the average shortest path length L to describe the average value of the shortest path length between two nodes in the network. Attention refers to the ability of a person’s mental activity to point and to concentrate on something. Sustained attention means that attention is kept on a certain cognitive object or activity for a certain period of time, which is also called the stability of attention. The research on attention of depression patients generally focuses on continuous attention, and the results obtained show inconsistencies. Most studies have shown that the sustained attention of the depression group is significantly worse than that of the healthy control group. An overview of magnetic resonance imaging technology and an analysis of depression based on resting state were carried out. The key brain areas of the sample core network were scanned, and the ALFF results were analyzed. The data showed that the severity of depression in the depression group was negatively correlated with the ReHo value in the posterior left cerebellum ( P = 0.010 ). The sense of despair was negatively correlated with the ReHo value in the posterior right cerebellum ( P = 0.013 ). The diurnal variation was negatively correlated with the ReHo value of the left ring ( P = 0.014 ). It was positively correlated with the ReHo value of the left ventricle ( P = 0.048 ). This experiment has better completed the research on the mechanism of depression by analyzing the functional images of patients with depression and normal human brain.

2022 ◽  
Vol 23 (1) ◽  
Juan J. Lastra-Díaz ◽  
Alicia Lara-Clares ◽  
Ana Garcia-Serrano

Abstract Background Ontology-based semantic similarity measures based on SNOMED-CT, MeSH, and Gene Ontology are being extensively used in many applications in biomedical text mining and genomics respectively, which has encouraged the development of semantic measures libraries based on the aforementioned ontologies. However, current state-of-the-art semantic measures libraries have some performance and scalability drawbacks derived from their ontology representations based on relational databases, or naive in-memory graph representations. Likewise, a recent reproducible survey on word similarity shows that one hybrid IC-based measure which integrates a shortest-path computation sets the state of the art in the family of ontology-based semantic measures. However, the lack of an efficient shortest-path algorithm for their real-time computation prevents both their practical use in any application and the use of any other path-based semantic similarity measure. Results To bridge the two aforementioned gaps, this work introduces for the first time an updated version of the HESML Java software library especially designed for the biomedical domain, which implements the most efficient and scalable ontology representation reported in the literature, together with a new method for the approximation of the Dijkstra’s algorithm for taxonomies, called Ancestors-based Shortest-Path Length (AncSPL), which allows the real-time computation of any path-based semantic similarity measure. Conclusions We introduce a set of reproducible benchmarks showing that HESML outperforms by several orders of magnitude the current state-of-the-art libraries in the three aforementioned biomedical ontologies, as well as the real-time performance and approximation quality of the new AncSPL shortest-path algorithm. Likewise, we show that AncSPL linearly scales regarding the dimension of the common ancestor subgraph regardless of the ontology size. Path-based measures based on the new AncSPL algorithm are up to six orders of magnitude faster than their exact implementation in large ontologies like SNOMED-CT and GO. Finally, we provide a detailed reproducibility protocol and dataset as supplementary material to allow the exact replication of all our experiments and results.

Algorithmica ◽  
2022 ◽  
José Arturo Gil ◽  
Simone Santini

AbstractIn this paper we study regular expression matching in cases in which the identity of the symbols received is subject to uncertainty. We develop a model of symbol emission and uses a modification of the shortest path algorithm to find optimal matches on the Cartesian Graph of an expression provided that the input is a finite list. In the case of infinite streams, we show that the problem is in general undecidable but, if each symbols is received with probability 0 infinitely often, then with probability 1 the problem is decidable.

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Eun Hak Lee ◽  
Kyoungtae Kim ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim ◽  
Shin-Hyung Cho

As the mode share of the subway in Seoul has increased, the estimation of passenger travel routes has become a crucial issue to identify the congestion sections in the subway network. This paper aims to estimate the travel train of subway passengers in Seoul. The alternative routes are generated based on the train log data. The travel route is then estimated by the empirical cumulative distribution functions (ECDFs) of access time, egress time, and transfer time. The train choice probability is estimated for alternative train combinations and the train combination with the highest probability is assigned to the subway passenger. The estimated result is validated using the transfer gate data which are recorded on private subway lines. The result showed that the accuracy of the estimated travel train is shown to be 95.6%. The choice ratios for no-transfer, one-transfer, two-transfer, three-transfer, and four-transfer trips are estimated to be 53.9%, 37.7%, 6.5%, 1.5%, and 0.4%, respectively. Regarding the practical application, the passenger kilometers by lines are estimated with the travel route estimation of the whole network. As results of the passenger kilometer calculation, the passenger kilometer of the proposed algorithm is estimated to be 88,314 million passenger kilometer. The proposed algorithm estimates the passenger kilometer about 13% higher than the shortest path algorithm. This result implies that the passengers do not always prefer the shortest path and detour about 13% for their convenience.

2022 ◽  
pp. 1-12
Daniel Bahrdt ◽  
Stefan Funke ◽  
Sokol Makolli ◽  
Claudius Proissl

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