Search Results - (( based optimization method algorithm ) OR ( test selection method algorithm ))
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1
Opposition-based Whale Optimization Algorithm
Published 2018“…The OWOA use the Opposition-based method to enhance Whale Optimization Algorithm (WOA) performance. …”
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2
Optimization of k-Nearest Neighbour to categorize Indonesian’s news articles
Published 2021“…The way to solve this problem is to conduct the feature selection process. There are several filter-based feature selection methods; some are Chi-Square, Information Gain, Genetic Algorithm, and Particle Swarm Optimization (PSO). …”
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3
Network reconfiguration and control for loss reduction using genetic algorithm
Published 2010“…Whereas the results obtained from both selection methods are similar to each other. Tests results show that Genetic Algorithm is a suitable algorithm as it is an optimization technique with, high accuracy, and it avoids local minimum by searching in several regions to arrive to the global optimum solution. …”
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4
VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern
Published 2012“…GA is a widely used optimization algorithm based on the concept of survival of the fittest. …”
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5
Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection
Published 2020“…Four state-of-the-art algorithms include particle swarm optimization, flower pollination algorithm, butterfly optimization algorithm, and CBGWO are used to examine the effectiveness of proposed methods in feature selection. …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…In fact a data clustering method is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on our proposed algorithm; which is Enhanced Binary Particle swarm Optimization (EBPSO), (ii) To mine data using various data chunks (windows) and overcome a failure of single clustering. …”
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7
Enhanced gravitational search algorithm for nano-process parameter optimization problem / Norlina Mohd Sabri
Published 2020“…In this research, the empirical experiments have been conducted for the five selected algorithms in the engineering optimization discipline, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Ant Colony Optimization (ACO) and Artificial Immune System (AIS). …”
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8
Solution of optimal power flow using non-dominated sorting multi-objective based hybrid firefly and particle swarm optimization algorithm
Published 2020“…Finally, an optimal compromised solution was selected from the Pareto optimal set by applying an ideal distance minimization method. …”
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Modified archive update mechanism of multi-objective particle swarm optimization in fuzzy classification and clustering
Published 2022“…Among multi-objective evolutionary algorithms proposed in the literature, particle swarm optimization (PSO)-based multi-objective (MOPSO) algorithm has been cited to be the most representative. …”
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10
Optimization of stiffened panel fatigue life by using finite element analysis
Published 2020“…The multi-objective genetic algorithm which selects the design points based on Pareto optimal design combined with the adaptive multi-objective algorithm method which uses an optimal space-filling was shown to be efficient for time limitation and budget. …”
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11
A new multiperspective framework for standardization and benchmarking of image dehazing algorithms
Published 2021“…Furthermore, seven criteria had been satisfied with the PLCC and SRCC tests. Hybridization of BWM and VIKOR methods can effectively solve the challenges in the selection of the optimal algorithm. …”
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Software defect prediction framework based on hybrid metaheuristic optimization methods
Published 2015“…The proposed framework and models that are are considered to be the specific research contributions of this thesis are: 1) a comparison framework of classification models for software defect prediction known as CF-SDP, 2) a hybrid genetic algorithm based feature selection and bagging technique for software defect prediction known as GAFS+B, 3) a hybrid particle swarm optimization based feature selection and bagging technique for software defect prediction known as PSOFS+B, and 4) a hybrid genetic algorithm based neural network parameter optimization and bagging technique for software defect prediction, known as NN-GAPO+B. …”
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13
An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In order to address the challenges that mentioned above in this study, in the first phase, a novel architecture based on ensemble feature selection techniques include Modified Binary Bat Algorithm (NBBA), Binary Quantum Particle Swarm Optimization (QBPSO) Algorithm and Binary Quantum Gravita tional Search Algorithm (QBGSA) is hybridized with the Multi-layer Perceptron (MLP) classifier in order to select relevant feature subsets and improve classification accuracy. …”
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14
Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)
Published 2020“…In an attempt to address this problem, the clustering-based method that utilizes crowding distance (CD) technique to balance the optimality of the objectives in Pareto optimal solution search is proposed. …”
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Effect of negative campaign strategy of election algorithm in solving optimization problem
Published 2020“…Election algorithm (EA) is an optimization technique based on minimization and coalition operations to solve competition among neurons. …”
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Self organizing multi-objective optimization problem
Published 2011“…This algorithm has been tested for optimization of components placement on printed circuit boards. …”
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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). …”
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Model structure selection for a discrete-time non-linear system using genetic algorithm
Published 2004“…They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. …”
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Hybrid performance measures and mixed evaluation method for data classification problems
Published 2012“…Moreover, the GA model that was optimized by OAERP2 measure (GAoe2) performed significantly and statistically differently as compared to other OAERP2-based models through win-draw-loss evaluation method and two nonparametric tests. …”
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20
Model structure selection for a discrete-time non-linear system using a genetic algorithm
Published 2004“…They offer many advantages such as global search characteristics, and this has led to the idea of using this programming method in modelling dynamic non-linear systems. In this paper, a methodology for model structure selection based on a genetic algorithm was developed and applied to non-linear discrete-time dynamic systems. …”
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