Search Results - (( ((model evaluation) OR (model selection)) _ algorithm ) OR ( based optimization max algorithm ))
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1
Reactive memory model for ant colony optimization and its application to TSP
Published 2014“…The performances of six (6) ant colony optimization variants were evaluated to select the base for the proposed model.Based on the results, Max-Min Ant System has been chosen as the base for the modification.The modified algorithm called RMMAS, was applied to TSPLIB95 data and results showed that RMMAS outperformed the standard MMAS.…”
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2
An optimal tasks scheduling algorithm based on QoS in cloud computing network
Published 2017“…The simulation tool used for testing the algorithm is WorkflowSim. We tested averages of execution time span of the proposed algorithm for 10 running times with 200-1000 tasks in 50 or 100 VMs. …”
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3
Reactive approach for automating exploration and exploitation in ant colony optimization
Published 2016“…Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. …”
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4
Artificial neural network-salp-swarm algorithm for stock price prediction
Published 2024“…Additionally, the SSA-ANN model is compared with other two hybrid models: the ANN optimized by the Whale Optimization Algorithm (WOA-ANN) and Moth-Flame Optimizer (MOA-ANN), as well as a single model, namely the Autoregressive Integrated Moving Average (ARIMA). …”
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5
Social spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition
Published 2018“…Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). …”
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Enhanced multi-agent approaches for efficient evacuation and rescue operations in managing disasters
Published 2025“…Building on the proposed algorithm and approaches, Agent-Based Simulation (ABS) models were developed to evaluate both evacuation and rescue operations in Al-Aqsa Mosque (AM) scenarios in Palestine. …”
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7
Dynamic transmit antenna shuffling scheme for hybrid multiple-input multiple-output in layered architecture
Published 2010“…Dynamic transmit antenna shuffling scheme improves the performance of MIMO by selecting the appropriate pairs of antennas at transmitter based on the CSI from receiver. …”
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Improving neural networks training using experiment design approach
Published 2005“…There are several methods of selecting training data from input space for neural networks which include D-optimal and Max-min design approaches. …”
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10
A particle swarm optimization and min-max-based workflow scheduling algorithm with QoS satisfaction for service-oriented grids
Published 2017“…The algorithm selects the best scheduling solutions based on the proposed constraint-handling strategy. …”
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11
A smart spectrum access technique for dynamic multi-hop routing in cognitive radio-based disaster response networks
Published 2022“…The multi-hop route selection is executed using the Hybrid Spiral Penguin Optimization (HSPO) algorithm based on the decision made by the DE-D2PG. …”
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12
Optimization of hydropower reservoir system using genetic algorithm for various climatic scenarios
Published 2015“…In order to increase the system efficiency and maximize the power generation, constructed operation models were optimized. To determine the optimum solution in each policy, real coded genetic algorithm is used as an optimization technique. …”
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13
Fair bandwidth assignment in hierarchical scheduling for mobile WiMAX system
Published 2011“…In this paper, we propose a fair bandwidth assignment algorithm that allocates the bandwidth among different services classes based on a hierarchical scheduler. …”
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14
Analytical Study Of Machine Learning Models For Stock Trading In Malaysian Market
Published 2024“…Previous studies usually concentrate on the forecasting stock index or selecting a few stocks with restricted features. Therefore, this study focused to contribute on evaluating different algorithm models such as traditional ML and deep learning models with big stock data of multiple parameters from selected companies in Bursa Malaysia. …”
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Prediction and multi-criteria-based schemes for seamless handover mechanism in mobile WiMAX networks
Published 2013“…It reduces the number of handovers by 29.7% and 26.9%, respectively, compared to the conventional RSSI based handover algorithm and the previous worked, mobility improved handover (MIHO) algorithm. …”
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16
Algorithmic approaches in model selection of the air passengers flows data
Published 2015“…Algorithm is an important element in any problem solving situation.In statistical modelling strategy, the algorithm provides a step by step process in model building, model testing, choosing the ‘best’ model and even forecasting using the chosen model.Tacit knowledge has contributed to the existence of a huge variability in manual modelling process especially between expert and non-expert modellers.Many algorithms (automated model selection) have been developed to bridge the gap either through single or multiple equation modelling.This study aims to evaluate the forecasting performances of several selected algorithms on air passengers flow data based on Root Mean Square Error (RMSE) and Geometric Root Mean Square Error (GRMSE).The findings show that multiple models selection performed well in one and two step-ahead forecast but was outperformed by single model in three step-ahead forecasts.…”
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17
Fair uplink bandwidth allocation and latency guarantee for mobile WiMAX using fuzzy adaptive deficit round robin
Published 2014“…One example of such a network is called WiMAX which is driven by WiMAX Forum based on IEEE 802.16 Wireless MAN standard. …”
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Cross-layer design using multi-channel system in WiMAX mesh networks
Published 2008“…The WiMAX mesh networks based on IEEE 802.16 standard is a key topology for the next generation wireless networking. …”
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19
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.…”
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A comparative evaluation of heuristic and metaheuristic job scheduling algorithms for optimized resource management in cloud environments
Published 2026“…This study provides a comprehensive analysis and comparison of six prevalent scheduling algorithms, namely FCFS, SJF, LJF, EDF, Max-Min, and PSO, under varying cloudlet loads of 200,400,600,800, and 1000. …”
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