Search results for: binary shuffled frog leaping algorithm

Authors: E. Younsi, H. Andriamboavonjy, A. David, S. Dokou, B. Lemrabet

Abstract:

Software and time optimization are very important factors in financial markets, which are competitive fields, and emergence of new computer tools further stresses the challenge. In this context, any improvement of technical indicators which generate a buy or sell signal is a major issue. Thus, many tools have been created to make them more effective. This worry about efficiency has been leading in present paper to seek best (and most innovative) way giving largest improvement in these indicators. The approach consists in attaching a signature to frequent market configurations by application of frequent patterns extraction method which is here most appropriate to optimize investment strategies. The goal of proposed trading algorithm is to find most accurate signatures using back testing procedure applied to technical indicators for improving their performance. The problem is then to determine the signatures which, combined with an indicator, outperform this indicator alone. To do this, the FP-Tree algorithm has been preferred, as it appears to be the most efficient algorithm to perform this task.

Keywords: quantitative analysis, back-testing, computational models, apriori algorithm, pattern recognition, data mining, FP-tree

Procedia PDF Downloads 363

Authors: R. B. Knudsen, O. T. Rasmussen, R. A. Alphinas

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The reason for conducting this research is to develop an algorithm that is capable of classifying news articles from the automobile industry, according to the competitive actions that they entail, with the use of Text Mining (TM) methods. It is needed to test how to properly preprocess the data for this research by preparing pipelines which fits each algorithm the best. The pipelines are tested along with nine different classification algorithms in the realm of regression, support vector machines, and neural networks. Preliminary testing for identifying the optimal pipelines and algorithms resulted in the selection of two algorithms with two different pipelines. The two algorithms are Logistic Regression (LR) and Artificial Neural Network (ANN). These algorithms are optimized further, where several parameters of each algorithm are tested. The best result is achieved with the ANN. The final model yields an accuracy of 0.79, a precision of 0.80, a recall of 0.78, and an F1 score of 0.76. By removing three of the classes that created noise, the final algorithm is capable of reaching an accuracy of 94%.

Keywords: Artificial Neural network, Competitive dynamics, Logistic Regression, Text classification, Text mining

Procedia PDF Downloads 122

Authors: Chieh-Chun Chang, Cheng-Ting Shih, Yan-Lin Liu, Shu-Jun Chang, Jay Wu

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With the widespread use of digital mammography (DM), radiation dose evaluation of breasts has become important. X-ray spectra are one of the key factors that influence the absorbed dose of glandular tissue. In this study, we estimated the X-ray spectrum of DM using the expectation maximization (EM) algorithm with the transmission measurement data. The interpolating polynomial model proposed by Boone was applied to generate the initial guess of the DM spectrum with the target/filter combination of Mo/Mo and the tube voltage of 26 kVp. The Monte Carlo N-particle code (MCNP5) was used to tally the transmission data through aluminum sheets of 0.2 to 3 mm. The X-ray spectrum was reconstructed by using the EM algorithm iteratively. The influence of the initial guess for EM reconstruction was evaluated. The percentage error of the average energy between the reference spectrum inputted for Monte Carlo simulation and the spectrum estimated by the EM algorithm was -0.14%. The normalized root mean square error (NRMSE) and the normalized root max square error (NRMaSE) between both spectra were 0.6% and 2.3%, respectively. We conclude that the EM algorithm with transmission measurement data is a convenient and useful tool for estimating x-ray spectra for DM in clinical practice.

Keywords: digital mammography, expectation maximization algorithm, X-Ray spectrum, X-Ray

Procedia PDF Downloads 732

Authors: Heng Chia Ying, Charmaine Tan Chai Nie, Burra Venkata Durga Kumar

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Process scheduling is the method of process management that determines which process the CPU will proceed with for the next task and how long it takes. Some issues were found in process management, particularly for Priority Scheduling (PS) and Round Robin Scheduling (RR). The proposed recommendations made for IPRRS are to combine the strengths of both into a combining algorithm while they draw on others to compensate for each weakness. A significant improvement on the combining technique of scheduler, Incorporating Priority Round-Robin Scheduler (IPRRS) address an algorithm for both high and low priority task to sustain the indefinite blocking issue faced in the priority scheduling algorithm and minimize the average turnaround time (ATT) and average waiting time (AWT) in RR scheduling algorithm. This paper will delve into the simple rules introduced by IPRRS and enhancements that both PS and RR bring to the execution of processes in the operating system. Furthermore, it incorporates the best aspects of each algorithm to build the optimum algorithm for a certain case in terms of prioritized processes, ATT, and AWT.

Keywords: round Robin scheduling, priority scheduling, indefinite blocking, process management, sustain, turnaround time

Procedia PDF Downloads 152

Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

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The increasing amount of collected data has limited the performance of the current analyzing algorithms. Thus, developing new cost-effective algorithms in terms of complexity, scalability, and accuracy raised significant interests. In this paper, a modified effective k-means based algorithm is developed and experimented. The new algorithm aims to reduce the computational load without significantly affecting the quality of the clusterings. The algorithm uses the City Block distance and a new stop criterion to guarantee the convergence. Conducted experiments on a real data set show its high performance when compared with the original k-means version.

Keywords: pattern recognition, global terrorism database, Manhattan distance, k-means clustering, terrorism data analysis

Procedia PDF Downloads 386

Authors: A. Mohammadi, S. Asghari, M. Aien, M. Rashidinejad

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After emergent of alternative current networks and their popularity, asynchronous motors became more widespread than other kinds of industrial motors. In order to control and run these motors efficiently, an accurate estimation of motor parameters is needed. There are different methods to obtain these parameters such as rotor locked test, no load test, DC test, analytical methods, and so on. The most common drawback of these methods is their inaccuracy in estimation of some motor parameters. In order to remove this concern, a novel method for parameter estimation of induction motors using particle swarm optimization (PSO) algorithm is proposed. In the proposed method, transient state of motor is used for parameter estimation. Comparison of the simulation results purtuined to the PSO algorithm with other available methods justifies the effectiveness of the proposed method.

Keywords: induction motor, motor parameter estimation, PSO algorithm, analytical method

Procedia PDF Downloads 635

Authors: Ramnath Narhete, Saket Pandey, Puran Gour

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This paper presents of dual-band planner antenna with a compact radiator for 2.4/5.2/5.8 proposed by optimizing its resonant frequency, Bandwidth of operation and radiation frequency using the genetic algorithm. The antenna consists L-shaped and E-shaped radiating element to generate two resonant modes for dual band operation. The above techniques have been successfully used in many applications. Dual band antenna with the compact radiator for 2.4/5.2/5.8 GHz WLAN application design and radiator size only width 8mm and a length is 11.3 mm. The antenna can we used for various application in the field of communication. Genetic algorithm will be used to design the antenna and impedance matching network.

Keywords: genetic algorithm, dual-band E, dual-band L, WLAN, compact radiator

Procedia PDF Downloads 580

Authors: Dipti Patra, Guguloth Uma, Smita Pradhan

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Registration is a fundamental task in image processing. It is used to transform different sets of data into one coordinate system, where data are acquired from different times, different viewing angles, and/or different sensors. The registration geometrically aligns two images (the reference and target images). Registration techniques are used in satellite images and it is important in order to be able to compare or integrate the data obtained from these different measurements. In this work, mutual information is considered as a similarity metric for registration of satellite images. The transformation is assumed to be a rigid transformation. An attempt has been made here to optimize the transformation function. The proposed image registration technique hybrid PSO-GA incorporates the notion of Particle Swarm Optimization and Genetic Algorithm and is used for finding the best optimum values of transformation parameters. The performance comparision obtained with the experiments on satellite images found that the proposed hybrid PSO-GA algorithm outperforms the other algorithms in terms of mutual information and registration accuracy.

Keywords: image registration, genetic algorithm, particle swarm optimization, hybrid PSO-GA algorithm and mutual information

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Authors: Ahlem Abid, Farah Jemili

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Intrusion detection has been the subject of numerous studies in industry and academia, but cyber security analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve intrusion detection system, the visualisation of the security events in form of graphs and diagrams is important to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and using a machine learning graph algorithm which can detect in real time different attacks as early as possible. We use the MAWILab intrusion detection dataset . We choose Microsoft Azure as a unified cloud environment to load our dataset on. We implement the k2 algorithm which is a graphical machine learning algorithm to classify attacks. Our system showed a good performance due to the graphical machine learning algorithm and spark structured streaming engine.

Keywords: Apache Spark Streaming, Graph, Intrusion detection, k2 algorithm, Machine Learning, MAWILab, Microsoft Azure Cloud

Procedia PDF Downloads 149

Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

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Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

Procedia PDF Downloads 303

Authors: Adilah Abdul Ghapor, Yong Zulina Zubairi, Rahmatullah Imon

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Missing value problem is common in statistics and has been of interest for years. This article considers two modern techniques in handling missing data for linear functional relationship model (LFRM) namely the Expectation-Maximization (EM) algorithm and Expectation-Maximization with Bootstrapping (EMB) algorithm using three performance indicators; namely the mean absolute error (MAE), root mean square error (RMSE) and estimated biased (EB). In this study, we applied the methods of imputing missing values in the LFRM. Results of the simulation study suggest that EMB algorithm performs much better than EM algorithm in both models. We also illustrate the applicability of the approach in a real data set.

Keywords: expectation-maximization, expectation-maximization with bootstrapping, linear functional relationship model, performance indicators

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Authors: Tomáš Dyk, Stanislav Procházka, Martin Drahanský

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The article proposes a new algorithm for detecting damaged areas of the tank barrel based on the image of the inner surface of the tank barrel. Damage position is calculated using image processing techniques such as edge detection, discrete wavelet transformation and image segmentation for accurate contour detection. The algorithm can detect surface damage in smoothbore and even in rifled tank barrels. The algorithm also calculates the volume of the detected damage from the depth map generated, for example, from the distance measurement unit. The proposed method was tested on data obtained by a tank barrel scanning device, which generates both surface image data and depth map. The article also discusses tank barrel scanning devices and how damaged surface impacts material resistance.

Keywords: barrel, barrel diagnostic, image processing, surface damage detection, tank

Procedia PDF Downloads 139

Authors: T. R. Sharika, Ashwini Kumar, Kamal Bijlani

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Encryption is the process of converting crucial information’s unreadable to unauthorized persons. Image security is an important type of encryption that secures all type of images from cryptanalysis. A stream cipher is a fast symmetric key algorithm which is used to convert plaintext to cipher text. In this paper we are proposing an image encryption algorithm with Discrete Cosine Transform and Stream Ciphers that can improve compression of images and enhanced security. The paper also explains the use of a shuffling algorithm for enhancing securing.

Keywords: decryption, DCT, encryption, RC4 cipher, stream cipher

Procedia PDF Downloads 363

Authors: Fathi Kallel, Ahmed Ben Hamida, Christian Berger-Vachon

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In this paper, a Speech Enhancement Algorithm based on Multi-Band Spectral Subtraction (MBSS) principle is evaluated for Bilateral Cochlear Implant (BCI) users. Specifically, dual-channel noise power spectral estimation algorithm using Power Spectral Densities (PSD) and Cross Power Spectral Densities (CPSD) of the observed signals is studied. The enhanced speech signal is obtained using Dual-Channel Multi-Band Spectral Subtraction ‘DC-MBSS’ algorithm. For performance evaluation, objective speech assessment test relying on Perceptual Evaluation of Speech Quality (PESQ) score is performed to fix the optimal number of frequency bands needed in DC-MBSS algorithm. In order to evaluate the speech intelligibility, subjective listening tests are assessed with 3 deafened BCI patients. Experimental results obtained using French Lafon database corrupted by an additive babble noise at different Signal-to-Noise Ratios (SNR) showed that DC-MBSS algorithm improves speech understanding for single and multiple interfering noise sources.

Keywords: speech enhancement, spectral substracion, noise estimation, cochlear impalnt

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Authors: El Mahdi Barrah, Said Safi, Abdessamad Malaoui

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In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results.

Keywords: Tifinagh character recognition, neural networks, local cost computation, ANN

Procedia PDF Downloads 335

Authors: Alireza Alesaadi

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Reliability analysis is a powerful method for determining the weak points of the electrical networks. In designing of electrical network, it is tried to design the most reliable network with minimal system shutting down, but it is usually associated with increasing the cost. In this paper, using adaptive genetic algorithm, a method was presented that provides the most reliable system with a certain economical cost. Finally, the proposed method is applied to a sample network and results will be analyzed.

Keywords: reliability, adaptive genetic algorithm, electrical network, communication engineering

Procedia PDF Downloads 513

Authors: Hoda Abugharsa

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In this paper we propose to design a sorter circuit using insertion sorting algorithm. The circuit will be designed using Algorithmic State Machines (ASM) method. That means converting the insertion sorting flowchart into an ASM chart. Then the ASM chart will be used to design the sorter circuit and the control unit.

Keywords: insert sorting algorithm, ASM chart, sorter circuit, state machine, control unit

Procedia PDF Downloads 445

Authors: Giorgi Methodishvili, Iuliia Methodishvili

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In this paper product development algorithm is used to determine the optimal management of financial resources in company. Aspects of financial management considered include put initial investment, examine all possible ways to solve the problem and the optimal rotation length of profit. The software of given problems is based using greedy algorithm. The obtained model and program maintenance enable us to define the optimal version of management of proper financial flows by using visual diagram on each level of investment.

Keywords: management, software, optimal, greedy algorithm, graph-diagram

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Authors: Abdelkhalek Bakkari

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Image segmentation is a critical step in image processing and pattern recognition. In this paper, we proposed a new robust automatic image classification based on a dynamic fuzzy c-means algorithm and mathematical morphology. The proposed segmentation algorithm (DFCM_MM) has been applied to MR perfusion images. The obtained results show the validity and robustness of the proposed approach.

Keywords: segmentation, classification, dynamic, fuzzy c-means, MR image

Procedia PDF Downloads 481

Authors: Dong-Keun Jeong, Jong-Hyun Kim, Woon-Ha Yoon, Hee-Je Kim

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In order to automatically operate the ultrasonic transducer drive system for sonicating aluminum, this paper proposes the ultrasonic transducer sensorless control algorithm. The resonance frequency shift and electrical impedance change is a common phenomenon in the state of the ultrasonic transducer. The proposed control algorithm make use of the impedance change of ultrasonic transducer according to the environment between air state and aluminum alloy state, it controls the ultrasonic transducer drive system autonomous without a sensor. The proposed sensorless autonomous ultrasonic transducer control algorithm was experimentally verified using a 3kW prototype ultrasonic transducer drive system.

Keywords: ultrasonic transducer drive system, impedance change, sensorless, autonomous control algorithm

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Authors: Fathi M. O. Hamed, Salma F. Elkofhaifee

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The aim of this work is to detect geometrical shape objects in an image. In this paper, the object is considered to be as a circle shape. The identification requires find three characteristics, which are number, size, and location of the object. To achieve the goal of this work, this paper presents an algorithm that combines from some of statistical approaches and image analysis techniques. This algorithm has been implemented to arrive at the major objectives in this paper. The algorithm has been evaluated by using simulated data, and yields good results, and then it has been applied to real data.

Keywords: image processing, median filter, projection, scale-space, segmentation, threshold

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Authors: Salim Ouchtati, Aissa Belmeguenai, Mouldi Bedda

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In this study, we propose an offline system for the recognition of the fragmented handwritten numeric chains. Firstly, we realized a recognition system of the isolated handwritten digits, in this part; the study is based mainly on the evaluation of neural network performances, trained with the gradient backpropagation algorithm. The used parameters to form the input vector of the neural network are extracted from the binary images of the isolated handwritten digit by several methods: the distribution sequence, sondes application, the Barr features, and the centered moments of the different projections and profiles. Secondly, the study is extended for the reading of the fragmented handwritten numeric chains constituted of a variable number of digits. The vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits).

Keywords: features extraction, handwritten numeric chains, image processing, neural networks

Procedia PDF Downloads 267

Authors: Ahad Salimi, Hassan Masoumi

Abstract:

Prostate cancer is one of the most common recognized cancers in men, and, is one of the most important mortality factors of cancer in this group. Determining of prostate’s boundary in TRUS (Transrectal Ultra Sound) images is very necessary for prostate cancer treatments. The weakness edges and speckle noise make the ultrasound images inherently to segment. In this paper a new automatic algorithm for prostate segmentation in TRUS images proposed that include three main stages. At first morphological smoothing and sticks filtering are used for noise removing. In second step, for finding a point in prostate region, SOFM algorithm is enlisted and in the last step, the boundary of prostate extracting accompanying active contour is employed. For validation of proposed method, a number of experiments are conducted. The results obtained by our algorithm show the promise of the proposed algorithm.

Keywords: SOFM, preprocessing, GVF contour, segmentation

Procedia PDF Downloads 330

Authors: Linda Boussaid, Farid Brahim Belaribi

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The physico-chemical properties of liquid materials in the industrial field, in general, and in that of the chemical industries, in particular, constitutes a prerequisite for the design of equipment, for the resolution of specific problems (related to the techniques of purification and separation, at risk in the transport of certain materials, etc.) and, therefore, at the production stage. Chloroalkanes, ethers constitute three chemical families having an industrial, theoretical and environmental interest. For example, these compounds are used in various applications in the chemical and pharmaceutical industries. In addition, they contribute to the particular thermodynamic behavior (deviation from ideality, association, etc.) of certain mixtures which constitute a severe test for predictive theoretical models. Finally, due to the degradation of the environment in the world, a renewed interest is observed for ethers, because some of their physicochemical properties could contribute to lower pollution (ethers would be used as additives in aqueous fuels.). This work is a thermodynamic, experimental and theoretical study of the volumetric properties of liquid binary systems formed from compounds belonging to the chemical families of chloroalkanes, ethers, having an industrial, theoretical and environmental interest. Experimental determination of the densities and excess volumes of the systems studied, at different temperatures in the interval [278.15-333.15] K and at atmospheric pressure, using an AntonPaar vibrating tube densitometer of the DMA5000 type. This contribution of experimental data, on the volumetric properties of the binary liquid mixtures of 1,2-dichloroethane with an ether, supplemented by an application of the theoretical model of Prigogine-Flory-Patterson PFP, will probably contribute to the enrichment of the thermodynamic database and the further development of the theory of Flory in its Prigogine-Flory-Patterson (PFP) version, for a better understanding of the thermodynamic behavior of these liquid binary mixtures

Keywords: prigogine-flory-patterson (pfp), propriétés volumétrique , volume d’excés, ethers

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

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Authors: Francesco Rizzuto, Matthew Stickland, Stephan Hannot

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The simulation of a positive displacement pump system with commercial software for Computer Fluid Dynamics (CFD), will result in an enormous computational effort due to the complexity of the pump system. This drawback restricts the use of it to a specific part of the pump in one simulation. This research focuses on developing an algorithm that provides a suitable result in agreement with experiment data, without that computational effort. The compressible equations are solved with an explicit algorithm. A comparison is presented between the FV method with Monotonic Upwind scheme for Conservative Laws (MUSCL) with slope limiter and experimental results. The source term for cavitation and friction is introduced into the algorithm with a slipping strategy and solved with a 4th order Runge-Kutta scheme (RK4). Different pumps are modeled and analyzed to evaluate the flexibility of the code. The simulation required minimal computation time and resources without compromising the accuracy of the simulation results. Therefore, this algorithm highlights the feasibility of pressure pulsation simulation as a design tool for an industrial purpose.

Keywords: cavitation, diaphragm, DVCM, finite volume, MUSCL, positive displacement pump

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Authors: Ping Bo, Meng Yunshan

Abstract:

Satellite-derived sea surface temperature (SST) is a key parameter for many operational and scientific applications. However, the disadvantage of SST data is a high percentage of missing data which is mainly caused by cloud coverage. Data Interpolating Empirical Orthogonal Function (DINEOF) algorithm is an EOF-based technique for reconstructing the missing data and has been widely used in oceanographic field. The reconstruction of SST images within a long time series using DINEOF can cause large discontinuities and one solution for this problem is to filter the temporal covariance matrix to reduce the spurious variability. Based on the previous researches, an algorithm is presented in this paper to improve the temporal correlations in EOF expansion. Similar with the previous researches, a filter, such as Laplacian filter, is implemented on the temporal covariance matrix, but the temporal relationship between two consecutive images which is used in the filter is considered in the presented algorithm, for example, two images in the same season are more likely correlated than those in the different seasons, hence the latter one is less weighted in the filter. The presented approach is tested for the monthly nighttime 4-km Advanced Very High Resolution Radiometer (AVHRR) Pathfinder SST for the long-term period spanning from 1989 to 2006. The results obtained from the presented algorithm are compared to those from the original DINEOF algorithm without filtering and from the DINEOF algorithm with filtering but without taking temporal relationship into account.

Keywords: data interpolating empirical orthogonal function, image reconstruction, sea surface temperature, temporal filter

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Authors: Jan Henrik Wosnitza

Abstract:

The logistic regression has developed into a standard approach for estimating conditional probabilities in a wide range of applications including credit risk prediction. The article at hand contributes to the current literature on logistic regression fourfold: First, it is demonstrated that the binary logistic regression automatically meets its model assumptions under very general conditions. This result explains, at least in part, the logistic regression's popularity. Second, the requirement of homoscedasticity in the context of binary logistic regression is theoretically substantiated. The variances among the groups of defaulted and non-defaulted obligors have to be the same across the level of the aggregated default indicators in order to achieve linear logits. Third, this article sheds some light on the question why nonlinear logits might be superior to linear logits in case of a small amount of data. Fourth, an innovative methodology for estimating correlations between obligor-specific log-odds is proposed. In order to crystallize the key ideas, this paper focuses on the example of credit risk prediction. However, the results presented in this paper can easily be transferred to any other field of application.

Keywords: correlation, credit risk estimation, default correlation, homoscedasticity, logistic regression, nonlinear logistic regression

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Authors: Anju Bala, Inderveer Chana

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Task scheduling is the key concern for the execution of performance-driven workflow applications. As efficient scheduling can have major impact on the performance of the system, task scheduling is often chosen for assigning the request to resources in an efficient way based on cloud resource characteristics. In this paper, priority based task scheduling algorithm has been proposed that prioritizes the tasks based on the length of the instructions. The proposed scheduling approach prioritize the tasks of Cloud applications according to the limits set by six sigma control charts based on dynamic threshold values. Further, the proposed algorithm has been validated through the CloudSim toolkit. The experimental results demonstrate that the proposed algorithm is effective for handling multiple task lists from workflows and in considerably reducing Makespan and Execution time.

Keywords: cloud computing, priority based scheduling, task scheduling, VM allocation

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Authors: Ahmed Tarek, Ahmed Alveed

Abstract:

Vehicle routing problem is a well-known re-search avenue in computing. Modern vehicle routing is more focused with the GPS-based coordinate system, as the state-of-the-art vehicle, and trucking systems are equipped with digital navigation. In this paper, a new two dimensional coordinate-based algorithm for addressing the vehicle routing problem for a supply chain network is proposed and explored, and the algorithm is compared with other available, and recently devised heuristics. For the algorithms discussed, which includes the pro-posed coordinate-based search heuristic as well, the advantages and the disadvantages associated with the heuristics are explored. The proposed algorithm is studied from the stand point of a small supermarket chain delivery network that supplies to its stores in four different states around the East Coast area, and is trying to optimize its trucking delivery cost. Minimizing the delivery cost for the supply network of a supermarket chain is important to ensure its business success.

Keywords: coordinate-based optimal routing, Hamiltonian Circuit, heuristic algorithm, traveling salesman problem, vehicle routing problem

Procedia PDF Downloads 148