Applications of particle swarm optimization in the railway domain

Wu, Qing; Cole, Colin; McSweeney, Tim

doi:10.1080/23248378.2016.1179599

Cited by 50 publications

(24 citation statements)

References 60 publications

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“…Liao et al 33 adopted a PSO to detect the damage of several kind of framework structures with a high accuracy. Wu et al, 34 Pau et al, 35 and other researchers also found the superiority of PSO in dealing with other problems. From Table 2 and Figure 6, we compared PSO algorithm with traditional training methods, BP, GA, MAGA, and SA approaches, the comparison results shown that the sensitivity, specificity, precision, accuracy, and F1 score of these methods in diagnosing and identifying chronic gingivitis are 75.45% ± 2.19%, 75.45% ± 2.27%, 75.50% ± 1.87%, 75.45% ± 1.63%, and 75.44% ± 1.66%, respectively.…”

Section: Discussionmentioning

confidence: 89%

Gingivitis identification via multichannel gray‐level co‐occurrence matrix and particle swarm optimization neural network

Wen

Jiang

Sun

et al. 2019

Int J Imaging Syst Tech

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The oral maintenance of patients with periodontal disease mainly depends on clinical examination. However, insufficient number of medical workers cannot carry out detailed oral health education for a large number of patients within limited time and provide these patients with proper and effective oral health nursing methods. In this research, our study put forward a new Artificial Intelligence (AI) based method to diagnose chronic gingivitis, which is based on multichannel gray-levelco-occurrence matrix (MGLCM) and particle swarm optimization neural network(PSONN). Meanwhile, different training algorithms were used as comparison groups.The data set contains 800 images: 400 chronic gingivitis images and 400 healthy gingiva images. The results certify that the specificity, sensitivity, precision, accuracy and F1 Score of MGLCM (PSONN as a classifier) method is 78.17%, 78.23%, 78.24% ,78.20%and 78.17%, respectively. The association of MGLCM and PSONN is more accurate and efficient than approaches: NBC, WN+SVM,ELM and CLAHE+ELM. K E Y W O R D Sartificial neural network, gingivitis identification, multichannel gray-level co-occurrence matrix, particle swarm optimization, pattern recognition

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Section: Discussionmentioning

confidence: 89%

Gingivitis identification via multichannel gray‐level co‐occurrence matrix and particle swarm optimization neural network

Wen

Jiang

Sun

et al. 2019

Int J Imaging Syst Tech

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“…More information regarding MPI can be accessed via the online book of Barney [34]. In railway research, the MPI has found many applications [7,[11][12][13][21][22][23]31,[35][36][37][38][39][40][41][42][43]; it is one of the most widely used parallel computing enabling techniques. In the authors' opinion, MPI is very suitable for parallel co-simulations and parallel optimisations, as presented in References [31] and [42] respectively, since they have very independent parallelised computing tasks.…”

Section: Mpimentioning

confidence: 99%

“…In addition to these applications, parallel GA has been used to optimise railway vehicle aerodynamics [77], truss structures [58], shock absorber distribution for railway tunnels [36], draft gear designs [37][38][39] and wedge suspension designs [42]. Parallel PSO has been used for optimisations of passenger traffic prediction [69], draft gear designs [37][38][39] and wedge suspension designs [42]. Evolutionary Algorithm optimisation has been used for optimisations of railway vehicle structures [33], and railway vehicle dynamics design [12].…”

Section: Iterative Optimisationmentioning

confidence: 99%

Parallel computing in railway research

Spiryagin

Cole

et al. 2018

International Journal of Rail Transportation

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Available computing power for researchers has been increasing exponentially over the last decade. Parallel computing is possibly the best way to harness computing power provided by multiple computing units. This paper reviews parallel computing applications in railway research as well as the enabling techniques used for the purpose. Nine enabling techniques were reviewed and Message Passing Interface, Domain Decomposition and Hadoop & Apache are the top three most widely used enabling techniques. Seven major application topics were reviewed and iterative optimisations, continuous dynamics and data & signal analysis are the most widely reported applications. The reasons why these applications are suitable for parallel computing were discussed as well as the suitability of various enabling techniques for different applications. Computing time speed-ups that were reported from these applications were summarised. The challenges for applying parallel computing for railway research are discussed. ARTICLE HISTORY

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“…The main problem to be solved is how to find-quickly and efficiently-the source of food. An extensive list of other algorithms inspired by animals (including swarm) intelligence may be found, e.g., in [6].Electrical engineering has started to adopt population-based algorithms, and they have been applied to solve diverse problems such as, e.g., the wind power station energy storage problem [7], the location of capacitor banks in the distribution system [8], the optimized placing of charging stations in the distribution system [9], the optimization of the voltage profile in the distribution system with distributed electricity sources [10], the assessment of 3-phase induction motor parameters [11], some railway transport issues [12], and current balancing in the railway system [13].We will discuss two technical problems in this paper; to both problems we have applied the ant colony algorithm (ACO) as a solution. One problem is improving the "quality" of the output voltage of the rectifier transformer used in supply of DC tram traction lines; the other problem is equalizing the distribution of loads between secondary windings and rectifiers of the rectifier-transformer set.…”

mentioning

confidence: 99%

“…Electrical engineering has started to adopt population-based algorithms, and they have been applied to solve diverse problems such as, e.g., the wind power station energy storage problem [7], the location of capacitor banks in the distribution system [8], the optimized placing of charging stations in the distribution system [9], the optimization of the voltage profile in the distribution system with distributed electricity sources [10], the assessment of 3-phase induction motor parameters [11], some railway transport issues [12], and current balancing in the railway system [13].…”

mentioning

confidence: 99%

The Application of Ant Colony Algorithms to Improving the Operation of Traction Rectifier Transformers

2019

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In this paper, we discuss a technical issue occurring in electric traction. Tram traction may use DC voltage; this is obtained by rectifying AC voltage supplied by the power grid. In the simplest design-one which is commonly used-only diode uncontrolled rectifiers are used. The rectified voltage is not smooth; it always contains a pulsating (AC) component. The amount of pulsation varies. It depends, among other factors, on the design of the transformer-rectifier set. In the 12-pulse system, we use a three-winding transformer, consisting of one primary winding and two secondary windings: one is delta-connected and the other is star-connected. The unbalance of secondary windings is an extra factor increasing the pulsation of DC voltage. To equalize secondary side voltages, a tap changer may be used. The setting of the tap changer is the question resolved in this paper; it is optimized by application of the ACO (ant colony optimization algorithm). We have analyzed different supply voltage variants, and in particular, distorted voltage containing 5th and 7th harmonics. The results of ant colony optimization application are described in this paper. The General Issue: Applying the Swarm Intelligence Algorithm to Optimization ProblemsEngineering nowadays often requires the numerical optimization of many and diverse problems. New optimization algorithms are invented all the time; among them, we may distinguish different types of metaheuristic algorithms. The heuristic approach may be termed practical; it may not be optimal in the mathematical sense of the word, but it is quite often sufficient enough from engineering viewpoint. Usually, its main advantage lies in its speed-a satisfactory solution may be found more quickly than in cases when more elaborate numerical algorithms are used.Among the metaheuristic algorithms, we find lots of procedures based on swarm intelligence. They are inspired by the behaviour of a large population of individuals belonging to one species. The performance of such a group seems to be co-ordinated, even though we cannot identify a leader, i.e., some sort of central control. Every individual in a swarm sends information and receives information from other members of the group, in a way that is peculiar to a given species. It must be stressed that information on the optimum way of acting (e.g., the shortest way to food source) is also exchanged. The entire swarm (system) progresses dynamically. When some individuals find the most effective course of action, the others (receivers of this information) concentrate in the regions of search areas that have proven to be most successful from the viewpoint of the desired goal (e.g., the source of food). Thus, the solution to the problem is attained [1]. Apart from searching for food, other behaviour patterns such as communication, task allotment, decision making, the localization of HQ, choice of partners, mating, and propagation have been the source of inspiration for population algorithms.Ant colony optimization [2][3][4][5] is one such algorithm. T...

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Applications of particle swarm optimization in the railway domain

Cited by 50 publications

References 60 publications

Gingivitis identification via multichannel gray‐level co‐occurrence matrix and particle swarm optimization neural network

Gingivitis identification via multichannel gray‐level co‐occurrence matrix and particle swarm optimization neural network

Parallel computing in railway research

The Application of Ant Colony Algorithms to Improving the Operation of Traction Rectifier Transformers

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