Search Results - (( using optimization _ algorithm ) OR ( variable equations using algorithm ))

Optimization of biochemical systems production using combination of newton method and particle swarm optimization by Mohd Arfian, Ismail, Mezhuyev, Vitaliy, Irfan, Darmawan, Shahreen, Kasim, Mohd Saberi, Mohamad, Ashraf Osman, Ibrahim

“…In the proposed method, the Newton method was used to deal with nonlinear equations system, while the PSO algorithm was utilized to fine-tune the variables in nonlinear equations system. …”
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Taguchi?s T-method with Normalization-Based Binary Bat Algorithm by Marlan Z.M., Jamaludin K.R., Harudin N.

“…Therefore, a variable selection technique using a swarm-based Binary Bat algorithm is proposed. …”

Optimization Of Sliding Mode Control Using Particle Swarm Algorithm For An Electro-Hydraulic Actuator System by Rozaimi, Ghazali

“…The dynamic parts of electro-hydraulic actuator(EHA) system are widely applied in the industrial field for the process that exposed to the motion control.In order to achieve accurate motion produced by these dynamic parts,an appropriate controller will be needed.However,the EHA system is well known to be nonlinear in nature.A great challenge is carried out in the EHA system modelling and the controller development due to its nonlinear characteristic and system complexity.An appropriate controller with proper controller parameters will be needed in order to maintain or enhance the performance of the utilized controller.This paper presents the optimization on the variables of sliding mode control (SMC) by using Particle Swarm Optimization (PSO) algorithm.The control scheme is established from the derived dynamic equation which stability is proven through Lyapunov theorem.From the obtained simulation results,it can be clearly inferred that the SMC controller variables tuning through PSO algorithm performed better compared with the conventional proportionalintegral-derivative (PID) controller.…”
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Weather prediction in Kota Kinabalu using linear regressions with multiple variables by Teong, Khan Vun, Chung, Gwo Chin, Jedol Dayou

“…This study employs machine learning algorithms, a linear regression model using statistics, and two optimization approaches, the normal equation approach, and gradient descent approach to predict the weather based on a few variables. …”
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Modification of particle swarm optimization algorithm for optimization of discrete values by Mohd Yassin, Ahmad Ihsan, Jusoh, Muhammad Huzaimy, Abdul Rahman, Farah Yasmin

“…Stochastic optimization algorithms are a new breed of optimizers that have recently been developed. …”
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EFFICIENCY IMPROVEMENT OF FLAT PLATE SOLAR COLLECTOR USING SEARCH GROUP ALGORITHM by ABD RASHID, NURSABRINA

“…This document proposes new optimization technique, the Search Group Algorithm (SGA), to optimize the efficiency of flat plate solar collector.…”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli by Seyed Hamidreza , Aghay Kaboli

“…This merit is provided by balancing the exploitation of solution structure and exploration of its appropriate weighting factors through use of a robust and efficient optimization algorithm in learning process of GEP approach. …”
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Independent And Dependent Job Scheduling Algorithms Based On Weighting Model For Grid Environment by M. Al-Najjar, Hazem

“…The results have demonstrated that the categorical and continuous variables of jobs can be used to improve the total execution time and average waiting time of job scheduling algorithms with less overhead in a real environment.…”
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A Hybrid of Optimization Method for Multi-Objective Constraint Optimization of Biochemical System Production by Mohd Arfian, Ismail, Safaai, Deris, Mohd Saberi, Mohamad, Mohd Adham, Isa, Afnizanfaizal, Abdullah, Muhammad Akmal, Remli, Mohi-Aldeen, Shayma Mustafa

“…The proposed method starts with Newton method by treating the biochemical system as a non-linear equations system. Then, Genetic Algorithm (GA) in SPEA and CCA were used to represent the variables in non-linear equations system into multiple sub-chromosomes. …”
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Comparative study of modified BFGS and new scale modified BFGS for solving unconstrained optimization / Shahirah Atikah Mohamad Husnin by Mohamad Husnin, Shahirah Atikah

“…This indicated that the new scaled mBFGS algorithm performance is better than mBFGS algorithms.…”
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The efficiency of conjugate gradient methods with global convergence / Siti Nur Hafiza Shamsudin by Shamsudin, Siti Nur Hafiza

“…Conjugate gradient methods are usually used to solve any problem that related to large number of variables such as a large linear system of equations. …”
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Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method by Mohd Arfian, Ismail, Mezhuyev, Vitaliy, Safaai, Deris, Mohd Saberi, Mohamad, Shahreen, Kasim, Saedudin, Rd Rohmat

“…The operation of the proposed method starts with Newton method by dealing with biochemical system production as a nonlinear equations system. Then DE and ComCA are used to represent the variables in nonlinear equation system and tune the variables in order to find the best solution. …”
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Thin Film Roughness Optimization In The Tin Coatings Using Genetic Algorithms by Fauzi, Nur Faiqah, Mohamad Jaya, Abdul Syukor, Mohammad Jarrah, Mu’ath Ibrahim, Akbar, Habibullah

“…Genetic algorithms were used in the optimization work of the coating process to optimize the coating roughness parameters. …”
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An improved electromagnetism-like mechanism algorithm for the optimization of maximum power point tracking / Tan Jian Ding by Tan, Jian Ding

“…The Electromagnetism-Like Mechanism algorithm (EM) is a meta-heuristic algorithm designed to search for global optimum solutions using bounded variables. …”
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OPTIMIZATION OF DESIGN PARAMETERS FOR A VARIABLE FREQUENCY 3-PHASE INDUCTION MOTOR by APPLANAIDU, VISHNUV ARTHA

“…The objective function achieved from the design are cost, efficiency and weight. The optimization technique used to optimize this objective functions is Genetic Algorithm (GA) which is a Non -Linear Programming technique. …”
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Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models by Quadros, Jaimon Dennis, Khan, Sher Afghan, Aabid, Abdul, Baig, Muneer

“…The data for training and testing the algorithms was derived using the regression equation developed using the Box-Behnken Design (BBD). …”
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Implementation of simulated annealing for two variables non-linear function by Engku Ahmad Rafiqi, Engku Ariff, Sakinah, Zakaria, Maliki, Ibrahim

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Variable Neighborhood Descent and Whale Optimization Algorithm for Examination Timetabling Problems at Universiti Malaysia Sarawak by Emily Sing Kiang, Siew

“…A constructive algorithm was developed to generate an initial feasible solution, which was subsequently refined using two primary approaches to evaluate their efficiency: Iterative Threshold Pipe Variable Neighborhood Descent (IT-PVND), and a modified Whale Optimization Algorithm (WOA). …”
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Partial Newton methods for a system of equations by Goh, Bean San, Leong, Wah June, Siri, Zailan

“…But the proposed partial Newton iteration makes it significantly simpler and faster to compute in each iteration for a system of equations with many variables. This is because it uses only one or two variables instead of all the search variables in each iteration.…”
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Modeling And Optimization Of Physical Vapour Deposition Coating Process Parameters For Tin Grain Size Using Combined Genetic Algorithms With Response Surface Methodology by Mohamad Jaya, Abdul Syukor, Muhamad, Mohd Razali, Abd Rahman, Md Nizam, Mohammad Jarrah, Mu'ath Ibrahim, Hasan Basari, Abd Samad

“…Additionally,analysis of variance(ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters, genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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