Search Results - (( (variable OR variables) evaluation using algorithm ) OR ( using optimization based algorithm ))

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

“…Comparing this developed algorithm with other algorithms shows its superiority in multi-objective optimization (MOO) evaluation metrics. …”
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A firefly algorithm based hybrid method for structural topology optimization by Gebremedhen, H.S., Woldemichael, D.E., Hashim, F.M.

“…The lower and upper limit of design variables (0 and 1) were used to find initial material distribution to initialize the firefly algorithm based section of the hybrid algorithm. …”
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Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data by Qadir Saraf, Tara Othman, Fuad, Norfaiza, Md Taujuddin, Nik Shahidah Afifi

“…The evaluation showed the overall superiority of VLBHO over the other algorithms in terms of accomplishing lower fitness values when optimizing mathematical functions of the variable length type.…”
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Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data by Qadir Saraf, Tara Othman, Fuad, Norfaiza, Md Taujuddin, Nik Shahidah Afifi

“…The evaluation showed the overall superiority of VLBHO over the other algorithms in terms of accomplishing lower fitness values when optimizing mathematical functions of the variable length type.…”
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Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data by Qadir Sara, Tara Othman, Fuad, Norfaiza, Md Taujuddin, Nik Shahidah Afifi

“…The evaluation showed the overall superiority of VLBHO over the other algorithms in terms of accomplishing lower fitness values when optimizing mathematical functions of the variable length type.…”
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Using Metaheuristics Algorithms (MHAs) to Optimize Water Supply Operation in Reservoirs: a Review by Almubaidin M.A.A., Ahmed A.N., Sidek L.B.M., Elshafie A.

Enhanced Flipping Technique to Reduce Variability in Image Steganography by Kamil S., Abdullah S.N.H.S., Hasan M.K., Bohani F.A.

Identification of continuous-time hammerstein model using improved archimedes optimization algorithm by Islam, Muhammad Shafiqul, Mohd Ashraf, Ahmad, Cho, Bo Wen

“…Therefore, this article identified various continuous-time Hammerstein models based on an improved Archimedes optimization algorithm (IAOA) to address these concerns. …”
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The performance of Taguchi�s T-method with binary bat algorithm based on great value priority binarization for prediction by Marlan Z.M., Ramlie F., Jamaludin K.R., Harudin N.

“…This paper proposes an optimization algorithm based on the Binary Bat algorithm methodology for replacing the conventional orthogonal array approach. …”

Harmony search-based robust optimal controller with prior defined structure by Rafieishahemabadi, Ali

“…In this approach, a combination of interacting two levels HS optimization algorithm is presented. In the first level, a new method for analytical formulation of integral square error cost function based on controller variables is elaborated for performance evaluation purposes by the proposed optimization algorithm. …”
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Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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Vector Evaluated Gravitational Search Algorithm (VEGSA) for multi-objective optimization problems by Zuwairie, Ibrahim, Badaruddin, Muhammad, Kamarul Hawari, Ghazali, Lim, Kian Sheng, Sophan Wahyudi, Nawawi, Zulkifli, Md. Yusof

“…The proposed algorithm, which is called Vector Evaluated Gravitational Search Algorithm (VEGSA), uses a number of populations of particles. …”
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An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time by Mirhassani, S.M., Golroodbari, S.Z.M, Goiroodbari, S.M.M., Mekhilef, Saad

“…The performance of the proposed system is evaluated using MATLAB simulation and experimentation, in which a digital signal controller is used to implement the proposed algorithm on a real boost converter connected to a PV simulator. …”
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Performance Evaluation of Vector Evaluated Gravitational Search Algorithms Based on ZDT Test Functions by Badaruddin, Muhammad, Zuwairie, Ibrahim, Kamarul Hawari, Ghazali, Mohd Riduwan, Ghazali, Kian, Sheng Lim, Sophan Wahyudi, Nawawi, Nor Azlina, Ab. Aziz, Marizan, Mubin, Norrima, Mokhtar

“…This paper presents a performance evaluation of Vector Evaluated Gravitational Search Algorithm (VEGSA), namely VEGSA-I and VEGSA-II algorithms, for multi-objective optimization problems. …”
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Single and Multiple variables control using Tree Physiology Optimization by Halim, A.H., Ismail, I.

“…The proposed algorithm is also compared with deterministic gradient-free algorithm: Nelder-Mead simplex (NMS) and another metaheuristic algorithm: Particle Swarm Optimization (PSO). …”
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Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance by Allawi, Mohammed Falah, Jaafar, Othman, Mohamad Hamzah, Firdaus, Koting, Suhana, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

“…The three different optimization algorithms used in this study are the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and shark machine learning algorithm (SMLA). …”
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Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification by Abd Samad, Md Fahmi

“…Identification studies using NARX (Nonlinear AutoRegressive with eXogenous input) models employing simulated systems and real plant data are used to demonstrate that the algorithm is able to detect significant variables and terms faster and to select a simpler model structure than other well-known EC methods.…”
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

“…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

“…In general, genetic based clustering algorithms showed the ability to reach near global optimal solution. …”
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Modeling forest fires risk using spatial decision tree by Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin, Sitanggang, Imas Sukaesih

“…Forest fire models have been developed for certain area using the weightage and criterion of variables that involve the subjective and qualitative judging for variables. …”
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