Search Results - (( time evaluation method algorithm ) OR ( data optimization method algorithm ))
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Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm
Published 2024“…This research introduces the improved Archimedes optimization algorithm (IAOA) for data-driven modeling of continuous-time Hammerstein models with missing data. …”
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VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern
Published 2012“…These two models are based on topological placement method. DM is optimized using genetic algorithm (GA). …”
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Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…So far, the tuning methods used for data-driven PID for the underactuated systems are mostly based on the multi-agent-based optimization, which means that the design requires substantial computation time and make it not practical for on-line tuning applications. …”
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Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…So far, the tuning methods used for data-driven PID for the underactuated systems are mostly based on the multi-agent-based optimization, which means that the design requires substantial computation time and make it not practical for on-line tuning applications. …”
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Fuzzy clustering method and evaluation based on multi criteria decision making technique
Published 2018“…The proposed algorithm is used as a pre-processing method for data followed by Gustafson-Kessel (GK) algorithm to classify credit scoring data. …”
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Optimal QoS aware multiple paths web service composition using heuristic algorithms and data mining techniques
Published 2014“…Experiments via WEKA tool and simulation prototype were conducted to evaluate the methods used. For the runtime path prediction method, the results showed that the path prediction method achieved promising prediction accuracy, and the number of paths involved in the prediction did not affect the accuracy. …”
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Delay and energy-aware routing for efficient data collection in wireless sensor networks / Ihsan Ali
Published 2024“…The hybrid method, IDBA-AFW, aims to enhance the original IDBA by incorporating the Adaptive Floyd-Warshall algorithm for graph transformation and optimization. …”
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An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system
Published 2023“…Comparative findings alongside other existing metaheuristic-based algorithms confirmed excellence of the proposed method through its superior performance against the conventional MPA, particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), multi-verse optimizer (MVO), sine-cosine algorithm (SCA), salp-swarm algorithm (SSA), slime mould algorithm (SMA), flow direction algorithm (FDA), and the formally published adaptive safe experimentation dynamics (ASED)-based methods.…”
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Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…The third set affirmed that the enhancement on the proposed algorithm, which made use of indexing method that suits the medoids, could boost the performance to about 9 to 27 times in terms of execution time depending on the complexity of the dataset. …”
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Intelligent web proxy cache replacement algorithm based on adaptive weight ranking policy via dynamic aging
Published 2016“…However, their performances are not well optimized. This work proposes a hybrid method that optimize cache replacement algorithm using Naïve Bayes (NB) based approach. …”
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An improved K-nearest neighbour with grasshopper optimization algorithm for imputation of missing data
Published 2021“…Thus, this paper proposes a novel method for imputation of missing data, named KNNGOA, which optimized the KNN imputation technique based on the grasshopper optimization algorithm. …”
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Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman
Published 2025“…Data for the study was collected from university records, and algorithm performance was tested against predefined scheduling criteria. …”
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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Resource scheduling algorithm with load balancing for cloud service provisioning
Published 2019“…Simulations were conducted to evaluate the effectiveness using Cloudsim simulator in cloud data centers and results shows that the proposed method achieves better performance in terms of average success rate, resource scheduling efficiency and response time. …”
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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. …”
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Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…Secondly, the modeling method of the proposed PV module is validated by experimental data. …”
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Data-driven PID controller of wind turbine systems using safe experimentation dynamics algorithm
Published 2024“…Simulation analyses, encompassing convergence curves of the fitness function, time response specification analysis of step response, stability analysis using Bode plots, and computational effort analysis based on NFE, evaluate the effectiveness of the proposed SEDA-based PID controller for wind turbine systems Furthermore, the study reveals that the settling time (Ts) and percentage of overshoot (Mp) are notably low, measuring 1.27E-4s and 0%, respectively, compared to other algorithms. …”
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Incremental interval type-2 fuzzy clustering of data streams using single pass method
Published 2020“…The performance of the proposed method is evaluated on large real-life datasets. The results obtained from several fuzzy cluster validity index measures show the enhanced performance of the proposed method over other clustering algorithms. …”
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