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Journal articles on the topic 'Hybrid programming models'

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Published: 4 June 2021
Last updated: 17 February 2022

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1

Andrews, D., D. Niehaus, and P. Ashenden. "Programming models for hybrid CPU/FPGA chips." Computer 37, no. 1 (January 2004): 118–20. http://dx.doi.org/10.1109/mc.2004.1260732.

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Alghamdi, Ahmed Mohammed, Fathy Elbouraey Eassa, Maher Ali Khamakhem, Abdullah Saad AL-Malaise AL-Ghamdi, Ahmed S. Alfakeeh, Abdullah S. Alshahrani, and Ala A. Alarood. "Parallel Hybrid Testing Techniques for the Dual-Programming Models-Based Programs." Symmetry 12, no. 9 (September 20, 2020): 1555. http://dx.doi.org/10.3390/sym12091555.

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The importance of high-performance computing is increasing, and Exascale systems will be feasible in a few years. These systems can be achieved by enhancing the hardware’s ability as well as the parallelism in the application by integrating more than one programming model. One of the dual-programming model combinations is Message Passing Interface (MPI) + OpenACC, which has several features including increased system parallelism, support for different platforms with more performance, better productivity, and less programming effort. Several testing tools target parallel applications built by using programming models, but more effort is needed, especially for high-level Graphics Processing Unit (GPU)-related programming models. Owing to the integration of different programming models, errors will be more frequent and unpredictable. Testing techniques are required to detect these errors, especially runtime errors resulting from the integration of MPI and OpenACC; studying their behavior is also important, especially some OpenACC runtime errors that cannot be detected by any compiler. In this paper, we enhance the capabilities of ACC_TEST to test the programs built by using the dual-programming models MPI + OpenACC and detect their related errors. Our tool integrated both static and dynamic testing techniques to create ACC_TEST and allowed us to benefit from the advantages of both techniques reducing overheads, enhancing system execution time, and covering a wide range of errors. Finally, ACC_TEST is a parallel testing tool that creates testing threads based on the number of application threads for detecting runtime errors.
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Jamaluddin, Muhammad Na’im Fikri, Azlan Ismail, Amir Abd Rashid, and Talha Takleh Omar Takleh. "Performance comparison of java based parallel programming models." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 3 (December 1, 2019): 1577. http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1577-1583.

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<span lang="EN-MY">Parallel programming has been implemented in many areas to solve various computational problem with the aim, to improve the performance and scalability of the software application. There are a few parallel programming models commonly used, namely, threads, and message passing (distributed) models. Furthermore, various APIs have been proposed to implement these models based on two popular languages, notably, C/C++ and Java. A few studies have been done to compare the performance of parallel programming models, specifically, pure versus hybrid model. However, most of existing comparisons targeted on MPI/OpenMP based on C/C++ language. In this paper, our aim is to explore the performance comparison between threads, message passing and hybrid model in Java, specifically using Java multithreading and MPJ Express. For this reason, we have chosen a problem called word count occurrence which is significant in Natural Language Processing and use it to design and implement the parallel programs. We then present their performance and discuss the results.</span>
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Li, Dong, Bronis R. de Supinski, Martin Schulz, Dimitrios S. Nikolopoulos, and Kirk W. Cameron. "Strategies for Energy-Efficient Resource Management of Hybrid Programming Models." IEEE Transactions on Parallel and Distributed Systems 24, no. 1 (January 2013): 144–57. http://dx.doi.org/10.1109/tpds.2012.95.

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Wang, Zhi-Cheng, and Xiao-Bei Wu. "Hybrid Biogeography-Based Optimization for Integer Programming." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/672983.

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Biogeography-based optimization (BBO) is a relatively new bioinspired heuristic for global optimization based on the mathematical models of biogeography. By investigating the applicability and performance of BBO for integer programming, we find that the original BBO algorithm does not perform well on a set of benchmark integer programming problems. Thus we modify the mutation operator and/or the neighborhood structure of the algorithm, resulting in three new BBO-based methods, named BlendBBO, BBO_DE, and LBBO_LDE, respectively. Computational experiments show that these methods are competitive approaches to solve integer programming problems, and the LBBO_LDE shows the best performance on the benchmark problems.
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Andrews, D., D. Niehaus, R. Jidin, M. Finley, W. Peck, M. Frisbie, J. Ortiz, Ed Komp, and P. Ashenden. "Programming models for hybrid FPGA-cpu computational components: a missing link." IEEE Micro 24, no. 4 (July 2004): 42–53. http://dx.doi.org/10.1109/mm.2004.36.

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Rabenseifner, Rolf, and Gerhard Wellein. "Communication and Optimization Aspects of Parallel Programming Models on Hybrid Architectures." International Journal of High Performance Computing Applications 17, no. 1 (February 2003): 49–62. http://dx.doi.org/10.1177/1094342003017001005.

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Mohamed, Ahmed S. "Broader dynamic load balancing for hybrid/multi-level parallel programming models." International Journal of High Performance Computing and Networking 3, no. 2/3 (2005): 171. http://dx.doi.org/10.1504/ijhpcn.2005.008034.

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Sitek, Paweł, Krzysztof Bzdyra, and Jarosław Wikarek. "A Hybrid Method for Modeling and Solving Supply Chain Optimization Problems with Soft and Logical Constraints." Mathematical Problems in Engineering 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/1532420.

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This paper presents a hybrid method for modeling and solving supply chain optimization problems with soft, hard, and logical constraints. Ability to implement soft and logical constraints is a very important functionality for supply chain optimization models. Such constraints are particularly useful for modeling problems resulting from commercial agreements, contracts, competition, technology, safety, and environmental conditions. Two programming and solving environments, mathematical programming (MP) and constraint logic programming (CLP), were combined in the hybrid method. This integration, hybridization, and the adequate multidimensional transformation of the problem (as a presolving method) helped to substantially reduce the search space of combinatorial models for supply chain optimization problems. The operation research MP and declarative CLP, where constraints are modeled in different ways and different solving procedures are implemented, were linked together to use the strengths of both. This approach is particularly important for the decision and combinatorial optimization models with the objective function and constraints, there are many decision variables, and these are summed (common in manufacturing, supply chain management, project management, and logistic problems). TheECLiPSesystem with Eplex library was proposed to implement a hybrid method. Additionally, the proposed hybrid transformed model is compared with the MILP-Mixed Integer Linear Programming model on the same data instances. For illustrative models, its use allowed finding optimal solutions eight to one hundred times faster and reducing the size of the combinatorial problem to a significant extent.
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Chorley, Martin J., David W. Walker, and Martyn F. Guest. "Hybrid Message-Passing and Shared-Memory Programming in a Molecular Dynamics Application On Multicore Clusters." International Journal of High Performance Computing Applications 23, no. 3 (June 2, 2009): 196–211. http://dx.doi.org/10.1177/1094342009106188.

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Hybrid programming, whereby shared-memory and message-passing programming techniques are combined within a single parallel application, has often been discussed as a method for increasing code performance on clusters of symmetric multiprocessors (SMPs). This paper examines whether the hybrid model brings any performance benefits for clusters based on multicore processors. A molecular dynamics application has been parallelized using both MPI and hybrid MPI/OpenMP programming models. The performance of this application has been examined on two high-end multicore clusters using both Infiniband and Gigabit Ethernet interconnects. The hybrid model has been found to perform well on the higher-latency Gigabit Ethernet connection, but offers no performance benefit on low-latency Infiniband interconnects. The changes in performance are attributed to the differing communication profiles of the hybrid and MPI codes.
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Huang, Miaoqing, Chenggang Lai, Xuan Shi, Zhijun Hao, and Haihang You. "Study of parallel programming models on computer clusters with Intel MIC coprocessors." International Journal of High Performance Computing Applications 31, no. 4 (April 13, 2015): 303–15. http://dx.doi.org/10.1177/1094342015580864.

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Coprocessors based on the Intel Many Integrated Core (MIC) Architecture have been adopted in many high-performance computer clusters. Typical parallel programming models, such as MPI and OpenMP, are supported on MIC processors to achieve the parallelism. In this work, we conduct a detailed study on the performance and scalability of the MIC processors under different programming models using the Beacon computer cluster. Our findings are as follows. (1) The native MPI programming model on the MIC processors is typically better than the offload programming model, which offloads the workload to MIC cores using OpenMP. (2) On top of the native MPI programming model, multithreading inside each MPI process can further improve the performance for parallel applications on computer clusters with MIC coprocessors. (3) Given a fixed number of MPI processes, it is a good strategy to schedule these MPI processes to as few MIC processors as possible to reduce the cross-processor communication overhead. (4) The hybrid MPI programming model, in which data processing is distributed to both MIC cores and CPU cores, can outperform the native MPI programming model.
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Jiekun Song, and Yu Zhang. "Random Fuzzy Programming Models and Hybrid Intelligent Algorithm for Oilfield Exploitation Plan." International Journal of Advancements in Computing Technology 4, no. 6 (April 15, 2012): 118–25. http://dx.doi.org/10.4156/ijact.vol4.issue6.14.

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XU, Lin, Quan-sheng LI, and Wan-sheng TANG. "Hybrid Intelligent Algorithm for Solving the Bilevel Programming Models with Fuzzy Variables." Systems Engineering - Theory & Practice 28, no. 7 (July 2008): 100–104. http://dx.doi.org/10.1016/s1874-8651(09)60030-2.

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Gajinov, Vladimir, Srdjan Stipić, Igor Erić, Osman S. Unsal, Eduard Ayguadé, and Adrian Cristal. "DaSH: A benchmark suite for hybrid dataflow and shared memory programming models." Parallel Computing 45 (June 2015): 18–48. http://dx.doi.org/10.1016/j.parco.2015.03.005.

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Zhou, Fuli, and Yandong He. "Pallet Scheduling Models Under Deterministic and Non-Deterministic Scenarios Using a Hybrid GA Method." International Journal of Decision Support System Technology 13, no. 2 (April 2021): 1–15. http://dx.doi.org/10.4018/ijdsst.2021040101.

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This study examines the pallet scheduling problem considering random demands under the novel pallet operation mechanism by resources sharing among the pallet sharing system. Two nonlinear integer pallet scheduling models under deterministic and non-deterministic environment are formulated in terms of the pallet demand variable. To solve the pallet programming model, the hybrid genetic algorithm (HGA) integrating local search strategy is designed to derive the optimal pallet scheduling solution. Besides, the fixed sample size sampling strategy is employed to deal with the uncertain demand during the non-deterministic programming model, realized by the Monte Carlo simulation. The two models can assist decision makers arrange a scientific pallet scheduling solution under deterministic and non-deterministic atmosphere. Finally, the numerical case is implemented to testify the effectiveness of the two models and efficiency of the hybrid algorithms.
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Sitek, Pawel, and Jaroslaw Wikarek. "A Hybrid Method for the Modelling and Optimisation of Constrained Search Problems." Foundations of Management 5, no. 3 (August 21, 2014): 7–22. http://dx.doi.org/10.2478/fman-2014-0016.

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AbstractThe paper presents a concept and the outline of the implementation of a hybrid approach to modelling and solving constrained problems. Two environments of mathematical programming (in particular, integer programming) and declarative programming (in particular, constraint logic programming) were integrated. The strengths of integer programming and constraint logic programming, in which constraints are treated in a different way and different methods are implemented, were combined to use the strengths of both. The hybrid method is not worse than either of its components used independently. The proposed approach is particularly important for the decision models with an objective function and many discrete decision variables added up in multiple constraints. To validate the proposed approach, two illustrative examples are presented and solved. The first example is the authors’ original model of cost optimisation in the supply chain with multimodal transportation. The second one is the two-echelon variant of the well-known capacitated vehicle routing problem.
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John, Santhosh. "Development of an Educational Ontology for Java Programming (JLEO) with a Hybrid Methodology Derived from Conventional Software Engineering Process Models." International Journal of Information and Education Technology 4, no. 4 (2014): 308–12. http://dx.doi.org/10.7763/ijiet.2014.v4.419.

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Bedhief, Asma Ouled. "Comparing Mixed-Integer Programming and Constraint Programming Models for the Hybrid Flow Shop Scheduling Problem with Dedicated Machines." Journal Européen des Systèmes Automatisés 54, no. 4 (August 31, 2021): 591–97. http://dx.doi.org/10.18280/jesa.540408.

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The paper considers a two-stage hybrid flow shop scheduling problem with dedicated machines and release dates. Each job must be first processed on the single machine of stage 1, and then, the job is processed on one of the two dedicated machines of stage 2, depending on its type. Moreover, the jobs are available for processing at their respective release dates. Our goal is to obtain a schedule that minimizes the makespan. This problem is strongly NP-hard. In this paper, two mathematical models are developed for the problem: a mixed-integer programming model and a constraint programming model. The performance of these two models is compared on different problem configurations. And the results show that the constraint programming outperforms the mixed-integer programming in finding optimal solutions for large problem sizes (450 jobs) with very reasonable computing times.
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Xu, Chengcheng, Zhibin Li, and Wei Wang. "SHORT-TERM TRAFFIC FLOW PREDICTION USING A METHODOLOGY BASED ON AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND GENETIC PROGRAMMING." TRANSPORT 31, no. 3 (September 21, 2016): 343–58. http://dx.doi.org/10.3846/16484142.2016.1212734.

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The accurate short-term traffic flow forecasting is fundamental to both theoretical and empirical aspects of intelligent transportation systems deployment. This study aimed to develop a simple and effective hybrid model for forecasting traffic volume that combines the AutoRegressive Integrated Moving Average (ARIMA) and the Genetic Programming (GP) models. By combining different models, different aspects of the underlying patterns of traffic flow could be captured. The ARIMA model was used to model the linear component of the traffic flow time series. Then the GP model was applied to capture the nonlinear component by modelling the residuals from the ARIMA model. The hybrid models were fitted for four different time-aggregations: 5, 10, 15, and 20 min. The validations of the proposed hybrid methodology were performed by using traffic data under both typical and atypical conditions from multiple locations on the I-880N freeway in the United States. The results indicated that the hybrid models had better predictive performance than utilizing only ARIMA model for different aggregation time intervals under typical conditions. The Mean Relative Error (MRE) of the hybrid models was found to be from 4.1 to 6.9% for different aggregation time intervals under typical conditions. The predictive performance of the hybrid method was improved with an increase in the aggregation time interval. In addition, the validation results showed that the predictive performance of the hybrid model was also better than that of the ARIMA model under atypical conditions.
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TRUONG, HONG-LINH, SCHAHRAM DUSTDAR, and KAMAL BHATTACHARYA. "CONCEPTUALIZING AND PROGRAMMING HYBRID SERVICES IN THE CLOUD." International Journal of Cooperative Information Systems 22, no. 04 (December 2013): 1341003. http://dx.doi.org/10.1142/s0218843013410037.

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For solving complex problems, in many cases, software alone might not be sufficient and we need hybrid systems of software and humans in which humans not only direct the software performance but also perform computing and vice versa. Therefore, we advocate constructing "social computers" which combine software and human services. However, to date, human capabilities cannot be easily programmed into complex applications in a similar way like software capabilities. There is a lack of techniques to conceptualize and program human and software capabilities in a unified way. In this paper, we explore a new way to virtualize, provision and program human capabilities using cloud computing concepts and service delivery models. We propose novel methods for conceptualizing and modeling clouds of human-based services (HBS) and combine HBS with software-based services (SBS) to establish clouds of hybrid services. In our model, we present common APIs, similar to well-developed APIs for software services, to access individual and team-based compute units in clouds of HBS. Based on that, we propose a framework for utilizing SBS and HBS to solve complex problems. We present several programming primitives for hybrid services, also covering forming hybrid solutions consisting of software and humans. We illustrate our concepts via some examples of using our cloud APIs and existing cloud APIs for software.
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Jiang, Hua, and Zhi Gang Lu. "A Fuzzy Programming Model of Supplier Selection." Advanced Materials Research 468-471 (February 2012): 668–73. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.668.

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An integrated supplier selection problem under fuzzy environment is studied in this paper. Firstly, the linear weight method is used to calculate the scores of suppliers according to their different attributes, such as: quality, service, warranty, delivery, reputation and position, which are assumed as fuzzy variables. Secondly, a fuzzy expected value programming model and a fuzzy chance-constrained programming model are proposed to select the best combination of the suppliers and determine the order quantities. A hybrid intelligent algorithm, based on fuzzy simulation, genetic algorithm and neural network, is used to solve the two models. Finally, a numerical example is given to illustrate the effectiveness of the proposed models.
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Jeon, Byoung Jin, and Hyoung Gwon Choi. "Comparison of Message Passing Interface and Hybrid Programming Models to Solve Pressure Equation in Distributed Memory System." Transactions of the Korean Society of Mechanical Engineers B 39, no. 2 (February 1, 2015): 191–97. http://dx.doi.org/10.3795/ksme-b.2015.39.2.191.

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Pope, B. J., B. G. Fitch, M. C. Pitman, J. J. Rice, and M. Reumann. "Performance of Hybrid Programming Models for Multiscale Cardiac Simulations: Preparing for Petascale Computation." IEEE Transactions on Biomedical Engineering 58, no. 10 (October 2011): 2965–69. http://dx.doi.org/10.1109/tbme.2011.2161580.

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Mehdizadeh, Saeid, Farshad Ahmadi, Ali Danandeh Mehr, and Mir Jafar Sadegh Safari. "Drought modeling using classic time series and hybrid wavelet-gene expression programming models." Journal of Hydrology 587 (August 2020): 125017. http://dx.doi.org/10.1016/j.jhydrol.2020.125017.

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Sitek, Paweł, and Jarosław Wikarek. "A Hybrid Programming Framework for Modeling and Solving Constraint Satisfaction and Optimization Problems." Scientific Programming 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5102616.

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This paper proposes a hybrid programming framework for modeling and solving of constraint satisfaction problems (CSPs) and constraint optimization problems (COPs). Two paradigms, CLP (constraint logic programming) and MP (mathematical programming), are integrated in the framework. The integration is supplemented with the original method of problem transformation, used in the framework as a presolving method. The transformation substantially reduces the feasible solution space. The framework automatically generates CSP and COP models based on current values of data instances, questions asked by a user, and set of predicates and facts of the problem being modeled, which altogether constitute a knowledge database for the given problem. This dynamic generation of dedicated models, based on the knowledge base, together with the parameters changing externally, for example, the user’s questions, is the implementation of the autonomous search concept. The models are solved using the internal or external solvers integrated with the framework. The architecture of the framework as well as its implementation outline is also included in the paper. The effectiveness of the framework regarding the modeling and solution search is assessed through the illustrative examples relating to scheduling problems with additional constrained resources.
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Sharma, Nidhi, and Manoj Wadhwa. "eXSRUP: Hybrid Software Development Model Integrating Extreme Programing, Scrum & Rational Unified Process." TELKOMNIKA Indonesian Journal of Electrical Engineering 16, no. 2 (November 1, 2015): 377. http://dx.doi.org/10.11591/tijee.v16i2.1627.

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<p>Software industries are progressively adopting the agile development practices of customized models such as Extreme Programming (XP) or Scrum or Rational Unified Process (RUP). Scrum and Extreme Programming (XP) are frequently used agile models, whereas Rational Unified Process (RUP) is one popular classic plan driven software development methodology. Both agile and plan driven models have their own merits &amp; demerits such as XP has good engineering practices, team collaboration and on the other hand weak documentation, poor performance in medium &amp; large scale projects. Scrum is based on project management practices. RUP model has some limitations such as impractical for small and fast paced projects, tendency to be over budgeted, condemn rapid changes in requirements. This research paper based on proposes hybrid framework eXSRUP by combining strengths of Scrum, XP and RUP by suppressing their limitations to produce high quality software.</p>
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SCHMOLLINGER, MARTIN, and MICHAEL KAUFMANN. "DESIGNING PARALLEL ALGORITHMS FOR HIERARCHICAL SMP CLUSTERS." International Journal of Foundations of Computer Science 14, no. 01 (February 2003): 59–78. http://dx.doi.org/10.1142/s0129054103001595.

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Clusters of symmetric multiprocessor nodes (SMP clusters) are one of the most important parallel architectures at the moment. The architecture consists of shared-memory nodes with multiple processors and a fast interconnection network between the nodes. New programming models try to exploit this architecture by using threads in the nodes and using message-passing-libraries for inter-node communication. In order to develop efficient algorithms, it is necessary to consider the hybrid nature of the architecture and of the programming models. We present the κNUMA-model and a methodology that build a good base for designing efficient algorithms for SMP clusters. The κNUMA-model is a computational model that extends the bulk-synchronous parallel (BSP) model with the characteristics of SMP clusters and new hybrid programming models. The κNUMA-methodology suggests to develop efficient overall algorithms by developing efficient algorithms for each level in the hierarchy. We use the problem of personalized one-to-all-broadcast and the dense matrix-vector-multiplication for the presentation. The theoretical results of the analysis of the dense matrix-vector-multiplication are verified practically. We show results of experiments, made on a Linux-cluster of dual Pentium-III nodes.
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Farajpanah, Hiwa, Morteza Lotfirad, Arash Adib, Hassan Esmaeili-Gisavandani, Özgur Kisi, Mohammad Mehdi Riyahi, and Jaber Salehpoor. "Ranking of hybrid wavelet-AI models by TOPSIS method for estimation of daily flow discharge." Water Supply 20, no. 8 (September 4, 2020): 3156–71. http://dx.doi.org/10.2166/ws.2020.211.

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Abstract This research uses the multi-layer perceptron–artificial neural network (MLP-ANN), radial basis function–ANN (RBF-ANN), least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), M5 model tree (M5T), gene expression programming (GEP), genetic programming (GP) and Bayesian network (BN) with five types of mother wavelet functions (MWFs: coif4, db10, dmey, fk6 and sym7) and selects the best model by the TOPSIS method. The case study is the Navrood watershed in the north of Iran and the considered parameters are daily flow discharge, temperature and precipitation during 1991 to 2018. The derived results show that the best method is the hybrid of the M5T model with sym7 wavelet function. The MWFs were decomposed by discrete wavelet transform (DWT). The combination of AI models and MWFs improves the correlation coefficient of MLP, RBF, LSSVM, ANFIS, GP, GEP, M5T and BN by 8.05%, 4.6%, 8.14%, 8.14%, 22.97%, 7.5%, 5.75% and 10% respectively.
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Sitek, Paweł, and Jarosław Wikarek. "A Hybrid Approach to the Optimization of Multiechelon Systems." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/925675.

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In freight transportation there are two main distribution strategies: direct shipping and multiechelon distribution. In the direct shipping, vehicles, starting from a depot, bring their freight directly to the destination, while in the multiechelon systems, freight is delivered from the depot to the customers through an intermediate points. Multiechelon systems are particularly useful for logistic issues in a competitive environment. The paper presents a concept and application of a hybrid approach to modeling and optimization of the Multi-Echelon Capacitated Vehicle Routing Problem. Two ways of mathematical programming (MP) and constraint logic programming (CLP) are integrated in one environment. The strengths of MP and CLP in which constraints are treated in a different way and different methods are implemented and combined to use the strengths of both. The proposed approach is particularly important for the discrete decision models with an objective function and many discrete decision variables added up in multiple constraints. An implementation of hybrid approach in theECLiPSesystem using Eplex library is presented. The Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP) and its variants are shown as an illustrative example of the hybrid approach. The presented hybrid approach will be compared with classical mathematical programming on the same benchmark data sets.
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Sokolov, Sergei, Anton Zhilenkov, Sergei Chernyi, Anatoliy Nyrkov, and Nikolay Glebov. "Hybrid neural networks in cyber physical system interface control systems." Bulletin of Electrical Engineering and Informatics 9, no. 3 (June 1, 2020): 1268–75. http://dx.doi.org/10.11591/eei.v9i3.1293.

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The calculation and results of simulation of the magnetic control system for the spacecraft momentum are presented in the paper. The simulation includes an assessment of the reliability of calculating the Earth's magnetic field parameters, as well as an assessment of the quality of object stabilization by resetting the total momentum with the aid of the system under review. The outcome of a comparative analysis of resource efficiency and energy efficiency are demonstrated in the implementation of the proposed hardware models of controllers on FPGA. The strengths and weaknesses of the programming models are shown. The developed models will allow to be modified and perform more complex operations in the future.
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Salpasaranis, Konstantinos, and Vasilios Stylianakis. "A Hybrid Genetic Programming Method in Optimization and Forecasting: A Case Study of the Broadband Penetration in OECD Countries." Advances in Operations Research 2012 (2012): 1–32. http://dx.doi.org/10.1155/2012/904797.

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The introduction of a hybrid genetic programming method (hGP) in fitting and forecasting of the broadband penetration data is proposed. The hGP uses some well-known diffusion models, such as those of Gompertz, Logistic, and Bass, in the initial population of the solutions in order to accelerate the algorithm. The produced solutions models of the hGP are used in fitting and forecasting the adoption of broadband penetration. We investigate the fitting performance of the hGP, and we use the hGP to forecast the broadband penetration in OECD (Organisation for Economic Co-operation and Development) countries. The results of the optimized diffusion models are compared to those of the hGP-generated models. The comparison indicates that the hGP manages to generate solutions with high-performance statistical indicators. The hGP cooperates with the existing diffusion models, thus allowing multiple approaches to forecasting. The modified algorithm is implemented in the Python programming language, which is fast in execution time, compact, and user friendly.
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Kim, Dongwook, Dug Hee Moon, and Ilkyeong Moon. "Balancing a mixed-model assembly line with unskilled temporary workers: algorithm and case study." Assembly Automation 38, no. 4 (September 3, 2018): 511–23. http://dx.doi.org/10.1108/aa-06-2017-070.

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PurposeThe purpose of this paper is to present the process of balancing a mixed-model assembly line by incorporating unskilled temporary workers who enhance productivity. The authors develop three models to minimize the sum of the workstation costs and the labor costs of skilled and unskilled temporary workers, cycle time and potential work overloads.Design/methodology/approachThis paper deals with the problem of designing an integrated mixed-model assembly line with the assignment of skilled and unskilled temporary workers. Three mathematical models are developed using integer linear programming and mixed integer linear programming. In addition, a hybrid genetic algorithm that minimizes total operation costs is developed.FindingsComputational experiments demonstrate the superiority of the hybrid genetic algorithm over the mathematical model and reveal managerial insights. The experiments show the trade-off between the labor costs of unskilled temporary workers and the operation costs of workstations.Originality/valueThe developed models are based on practical features of a real-world problem, including simultaneous assignments of workers and precedence restrictions for tasks. Special genetic operators and heuristic algorithms are used to ensure the feasibility of solutions and make the hybrid genetic algorithm efficient. Through a case study, the authors demonstrated the validity of employing unskilled temporary workers in an assembly line.
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Salpasaranis, Konstantinos, and Vasilios Stylianakis. "Forecasting Models of the Coronavirus (COVID-19) Cumulative Confirmed Cases Using a Hybrid Genetic Programming Method." European Journal of Engineering Research and Science 5, no. 12 (December 15, 2020): 52–60. http://dx.doi.org/10.24018/ejers.2020.5.12.2129.

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The coronavirus disease 2019 (COVID-19) diffusion process, starting in China, caused more than 4600 lives until June 2020 and became a major threat to global public health systems. In Greece, the phenomenon started on February 2020 and it is still being developed. This paper presents the implementation of a hybrid Genetic Programming (hGP) method in finding fitting models of the Coronavirus (COVID 19) for the cumulative confirmed cases in China for the first saturation level until May 2020 and it proposes forecasting models for Greece before summer tourist season. The specific hGP method encapsulates the use of some well-known diffusion models for forecasting purposes, epidemiological models and produces time dependent models with high performance statistical indices. A retrospective study confirmed the excellent forecasting performance of the method until 3 June 2020.
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Barton, Alan J., Julio J. Valdés, and Robert Orchard. "Neural networks with multiple general neuron models: A hybrid computational intelligence approach using Genetic Programming." Neural Networks 22, no. 5-6 (July 2009): 614–22. http://dx.doi.org/10.1016/j.neunet.2009.06.043.

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Xu, Rengan, Xiaonan Tian, Sunita Chandrasekaran, and Barbara Chapman. "Multi-GPU Support on Single Node Using Directive-Based Programming Model." Scientific Programming 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/621730.

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Existing studies show that using single GPU can lead to obtaining significant performance gains. We should be able to achieve further performance speedup if we use more than one GPU. Heterogeneous processors consisting of multiple CPUs and GPUs offer immense potential and are often considered as a leading candidate for porting complex scientific applications. Unfortunately programming heterogeneous systems requires more effort than what is required for traditional multicore systems. Directive-based programming approaches are being widely adopted since they make it easy to use/port/maintain application code. OpenMP and OpenACC are two popular models used to port applications to accelerators. However, neither of the models provides support for multiple GPUs. A plausible solution is to use combination of OpenMP and OpenACC that forms a hybrid model; however, building this model has its own limitations due to lack of necessary compilers’ support. Moreover, the model also lacks support for direct device-to-device communication. To overcome these limitations, an alternate strategy is to extend OpenACC by proposing and developing extensions that follow a task-based implementation for supporting multiple GPUs. We critically analyze the applicability of the hybrid model approach and evaluate the proposed strategy using several case studies and demonstrate their effectiveness.
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Mohamed, Y., and S. M. AbouRizk. "A hybrid approach for developing special purpose simulation tools." Canadian Journal of Civil Engineering 33, no. 12 (December 1, 2006): 1505–15. http://dx.doi.org/10.1139/l06-073.

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The use of simulation techniques is an effective approach for modeling construction operations. Unfortunately, the high level of technical knowledge and development time required for building functional simulation models renders simulation modeling an impractical technology for many in the construction industry. Research in construction simulation tackles this conflict by providing modeling approaches that reduce the knowledge and time usually required for building simulation models of construction operations. Special purpose simulation (SPS) allows construction engineers with only minimal simulation knowledge to build practical simulation models. This paper presents a hybrid approach (HSPS) for effective and time-saving development of SPS tools. The approach utilizes visual, general purpose modeling elements to customize the simulation behaviors of new SPS elements, minimizing the programming effort required for developing these elements. This paper describes the theoretical background to the HSPS approach, its implementation, and a sample application successfully created subsequently. It also shows the results of an experiment quantifying the savings in development time achieved using this approach.Key words: automation, simulation models, computerized simulation, tunnel construction, construction management.
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KAO, LIE-JANE, and CHENG-FEW LEE. "ALTERNATIVE METHOD FOR DETERMINING INDUSTRIAL BOND RATINGS: THEORY AND EMPIRICAL EVIDENCE." International Journal of Information Technology & Decision Making 11, no. 06 (November 2012): 1215–35. http://dx.doi.org/10.1142/s0219622012500332.

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The financial-ratio-based credit-scoring model for bond rating system requires the maximization of two conflicting objectives (i.e., the explanatory and discriminatory power, simultaneously), which had not been directly addressed in literature. The main purpose of this study is to develop a hybrid multivariate credit-scoring model that combines the principle component analysis and Fisher's discriminant analysis using the MINIMAX goal programming technique so that the maximization of the two conflicting objectives can be compromised. The performance of alternative credit-scoring models is analyzed and compared using dataset from previous studies. We find that the proposed hybrid credit-scoring model outperforms other alternative models in both explanatory and discriminatory powers.
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Valuev, Andrey. "ON CALCULATION OF LINEAR RESOURCE PLANNING MODELS FOR OPTIMAL PROJECT SCHEDULING." Mathematical Modelling and Analysis 13, no. 2 (June 30, 2008): 275–88. http://dx.doi.org/10.3846/1392-6292.2008.13.275-288.

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Recent author's papers have shown new opportunities resulting from the treatment of resource planning in project scheduling as the optimization problem for a hybrid system. This approach gives the possibility to work out the optimum resource sharing in an iteration process of branch‐and‐bound type. The present paper concentrates on the most standard case of the problem in question for which all the relationships may be represented in the linear form. Two exact finite methods are proposed. The first method is obtained using the piecewise‐linear form of Bellman function, the second evolves from the decomposition approach for dynamic linear programming problem.
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Stuckey, Peter J., Thibaut Feydy, Andreas Schutt, Guido Tack, and Julien Fischer. "The MiniZinc Challenge 2008–2013." AI Magazine 35, no. 2 (June 19, 2014): 55–60. http://dx.doi.org/10.1609/aimag.v35i2.2539.

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MiniZinc is a solver agnostic modeling language for defining and solver combinatorial satisfaction and optimization problems. MiniZinc provides a solver independent modeling language which is now supported by constraint programming solvers, mixed integer programming solvers, SAT and SAT modulo theory solvers, and hybrid solvers. Since 2008 we have run the MiniZinc challenge every year, which compares and contrasts the different strengths of different solvers and solving technologies on a set of MiniZinc models. Here we report on what we have learnt from running the competition for 6 years.
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Acakpovi, Amevi. "Original Framework for Optimizing Hybrid Energy Supply." Journal of Energy 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/8317505.

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This paper proposes an original framework for optimizing hybrid energy systems. The recent growth of hybrid energy systems in remote areas across the world added to the increasing cost of renewable energy has triggered the inevitable development of hybrid energy systems. Hybrid energy systems always pose a problem of optimization of cost which has been approached with different perspectives in the recent past. This paper proposes a framework to guide the techniques of optimizing hybrid energy systems in general. The proposed framework comprises four stages including identification of input variables for energy generation, establishment of models of energy generation by individual sources, development of artificial intelligence, and finally summation of selected sources. A case study of a solar, wind, and hydro hybrid system was undertaken with a linear programming approach. Substantial results were obtained with regard to how load requests were constantly satisfied while minimizing the cost of electricity. The developed framework gained its originality from the fact that it has included models of individual sources of energy that even make the optimization problem more complex. This paper also has impacts on the development of policies which will encourage the integration and development of renewable energies.
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Al-Hajj, Rami, Ali Assi, Mohamad Fouad, and Emad Mabrouk. "A Hybrid LSTM-Based Genetic Programming Approach for Short-Term Prediction of Global Solar Radiation Using Weather Data." Processes 9, no. 7 (July 8, 2021): 1187. http://dx.doi.org/10.3390/pr9071187.

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The integration of solar energy in smart grids and other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power the supply-demand balance’s consistency. An accurate global solar radiation (GSR) prediction model can ensure overall system reliability and power generation scheduling. This article describes a nonlinear hybrid model based on Long Short-Term Memory (LSTM) models and the Genetic Programming technique for short-term prediction of global solar radiation. The LSTMs are Recurrent Neural Network (RNN) models that are successfully used to predict time-series data. We use these models as base predictors of GSR using weather and solar radiation (SR) data. Genetic programming (GP) is an evolutionary heuristic computing technique that enables automatic search for complex solution formulas. We use the GP in a post-processing stage to combine the LSTM models’ outputs to find the best prediction of the GSR. We have examined two versions of the GP in the proposed model: a standard version and a boosted version that incorporates a local search technique. We have shown an improvement in terms of performance provided by the proposed hybrid model. We have compared its performance to stacking techniques based on machine learning for combination. The results show that the suggested method provides significant improvement in terms of performance and consistency.
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Tripathi, Arpit, Pulkit Gupta, Aditya Trivedi, and Rahul Kala. "Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms." International Journal of Intelligent Information Technologies 7, no. 2 (April 2011): 63–83. http://dx.doi.org/10.4018/jiit.2011040104.

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The ease of use and re-configuration in a wireless network has played a key role in their widespread growth. The node deployment problem deals with an optimal placement strategy of the wireless nodes. This paper models a wireless sensor network, consisting of a number of nodes, and a unique sink to which all the information is transmitted using the shortest connecting path. Traditionally the systems have used Genetic Algorithms for optimal placement of the nodes that usually fail to give results in problems employing large numbers of nodes or higher areas to be covered. This paper proposes a hybrid Genetic Programming (GP) and Genetic Algorithm (GA) for solving the problem. While the GP optimizes the deployment structure, the GA is used for actual node placement as per the GP optimized structure. The GA serves as a slave and GP serves as master in this hierarchical implementation. The algorithm optimizes total coverage area, energy utilization, lifetime of the network, and the number of nodes deployed. Experimental results show that the algorithm could place the sensor nodes in a variety of scenarios. The placement was found to be better than random placement strategy as well as the Genetic Algorithm placement strategy.
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Mejari, Manas, Vihangkumar V. Naik, Dario Piga, and Alberto Bemporad. "Identification of hybrid and linear parameter‐varying models via piecewise affine regression using mixed integer programming." International Journal of Robust and Nonlinear Control 30, no. 15 (September 8, 2020): 5802–19. http://dx.doi.org/10.1002/rnc.5198.

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Alshahrani, Saeed, Waleed Al Shehri, Jameel Almalki, Ahmed M. Alghamdi, and Abdullah M. Alammari. "Accelerating Spark-Based Applications with MPI and OpenACC." Complexity 2021 (July 21, 2021): 1–17. http://dx.doi.org/10.1155/2021/9943289.

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The amount of data produced in scientific and commercial fields is growing dramatically. Correspondingly, big data technologies, such as Hadoop and Spark, have emerged to tackle the challenges of collecting, processing, and storing such large-scale data. Unfortunately, big data applications usually have performance issues and do not fully exploit a hardware infrastructure. One reason is that applications are developed using high-level programming languages that do not provide low-level system control in terms of performance of highly parallel programming models like message passing interface (MPI). Moreover, big data is considered a barrier of parallel programming models or accelerators (e.g., CUDA and OpenCL). Therefore, the aim of this study is to investigate how the performance of big data applications can be enhanced without sacrificing the power consumption of a hardware infrastructure. A Hybrid Spark MPI OpenACC (HSMO) system is proposed for integrating Spark as a big data programming model, with MPI and OpenACC as parallel programming models. Such integration brings together the advantages of each programming model and provides greater effectiveness. To enhance performance without sacrificing power consumption, the integration approach needs to exploit the hardware infrastructure in an intelligent manner. For achieving this performance enhancement, a mapping technique is proposed that is built based on the application’s virtual topology as well as the physical topology of the undelaying resources. To the best of our knowledge, there is no existing method in big data applications related to utilizing graphics processing units (GPUs), which are now an essential part of high-performance computing (HPC) as a powerful resource for fast computation.
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Baldi, Pierre, and Yves Chauvin. "Hybrid Modeling, HMM/NN Architectures, and Protein Applications." Neural Computation 8, no. 7 (October 1996): 1541–65. http://dx.doi.org/10.1162/neco.1996.8.7.1541.

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We describe a hybrid modeling approach where the parameters of a model are calculated and modulated by another model, typically a neural network (NN), to avoid both overfitting and underfitting. We develop the approach for the case of Hidden Markov Models (HMMs), by deriving a class of hybrid HMM/NN architectures. These architectures can be trained with unified algorithms that blend HMM dynamic programming with NN backpropagation. In the case of complex data, mixtures of HMMs or modulated HMMs must be used. NNs can then be applied both to the parameters of each single HMM, and to the switching or modulation of the models, as a function of input or context. Hybrid HMM/NN architectures provide a flexible NN parameterization for the control of model structure and complexity. At the same time, they can capture distributions that, in practice, are inaccessible to single HMMs. The HMM/NN hybrid approach is tested, in its simplest form, by constructing a model of the immunoglobulin protein family. A hybrid model is trained, and a multiple alignment derived, with less than a fourth of the number of parameters used with previous single HMMs.
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Janeiro, Fernando M., and Pedro M. Ramos. "Gene expression programming and genetic algorithms in impedance circuit identification." ACTA IMEKO 1, no. 1 (June 6, 2012): 19. http://dx.doi.org/10.21014/acta_imeko.v1i1.16.

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Impedance circuit identification through spectroscopy is often used to characterize sensors. When the circuit topology is known, it has been shown that the component values can be obtained by genetic algorithms. Also, gene expression programming can be used to search for an adequate circuit topology. In this paper, an improved version of the impedance circuit identification based on gene expression programming and hybrid genetic algorithm is presented to both identify the circuit and estimate its parameters. Simulation results are used to validate the proposed algorithm in different situations. Further validation is presented from measurements on a circuit that models a humidity sensor and also from measurements on a viscosity sensor.
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Terekhov, D., and J. C. Beck. "A Constraint Programming Approach for Solving a Queueing Control Problem." Journal of Artificial Intelligence Research 32 (May 19, 2008): 123–67. http://dx.doi.org/10.1613/jair.2446.

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In a facility with front room and back room operations, it is useful to switch workers between the rooms in order to cope with changing customer demand. Assuming stochastic customer arrival and service times, we seek a policy for switching workers such that the expected customer waiting time is minimized while the expected back room staffing is sufficient to perform all work. Three novel constraint programming models and several shaving procedures for these models are presented. Experimental results show that a model based on closed-form expressions together with a combination of shaving procedures is the most efficient. This model is able to find and prove optimal solutions for many problem instances within a reasonable run-time. Previously, the only available approach was a heuristic algorithm. Furthermore, a hybrid method combining the heuristic and the best constraint programming method is shown to perform as well as the heuristic in terms of solution quality over time, while achieving the same performance in terms of proving optimality as the pure constraint programming model. This is the first work of which we are aware that solves such queueing-based problems with constraint programming.
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Zakwan, Mohammad, and Majid Niazkar. "A Comparative Analysis of Data-Driven Empirical and Artificial Intelligence Models for Estimating Infiltration Rates." Complexity 2021 (May 4, 2021): 1–13. http://dx.doi.org/10.1155/2021/9945218.

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Infiltration is a vital phenomenon in the water cycle, and consequently, estimation of infiltration rate is important for many hydrologic studies. In the present paper, different data-driven models including Multiple Linear Regression (MLR), Generalized Reduced Gradient (GRG), two Artificial Intelligence (AI) techniques (Artificial Neural Network (ANN) and Multigene Genetic Programming (MGGP)), and the hybrid MGGP-GRG have been applied to estimate the infiltration rates. The estimated infiltration rates were compared with those obtained by empirical infiltration models (Horton’s model, Philip’s model, and modified Kostiakov’s model) for the published infiltration data. Among the conventional models considered, Philip’s model provided the best estimates of infiltration rate. It was observed that the application of the hybrid MGGP-GRG model and MGGP improved the estimates of infiltration rates as compared to conventional infiltration model, while ANN provided the best prediction of infiltration rates. To be more specific, the application of ANN and the hybrid MGGP-GRG reduced the sum of square of errors by 97.86% and 81.53%, respectively. Finally, based on the comparative analysis, implementation of AI-based models, as a more accurate alternative, is suggested for estimating infiltration rates in hydrological models.
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Pan, Chaofeng, Yanyan Liang, Long Chen, and Liao Chen. "Optimal Control for Hybrid Energy Storage Electric Vehicle to Achieve Energy Saving Using Dynamic Programming Approach." Energies 12, no. 4 (February 13, 2019): 588. http://dx.doi.org/10.3390/en12040588.

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In this paper, the efficiency characteristics of battery, super capacitor (SC), direct current (DC)-DC converter and electric motor in a hybrid power system of an electric vehicle (EV) are analyzed. In addition, the optimal efficiency model of the hybrid power system is proposed based on the hybrid power system component’s models. A rule-based strategy is then proposed based on the projection partition of composite power system efficiency, so it has strong adaptive adjustment ability. Additionally. the simulation results under the New European Driving Cycle (NEDC) condition show that the efficiency of rule-based strategy is higher than that of single power system. Furthermore, in order to explore the maximum energy-saving potential of hybrid power electric vehicles, a dynamic programming (DP) optimization method is proposed on the basis of the establishment of the whole hybrid power system, which takes into account various energy consumption factors of the whole system. Compared to the battery-only EV based on simulation results, the hybrid power system controlled by rule-based strategy can decrease energy consumption by 13.4% in line with the NEDC condition, while the power-split strategy derived from the DP approach can reduce energy consumption by 17.6%. The results show that compared with rule-based strategy, the optimized DP strategy has higher system efficiency and lower energy consumption.
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Liu, Gao Ming, Xiao Bo Wang, Wei Song, and Chong Zhan Li. "The Hybrid Parallel Algorithm of Online Verification Based on P2P." Applied Mechanics and Materials 373-375 (August 2013): 1251–55. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1251.

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Taking into account the gradually expanded scale of large interconnected power system, especially the UHV synchronous grid successfully puts into operation. Traditional centralized computing will encounter the bottleneck of hardware computing power. A protection setting online checking hybrid parallel algorithm based on P2P is proposed. Peer-to-peer communications using P2P network technology to achieve inter-regional information fast interactive. The design of MPI+OpenMP hybrid parallel programming models and algorithms is emphatically introduced. Online checking two levels parallel of process-level and thread-level is achieved through the parallel analysis of online checking. Finally, the hybrid parallel algorithm was tested and compared based on P2P technology distributed parallel computing platform. The results show that the proposed algorithm is correct and effective.
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