Search Results - (( simulation optimization method algorithm ) OR ( variable selection problem algorithm ))
Search alternatives:
- selection problem »
- problem algorithm »
- method algorithm »
- variable »
-
1
Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification
Published 2011“…Evolutionary computation (EC) is known to be an effective search and optimization method and in this paper EC is proposed as a model structure selection algorithm. …”
Get full text
Get full text
Get full text
Article -
2
A high-performance democratic political algorithm for solving multi-objective optimal power flow problem
Published 2025“…The proposed method is a version of the democratic political optimization algorithm in which the search capability of this method to cover the borders of the Pareto frontier is enhanced. …”
Article -
3
A high-performance democratic political algorithm for solving multi-objective optimal power flow problem
Published 2024“…The proposed method is a version of the democratic political optimization algorithm in which the search capability of this method to cover the borders of the Pareto frontier is enhanced. …”
Get full text
Get full text
Article -
4
Optimization and control of hydro generation scheduling using hybrid firefly algorithm and particle swarm optimization techniques
Published 2018“…Firstly, a hybrid algorithm namely firefly particle swarm optimization (FPSO) and series division method (SDM) based on the practical swarm optimization and the firefly algorithm is proposed. …”
Get full text
Get full text
Thesis -
5
Process Planning Optimization In Reconfigurable Manufacturing Systems
Published 2008“…The five (5) AADTs include; a variant of the simulated annealing algorithm that implements heuristic knowledge at critical decision points, two (2) cooperative search schemes based on a “loose hybridization” of the Boltzmann Machine algorithm with (i) simulated annealing, and (ii) genetic algorithm search techniques, and two (2) modified genetic algorithms. …”
Get full text
Get full text
Thesis -
6
Information Theoretic-based Feature Selection for Machine Learning
Published 2018“…In these extended IFS method, feature selection method was defined and presented as a 0-1 Knapsack Problem (MKP). …”
Get full text
Get full text
Get full text
Get full text
Thesis -
7
Bayesian random forests for high-dimensional classification and regression with complete and incomplete microarray data
Published 2018“…Random Forests (RF) are ensemble of trees methods widely used for data prediction, interpretation and variable selection purposes. …”
Get full text
Get full text
Get full text
Get full text
Thesis -
8
A multi-objective portfolio selection model with fuzzy Value-at-Risk ratio
Published 2018“…Based on the two ratios, a multi-objective model is built to evaluate their joint impact on portfolio selection. Then the proposed model is solved by a fuzzy simulation-based multi-objective particle swarm optimization algorithm, where the global best of each iteration is determined by an improved dominance times-based method. …”
Get full text
Get full text
Article -
9
A multi-objective portfolio selection model with fuzzy Value-at-Risk ratio
Published 2018“…Based on the two ratios, a multi-objective model is built to evaluate their joint impact on portfolio selection. Then the proposed model is solved by a fuzzy simulation-based multi-objective particle swarm optimization algorithm, where the global best of each iteration is determined by an improved dominance times-based method. …”
Get full text
Get full text
Article -
10
Development of optimized maintenance scheduling model for coal-fired power plant boiler
Published 2023“…Literature revealed that mathematical methods and metaheuristic algorithms are common approaches in solving combinatorial optimization problems with a large search space in a reasonable computational run time. …”
text::Thesis -
11
Development of robust procedures for partial least square regression with application to near infrared spectral data
Published 2021“…To fill-in the gap in the literature, a new robust procedure in wavelength selection based on input scaling method is developed using Filter-Wrapper method. …”
Get full text
Get full text
Thesis -
12
Rank-based optimal neural network architecture for dissolved oxygen prediction in a 200L bioreactor
Published 2017“…In order to select the appropriate structure, trial and error method or repeated runs are usually used to find the number of hidden neurons that gives smallest value of error and highest value of correlation coefficient. …”
Get full text
Get full text
Conference or Workshop Item -
13
-
14
Determining malaria risk factors in Abuja, Nigeria using various statistical approaches
Published 2018“…Data collected were used for the multilevel analysis, Markov Chain Monte Carlo (MCMC) simulation via WinBUGS algorithm and influence diagrams for BBNs. …”
Get full text
Get full text
Thesis -
15
-
16
Optimization of turning parameters using genetic algorithm method
Published 2008“…The simulation based on Genetic Algorithm are successful develop and the optimum parameters values are obtained from the simulation.…”
Get full text
Get full text
Undergraduates Project Papers -
17
Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation
Published 2022“…This thesis proposes and simulates the three novel optimization algorithms to handle DG allocation, different single-objective, and multi-objective OPF problems. …”
Get full text
Get full text
Thesis -
18
A discrete simulated kalman filter optimizer for combinatorial optimization problems
Published 2022“…An example of a numerical algorithm is the simulated Kalman filter (SKF). Various method has been introduced as an extension of a numerical algorithm to adapt it to a discrete search space. …”
Get full text
Get full text
Thesis -
19
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.…”
Get full text
Get full text
Get full text
Article -
20
Formulating new enhanced pattern classification algorithms based on ACO-SVM
Published 2013“…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.…”
Get full text
Get full text
Get full text
Article