Search Results - (( pressure distribution genetic algorithm ) OR ( parameters application based algorithm ))*

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  1. 1
    Development of cost reduction mathematical model for natural gas transmission network system

    Development of cost reduction mathematical model for natural gas transmission network system by Mehrdad, Nikbakht Eliaderany

    Published 2012
    “…In this research, an Extended Genetic Algorithms (EGA) was investigated to solve the proposed mathematical model and to achieve global or near to global optimum solutions in a reasonable time. …”
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  2. 2
    Improvement of Cepstrum Analysis for the Purpose to Detect Leak, Feature and Its Location in Water Distribution System based on Pressure Transient Analysis

    Improvement of Cepstrum Analysis for the Purpose to Detect Leak, Feature and Its Location in Water Distribution System based on Pressure Transient Analysis by Hanafi, M. Yusop, M. F., Ghazali, M. F. M., Yusof, Wan Sofian, Wan Hamat

    Published 2017
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  3. 3
    Optimization of pressure vessel design using pyopt

    Optimization of pressure vessel design using pyopt by Woldemichael, D.E., Woldeyohannes, A.D.

    Published 2016
    “…In this study, we used pyOpt to solve pressure vessel design problem. Among the available optimizers in pyOpt, SLSQP (Sequential least squares programming), COBYLA (Constrained Optimization by Linear Approximation), ALPSO (Augmented Lagrangian Particle Swarm Optimizer), NSGAII (Non Sorting Genetic Algorithm II), MIDACO (Mixed Integer Distributed Ant Colony Optimization), and ALGENCAN (Augmented Lagrangian with GENCAN) were used. …”
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  4. 4
    Optimization of pressure vessel design using pyopt

    Optimization of pressure vessel design using pyopt by Woldemichael, D.E., Woldeyohannes, A.D.

    Published 2016
    “…In this study, we used pyOpt to solve pressure vessel design problem. Among the available optimizers in pyOpt, SLSQP (Sequential least squares programming), COBYLA (Constrained Optimization by Linear Approximation), ALPSO (Augmented Lagrangian Particle Swarm Optimizer), NSGAII (Non Sorting Genetic Algorithm II), MIDACO (Mixed Integer Distributed Ant Colony Optimization), and ALGENCAN (Augmented Lagrangian with GENCAN) were used. …”
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  5. 5
    Improvement of Cepstrum Analysis for the Purpose to Detect Leak, Feature and Its Location in Water Distribution System based on Pressure Transient Analysis / Hanafi.M.Yusop ...[et al.]

    Improvement of Cepstrum Analysis for the Purpose to Detect Leak, Feature and Its Location in Water Distribution System based on Pressure Transient Analysis / Hanafi.M.Yusop ...[et... by M.Yusop, Hanafi, Ghazali, M.F., M.Yusof, M.F., W.Hamat, W.S.

    Published 2019
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  6. 6
    A Comparison Study Between Two Algorithms Particle Swarm Optimization for Depth Control of Underwater Remotely Operated Vehicle

    A Comparison Study Between Two Algorithms Particle Swarm Optimization for Depth Control of Underwater Remotely Operated Vehicle by Mohd Aras, Mohd Shahrieel, Jaafar, Hazriq Izzuan, Razilah , Abdul Rahim, Ahmad , Arfah

    Published 2013
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  7. 7
    A novel normal parameter reduction algorithm of soft sets

    A novel normal parameter reduction algorithm of soft sets by Ma, Xiuqin, Norrozila, Sulaiman, Qin, Hongwu, Herawan, Tutut

    Published 2010
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  8. 8
    An alternative approach to normal parameter reduction algorithms for decision making using a soft set theory / Sani Danjuma

    An alternative approach to normal parameter reduction algorithms for decision making using a soft set theory / Sani Danjuma by Sani , Danjuma

    Published 2017
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  9. 9
    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh

    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

    Published 2015
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  10. 10
    A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application

    A simulation based fly optimisation algorithm for swarms of mini autonomous surface vehicles application by Zainal Abidin, Zulkifli

    Published 2011
    “…The optimal simulation parameters can be used for the real application. FOA is suitable for applications that need a small number of agents; in the range of 8 to 24 only. …”
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  11. 11
    Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems

    Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems by Kek, Sie Long

    Published 2011
    “…The main idea is the integration of optimal control and parameter estimation. In this work, a simplified model-based optimal control model with adjustable parameters is constructed. …”
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  12. 12
    Fuzzy Logic Based Self-Adaptive Handover Algorithm for MobileWiMAX.

    Fuzzy Logic Based Self-Adaptive Handover Algorithm for MobileWiMAX. by Mohd Ali, Borhanuddin, Ismail, Alyani, Noordin, Nor Kamariah, Ben-Mubarak, Mohammed A., Ng , Ben Chee Kyun

    Published 2013
    “…The proposed FuzSAHO algorithm will first self-adapt the handover parameters based on a set of multiple criteria, which includes the RSSI and MS velocity. …”
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  13. 13
    On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing

    On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing by Kamal Z., Zamli, Alsariera, Yazan A., Nasser, Abdullah B., Alsewari, Abdulrahman A.

    Published 2015
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  14. 14
    Finite impulse response optimizers for solving optimization problems

    Finite impulse response optimizers for solving optimization problems by Ab Rahman, Tasiransurini

    Published 2019
    “…In this work, three new estimation-based metaheuristic algorithms are introduced. The first algorithm is a single-agent-based algorithm, named Single-agent FIR optimizer (SAFIRO). …”
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  15. 15
    Enhanced gravitational search algorithm for nano-process parameter optimization problem / Norlina Mohd Sabri

    Enhanced gravitational search algorithm for nano-process parameter optimization problem / Norlina Mohd Sabri by Mohd Sabri, Norlina

    Published 2020
    “…Based on the capabilities of the metaheuristic algorithms, this research is proposing the enhanced Gravitational Search Algorithm (eGSA) to solve the nano-process parameter optimization problem. …”
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  16. 16
    Finite impulse response optimizers for solving optimization problems

    Finite impulse response optimizers for solving optimization problems by Tasiransurini, Ab Rahman

    Published 2019
    “…In this work, three new estimation-based metaheuristic algorithms are introduced. The first algorithm is a single-agent-based algorithm, named Single-agent FIR optimizer (SAFIRO). …”
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  17. 17
    Application of weighted average on modal parameters for damage detection algorithms: Case study on steel beam

    Application of weighted average on modal parameters for damage detection algorithms: Case study on steel beam by Fayyadh, M.M., Abdul Razak, H.

    Published 2011
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  18. 18
    LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting

    LS-SVM Hyper-parameters Optimization Based on GWO Algorithm for Time Series Forecasting by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, M. N. M., Kahar

    Published 2015
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  19. 19
    Optimization of turning parameters using genetic algorithm method

    Optimization of turning parameters using genetic algorithm method by Shah Izwandi, Mohd Zawawi

    Published 2008
    “…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). This method was demonstrated for the optimization of machining parameters for turning operation using conventional lathe machines. …”
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  20. 20
    Semiparametric inference procedure for the accelarated failure time model with interval-censored data

    Semiparametric inference procedure for the accelarated failure time model with interval-censored data by Karimi, Mostafa

    Published 2019
    “…A computationally simple two-step iterative algorithm, called estimationapproximation algorithm, is introduced for estimating the parameters of the model on the basis of the rank estimators. …”
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