Search Results - (( using computational grid algorithm ) OR ( simulation optimization method algorithm ))

DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS by MEHMOOD SHAH, SYED NASIR

“…The four prime aspects of this work are: firstly, a model of the grid scheduling problem for dynamic grid computing environment; secondly, development of a new web based simulator (SyedWSim), enabling the grid users to conduct a statistical analysis of grid workload traces and provides a realistic basis for experimentation in resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new grid resource allocation method of optimal computational cost using synthetic and real workload traces with respect to other allocation methods; and finally, proposal of some new job scheduling algorithms of optimal performance considering parameters like waiting time, turnaround time, response time, bounded slowdown, completion time and stretch time. …”
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Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid by Lorpunmanee, Siriluck, Md Sap, Mohd Noor, Abdullah, Abdul Hanan

“…In this paper, we combine Fuzzy C-Mean and Genetic Algorithms which are popular algorithms, the Grid can be used for scheduling. …”
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Meta-scheduler in Grid environment with multiple objectives by using genetic algorithm by Lorpunmanee, Siriluck, Sap, M.N.M, Abdullah, Abdul Hanan, Srinoy, Surat

“…The simulation has been obtained using historical information to study the job scheduling in Grid environment. …”
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Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method by Abbaszadeh M., Soltani-Mohammadi S., Ahmed A.N.

“…Copper deposits; Deposits; Geology; Learning algorithms; Mineralogy; Static Var compensators; Support vector machines; Three dimensional computer graphics; Alteration zones; Grid search; Grid-search method; Mineralization zone; Model Selection; Particle swarm optimization algorithm; Penalty parameters; Performance; Support vector classifiers; Support vectors machine; Particle swarm optimization (PSO); accuracy assessment; algorithm; classification; computer simulation; copper; geological survey; mineral alteration; mineralization; numerical model; ore deposit; parameterization; performance assessment; porphyry; resource assessment; support vector machine; three-dimensional modeling; Iran…”

Optimization of substation grounding grid design for horizontal and vertical multilayer and uniform soil condition using Simulated Annealing method by Permal N., Osman M., Ariffin A.M., Boopalan N., Abidin Ab Kadir M.Z.

Optimizing BOINC scheduling using genetic algorithm based on thermal profile by Binti, N.N., Zakaria, M.N.B., Aziz, I.B.A., Binti, N.S.

“…Universiti Teknologi Petronas (UTP) campus grid used BOINC as middleware in computer labs. However, computer can only process jobs during weekday and office hour because they want to reduce energy used for cooling power. …”
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Workload utilization dissemination on grid resources for simulation environment by Yahaya, Bakri, Latip, Rohaya, Abdullah, Azizol, Othman, Mohamed

“…Among these factors are the manipulation of computer RI's, type of workload information with method of use, the workload dissemination direction along with implementation method and using certain algorithm to come out with new integrated scheduling with load balancing capability. …”
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Performance comparisons of sequential and asynchronous iterative water filling algorithm in cognitive smart grid communications by Hiew Y.-K., Aripin N.M., Din N.M.

Mapping of hydrocarbon with marine EM method using 2-D forward modeling by Hussain, Nazabat, Karsiti , Mohd Noh, Yahya, Norashikin, Burhanudin, Zainal Arif

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Transmission path optimization Based on Efficiency Communication System by Jin Fan, Kit, Guan Lim, Sin, Helen Ee Chuo, Min, Keng Tan, Ali Farzamnia, Tze, Kenneth Kin Teo

“…In this paper, routing attribute table that is maintained by the nodes, the density and relative distance of node distribution, is used to determine the clustering method. At the same time, the developed algorithm combines the relative position of the cluster and the base station to further improve the routing method of the sub-cluster head, Finally, the algorithm analyzes the influence of different parameters on the transmission path, and use the simulation experiments to evaluate the conclusions.…”
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An integrated reservoir modelling and evolutionary algorithm for optimizing field development in a mature fractured reservoir by Sambo, C.H., Hematpour, H., Danaei, S., Herman, M., Ghosh, D.P., Abass, A., Elraies, K.A.

“…The second method is automatic optimization using Genetic Algorithm. …”
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Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques by Vasant, P.M., Rahman, I., Singh, B.S.M., Abdullah-Al-Wadud, M.

“…However, PSOGSA method takes much longer computational time than single methods because of incorporating two single methods in one algorithm. …”
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Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques by Vasant, P.M., Rahman, I., Singh, B.S.M., Abdullah-Al-Wadud, M.

“…However, PSOGSA method takes much longer computational time than single methods because of incorporating two single methods in one algorithm. …”
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Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation by Ahmadipour, Masoud

“…Furthermore, to evaluate the efficiency of the proposed modified differential evolution for the training of ridgelet probabilistic neural network, four statistical search techniques, namely, particle swarm optimization, genetic algorithm, simulated angling, and classical differential evolution are used and their results are compared. …”
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Numerical simulation of fluid flow in three dimensional domain based on two stage pressure-velocity correction method by Sabir, O., Ya, T.M.Y.S.T.

“…A second correction step is added to the pressure value and the velocity vectors using Simplified Marker and Cell method (SMAC). This new scheme is applied on staggered grid using implicit finite difference methods in order to achieve second order accuracy. …”
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Numerical simulation of fluid flow in three dimensional domain based on two stage pressure-velocity correction method by Sabir, O., Ya, T.M.Y.S.T.

“…A second correction step is added to the pressure value and the velocity vectors using Simplified Marker and Cell method (SMAC). This new scheme is applied on staggered grid using implicit finite difference methods in order to achieve second order accuracy. …”
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Design and modelling of an autonomous robotic airship with soft computing control by Wong, Wei Kitt

“…Along with the prototype airship, a path planner was also proposed for the UAV for the purpose of obstacle avoidance and path optimization using genetic algorithm to minimize on the distance travelled and to generate the shortest path to cover the specified waypoints. …”
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A multi-input converter for hybrid photovoltaic array/wind turbine/fuel cell and battery storage system connected AC grid network by Rosli, M.A., Yahaya, N.Z., Baharudin, Z.

“…The perturbation and observation (P&O) method is mainly used to accomplish the maximum power point tracking (MPPT) algorithm for PV array and WT sources and set FC operation power on optimal range by proton exchange membrane fuel cell (PEMFC). …”
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Optimization and assessment of substation grounding grid designs in non-homogeneous soil conditions by Navinesshani A/P Permal, Dr.

“…Moreover, a good grounding system should not only be efficient but also economical. An optimisation method established from the Simulated Annealing (SA) algorithm is applied to search for an optimal grounding design solution. …”

Optimization-driven extreme learning machine for floating photovoltaic power prediction: A teaching learning-based approach by Mohd Redzuan, Ahmad, Nor Farizan, Zakaria, Mohd Shawal, Jadin, Mohd Herwan, Sulaiman

“…This study presents a novel Teaching–Learning-Based Optimization enhanced Extreme Learning Machine (TLBO-ELM) framework that achieves optimal parameter configuration without algorithmic tuning while maintaining computational efficiency for real-time deployment. …”
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