Search Results - (( using optimization _ algorithm ) OR ( data optimization max algorithm ))
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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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Reactive max-min ant system: An experimental analysis of the combination with K-OPT local searches
Published 2015“…The exploration versus exploitation dilemma rises in ACO search.Reactive max-min ant system algorithm is a recent proposition to automate the exploration and exploitation.It memorizes the search regions in terms of reactive heuristics to be harnessed after restart, which is to avoid the arbitrary exploration later.This paper examined the assumption that local heuristics are useless when combined with local search especially when it applied for combinatorial optimization problems with rugged fitness landscape.Results showed that coupling reactive heuristics with k-Opt local search algorithms produces higher quality solutions and more robust search than max-min ant system algorithm.Well-known combinatorial optimization problems are used in experiments, i.e. traveling salesman and quadratic assignment problems. …”
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Conference or Workshop Item -
3
Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms
Published 2025“…Three distinct metaheuristic algorithms were investigated: Barnacles Mating Optimizer (BMO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA), each integrated with CatBoost to optimize critical parameters including learning rate, tree depth, regularization, and bagging temperature. …”
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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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Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network
Published 2024journal::journal article -
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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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Maximum 2-satisfiability in radial basis function neural network
Published 2020“…Hence, we restrict the analysis to MAX2SAT clauses. We utilize Dev C++ as the platform of training and testing our proposed algorithm. …”
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Improvement of Centralized Routing and Scheduling Using Cross-Layer Design and Multi-Slot Assignment in Wimax Mesh Networks
Published 2009“…This thesis proposes an optimized strategy namely cross-layer design in routing algorithms used find the best route for all SSs and scheduling algorithms, used to assign a time slot for each possible node transmission. …”
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9
Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm
Published 2018“…We introduced two new approaches to normalization techniques to enhance the K-Means algorithms. This is to remedy the problem of using the existing Min-Max (MM) and Decimal Scaling (DS) techniques, which have overflow weakness. …”
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10
Prediction and multi-criteria-based schemes for seamless handover mechanism in mobile WiMAX networks
Published 2013“…Finally, the FuzSAHO decision algorithim is optimized for real-time application, by using a new handover criteria, namely queue length besides RSSI and MS velocity. …”
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11
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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Analysis of optimizers on AlexNet architecture for face biometric authentication system
Published 2022“…The experiment shows AdaMax performs well when compared to the other optimizers on the proposed dataset.…”
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Proceeding Paper -
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Adaptive and optimized radio resource allocation algorithms for OFDMA based networks
Published 2015“…AORAA contains an adaptive and optimized subcarrier allocation algorithm which uses graph theoretic techniques to do the best probable matching of subcarrier and users’ channel information. …”
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14
Interference avoidance routing and scheduling using multiple transceivers for IEEE 802.16 mesh network
Published 2010“…Here, a routing tree is constructed based on the energy/bit minimization routing (EbMR). This algorithm looks for a short path from the subscriber station (SS) node to BS, while the optimal path is achieved when the whole path has the lowest EbMR. …”
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15
A Comparative Study of Z-Score and Min-Max Normalization for Rainfall Classification in Pekanbaru
Published 2024“…Data preprocessing plays a crucial role in enhancing the performance of machine learning algorithms for classification tasks. …”
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Digital beamforming implementation of switch-beam smart antenna system by using integrated digital signal processor and field-programmable gate array
Published 2008“…Also, for more antenna radiation pattern optimization, the sidelobe cancellation is performed base on the chebyshev algorithm. …”
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Dynamic transmit antenna shuffling scheme for hybrid multiple-input multiple-output in layered architecture
Published 2010“…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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Performance comparison of GA and PSO based ANN training on medical dataset / Muhammad Amirul Danish Jamal
Published 2025“…The assessment incorporates vital performance metrics such as accuracy, precision, sensitivity, Mean Square Error (MSE), Mean Absolute Error (MAE), and training efficiency. Data preprocessing was carried out using min-max normalization, and an ANN architecture featuring 20 hidden neurons was created and optimized with MATLAB. …”
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19
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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