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1
Metaheuristic optimization techniques for localization in outdoor wireless sensor networks: a comprehensive review
Published 2025“…The primary objective of this paper isto review localization algorithms based on metaheuristic optimization techniques to improve localization accuracy. …”
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2
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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3
Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm
Published 2018“…The K-Means algorithm is the commonest and fast technique in partitional cluster algorithms, although with unnormalized datasets it can achieve local optimal. …”
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4
Design Optimization of a Gas Turbine Engine for Marine Applications: Off-Design Performance and Control System Considerations
Published 2022“…A gain-scheduling method incorporating a meta-heuristic optimization is proposed to optimize a thermodynamics-based model of a small GT engine. …”
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Analysis of optimizers on AlexNet architecture for face biometric authentication system
Published 2022“…However, an optimization technique is crucial in the Deep Learning models. …”
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Proceeding Paper -
6
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. Two dynamic transmit antenna shuffling schemes, namely ‘Optimal’ and ‘Max STBC’, are proposed to enhance the V-BLAST/STBC transceiver scheme with LC-QR proposed in the first part. …”
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7
Improving neural networks training using experiment design approach
Published 2005“…This data is selected by two methods: random and Experiment Design Approach (D-optimal and Maxmin Distance). …”
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8
Robust PID anti-swing control of automatic gantry crane based on Kharitonov's stability
Published 2009“…The proposed method employs Genetic Algorithm (GA) in min-max optimization to find the stable robust PID. …”
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9
OTS: an optimal tasks scheduling algorithm based on QoS in cloud computing network
Published 2019“…This study presents an optimal tasks scheduling algorithm by enhancing Max-Min algorithm. …”
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10
A comparative performance analysis of computational intelligence techniques to solve the asymmetric travelling salesman problem
Published 2021“…This paper presents a comparative performance analysis of some metaheuristics such as the African Buffalo Optimization algorithm (ABO), Improved Extremal Optimization (IEO), Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO), Max-Min Ant System (MMAS), Cooperative Genetic Ant System (CGAS), and the heuristic, Randomized Insertion Algorithm (RAI) to solve the asymmetric Travelling Salesman Problem (ATSP). …”
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An improvement of stochastic gradient descent approach for mean-variance portfolio optimization problem
Published 2021“…Furthermore, the applicability of SGD, Adam, AdaMax, Nadam, AMSGrad, and AdamSE algorithms in solving the mean-variance portfolio optimization problem is validated.…”
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Performance impact of relay selection in WiMAX IEEE 802.16j multi-hop relay networks
Published 2019“…We also did extensive performance evaluation of the proposed linear model. Results show that optimal relays selection scheme do provide a substantial increase of up to 66% in overall network capacity in the fixed WiMAX network. …”
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13
An optimal tasks scheduling algorithm based on QoS in cloud computing network
Published 2017“…This study presents an optimal task scheduling algorithm by enhancing Max-Min and TS algorithm. …”
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14
Max degree around (MDA) algorithm: a smart and efficient approximate algorithm for Vertex cover and independent set problems
Published 2016“…Hence, these approaches regularly fail to provide optimal results on specific graph instances. These problems motivated to propose Max Degres Around (MDA) approximation algorithm for the MVC problem. …”
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15
Maximum 2-satisfiability in radial basis function neural network
Published 2020“…In this study, the effectiveness of RBFNN-MAX2SAT can be estimated by evaluating the proposed models with testing data sets. …”
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An exploration technique for the interacted multiple ant colonies optimization framework
Published 2024Conference Paper -
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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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18
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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Energy-efficient base station transmission design for green 5G massive MIMO and hybrid networks
Published 2021“…Then, a new corresponding iterative EE optimization algorithm is proposed based on the BSTPA model to further improve the system’s EE. …”
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20
Data normalization techniques in swarm-based forecasting models for energy commodity spot price
Published 2014“…Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study investigates the effect of different data normalization techniques.which are Min-max, Z-score and decimal scaling, on Swarm-based forecasting models.Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC).Forecasting models are later developed to predict the daily spot price of crude oil and gasoline.Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max.Nevertheless, the GWO is more superior than ABC as its model generates the highest accuracy for both crude oil and gasoline price.Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.…”
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