Search Results - (( simulation optimization using algorithm ) OR ( using vector valued algorithm ))
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Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier
Published 2013“…In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous values and simultaneously solve the two problems.This paper presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features.…”
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Simulated kalman filter with modified measurement, substitution mutation and hamming distance calculation for solving traveling salesman problem
Published 2022“…Purpose – The purpose of the research is to solve Travelling Salesman Problem (TSP) using Simulated Kalman Filter (SKF) algorithm and single-solution SKF (ssSKF) algorithm based on numerical ordering technique. …”
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Compressed Sensing Implementations For Sparse Channel Estimation In OFDM Systems
Published 2018“…Simulation results demonstrate that the proposed CoFA and SdMP algorithms for CE have around 11.1%, 18.3%, 28.9% and 42.8% and around 5.6%, 13.9%, 22.8% and 33.3% performance improvement at MSE value of 2 × 10−3 when compared to FACS, gOMP, OMP and ROMP algorithms, respectively. …”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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5
Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance
Published 2019“…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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Reactive power planning for maximum load margin improvement using Fast Artificial Immune Support Vector Machine (FAISVM)
Published 2023“…It also proposes a fast optimization algorithm for reactive power planning problem (RPP) through Fast Artificial Immune Support Vector Machine (FAISVM). …”
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SINR improvement using Firefly Algorithm (FA) for Linear Constrained Minimum Variance (LCMV) beamforming technique
Published 2023Conference Paper -
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Optimization variation for multiple heuristic approaches in solar tracking
Published 2023“…In this research, genetic algorithm is one of the optimization techniques used to maximize the performance of solar tracking system. …”
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Fault classification in smart distribution network using support vector machine
Published 2023“…Gaussian radial basis function (RBF) kernel function has been used for training of SVM to accomplish the most optimized classifier. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…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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Robust correlation feature selection based support vector machine approach for high dimensional datasets
Published 2025“…The third step employs the support vector machine algorithm to calculate prediction values. …”
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Sequence and sequence-less t-way test suite generation strategy based on the elitist flower pollination algorithm
Published 2018“…In line with the emerging field called Search based Software Engineering, many recently developed t-way strategies have adopted meta-heuristic algorithms as the basis of their implementations such as Simulated Annealing, Genetic Algorithm, Ant Colony Optimization Algorithm, Particle Swarm Optimization, Harmony Search and Cuckoo Search, owing their superior performance in term of test size reduction as compared to general computational based strategies, such as General t-way, Test Vector Generator, In Parameter Order General, Jenny, and Automatic Efficient Test Generator. …”
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A novel multi-state particle swarm optimization for discrete combinatorial optimization problems
Published 2012“…Particle swarm optimization (PSO) has been widely used to solve real-valued optimization problems. …”
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Numerical simulation of fluid flow in three dimensional domain based on two stage pressure-velocity correction method
Published 2015“…A second correction step is added to the pressure value and the velocity vectors using Simplified Marker and Cell method (SMAC). …”
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Numerical simulation of fluid flow in three dimensional domain based on two stage pressure-velocity correction method
Published 2015“…A second correction step is added to the pressure value and the velocity vectors using Simplified Marker and Cell method (SMAC). …”
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Novel reservoir system simulation procedure for gap minimization between water supply and demand
Published 2019“…In addition, SMLA has demonstrated a significant change in the performance indicator values when using forecasted reservoir inflow data rather than deterministic reservoir inflow data.…”
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Reproducing kernel Hilbert space method for cox proportional hazard model
Published 2016“…This algorithm is used to determine the vector i a that enables us to find the optimal parameters of ƒ(x)which is simplified as F(x)= ∑aᵢK(x,xᵢ) . …”
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Estimating the optimal threshold value for foreign investment in Malaysia / Noorzahirah Mohd Sidek and Mahadzir Ismail
Published 2012“…Finally, the robustness of the model (s) and the estimation of the threshold value (s) are conducted using heuristic approaches of genetic algorithm, neuro network and support vector machine. …”
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LSSVM parameters tuning with enhanced artificial bee colony
Published 2014“…To guarantee its convincing performance, it is crucial to select an appropriate technique in order to obtain the optimized hyper-parameters of LSSVM algorithm.In this paper, an Enhanced Artificial Bee Colony (eABC) is used to obtain the ideal value of LSSVM’s hyper parameters, which are regularization parameter, γ and kernel parameter, σ2.Later, LSSVM is used as the prediction model. …”
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