Search Results - (( parameter optimization _ algorithm ) OR ( variable integration process algorithm ))

Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization by Nur Iffah, Mohamed Azmi

“…Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. …”
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Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization by Nur Iffah, Mohamed Azmi, Kamal Arifin, Mat Piah, Wan Azhar, Wan Yusoff, F. R. M., Romlay

“…Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. …”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

“…Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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Integrated ACOR/IACOMV-R-SVM Algorithm by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…The first algorithm, ACOR-SVM, will tune SVM parameters, while the second IACOMV-R-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. …”
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Mixed variable ant colony optimization technique for feature subset selection and model selection by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…This paper presents the integration of Mixed Variable Ant Colony Optimization and Support Vector Machine (SVM) to enhance the performance of SVM through simultaneously tuning its parameters and selecting a small number of features.The process of selecting a suitable feature subset and optimizing SVM parameters must occur simultaneously,because these processes affect each ot her which in turn will affect the SVM performance.Thus producing unacceptable classification accuracy.Five datasets from UCI were used to evaluate the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with the small size of features subset.…”
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Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decisio... by Daba, Layth Muwafaq Abdulhussein

“…Unlike the existing optimization algorithm, VL-WIDE features the capability of searching different lengths of solutions to cover the variable number of cloudlets for deployment. …”
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Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization by Silitonga A.S., Shamsuddin A.H., Mahlia T.M.I., Milano J., Kusumo F., Siswantoro J., Dharma S., Sebayang A.H., Masjuki H.H., Ong H.C.

Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization by Lee, Jesee Kar Ming

“…The ANN model is further improved using GA and PSO. Each algorithm has its own parameters and is further optimized using RSM. …”
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Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms by Ehteram M., Yenn Teo F., Najah Ahmed A., Dashti Latif S., Feng Huang Y., Abozweita O., Al-Ansari N., El-Shafie A.

Solving SVM model selection problem using ACOR and IACOR by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…In applying ACO for optimizing SVM parameters which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretize process would result in loss of some information and hence affect the classification accuracy.In order to enhance SVM performance and solving the discretization problem, this study proposes two algorithms to optimize SVM parameters using Continuous ACO (ACOR) and Incremental Continuous Ant Colony Optimization (IACOR) without the need to discretize continuous value for SVM parameters.Eight datasets from UCI were used to evaluate the credibility of the proposed integrated algorithm in terms of classification accuracy and size of features subset.Promising results were obtained when compared to grid search technique, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM. …”
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Intelligence Integration Of Particle Swarm Optimization And Physical Vapour Deposition For Tin Grain Size Coating Process Parameters by Abdul Syukor, Mohamad Jaya, Mu*ath Ibrahim, Mohammad Jarrah, Mohd Asyadi Azam, Mohd Abid, Mohd Razali, Muhamad

“…Optimization of thin film coating parameters is important in identifying the required output.Two main issues of the process of physical vapor deposition (PVD) are manufacturing costs and customization of cutting tool properties.The aim of this study is to identify optimal PVD coating process parameters.Three process parameters were selected,namely nitrogen gas pressure (N2),argon gas pressure (Ar),and Turntable Speed (TT),while thin film grain size of titanium nitrite (TiN) was selected as an output response.Coating grain size was characterized using Atomic Force Microscopy (AFM) equipment.In this paper,to obtain a proper output result,an approach in modeling surface grain size of Titanium Nitrite (TiN)coating using Response Surface Method (RSM) has been implemented. …”
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Optimization of medical image steganography using n-decomposition genetic algorithm by Al-Sarayefi, Bushra Abdullah Shtayt

“…To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. …”
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Optimizing n-1 contingency rankings using a nature-inspired modified sine cosine algorithm by Priyadi, Irnanda, Daratha, Novalio, Gunawan, Teddy Surya, Ramli, Kalamullah, Jalistio, Febrian, Mokhlis, Hazlie

“…The MSCA method is validated using the IEEE 30-bus test case, focusing on optimal parameter tuning for population size, iterations, and key variables. …”
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Rainfall prediction using multiple inclusive models and large climate indices by Mohamadi S., Sheikh Khozani Z., Ehteram M., Ahmed A.N., El-Shafie A.

Multi-objective optimization of process variables for MWCNT-added electro-discharge machining of 316L steel by Al-Amin, M., Abdul-Rani, A.M., Ahmed, R., Shahid, M.U., Zohura, F.T., Rani, M.D.B.A.

“…The best 21 solution sets predicted through the multi-objective optimization tool called non-dominated sorting genetic algorithm-II (NSGA-II) obeying the set objective functions are proposed which are obtained from the Pareto optimal frontiers. …”
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Multi-objective optimization of process variables for MWCNT-added electro-discharge machining of 316L steel by Al-Amin, M., Abdul-Rani, A.M., Ahmed, R., Shahid, M.U., Zohura, F.T., Rani, M.D.B.A.

“…The best 21 solution sets predicted through the multi-objective optimization tool called non-dominated sorting genetic algorithm-II (NSGA-II) obeying the set objective functions are proposed which are obtained from the Pareto optimal frontiers. …”
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Modelling of cupping suction system based on system identification method by Suresh, Kavindran, Ghazali, M. R., Ahmad, M. A.

“…The detection of cupped suction system plants using a standard model based on a modified Sine Cosine Algorithm (mSCA) is presented in this research. According to the findings, the mSCA-based technique can produce optimal parameters of model that provides an identified output response comparable to the actual experiment's cupping suction system output, with an integral square error for various random input surfaces and its objective function. …”
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Online auto-tuned proportional-integral controller using particle swarm optimization for dual active bridge DC-DC converter by Suliana, Ab Ghani

“…APSO-PI is an auto-tuned PI using the PSO algorithm where the optimal values of Kp and Kiwere tuned at the initial control process only. …”
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Operational matrix based on orthogonal polynomials and artificial neural networks methods for solving fractal-fractional differential equations by Shloof, Aml Melad Asan

“…This discovery indicates convergence toward optimal model coefficients as the learning process advances. …”
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Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems by Kek, Sie Long

“…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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