Search Results - (( model evaluation method algorithm ) OR ( time 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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Comparative analysis of spiral dynamic algorithm and artificial bee colony optimization for position control of flexible link manipulators
Published 2024“…This study aims to evaluate the effectiveness of two optimization algorithms, artificial bee colony (ABC) and spiral dynamic algorithm (SDA), in controlling the position of a flexible-link manipulator. …”
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Identification of the thermoelectric cooler using hybrid multi-verse optimizer and sine cosine algorithm based continuous-time Hammerstein model
Published 2021“…This paper presents the identification of the ThermoElectric Cooler (TEC) plant using a hybrid method of Multi-Verse Optimizer with Sine Cosine Algorithm (hMVOSCA) based on continuous-time Hammerstein model. …”
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Analysis of toothbrush rig parameter estimation using different model orders in Real-Coded Genetic Algorithm (RCGA)
Published 2018“…Realcoded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. …”
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VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern
Published 2012“…DM is optimized using genetic algorithm (GA). GA is a widely used optimization algorithm based on the concept of survival of the fittest. …”
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DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS
Published 2012“…The four prime aspects of this work are: firstly, a model of the grid scheduling problem for dynamic grid computing environment; secondly, development of a new web based simulator (SyedWSim), enabling the grid users to conduct a statistical analysis of grid workload traces and provides a realistic basis for experimentation in resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new grid resource allocation method of optimal computational cost using synthetic and real workload traces with respect to other allocation methods; and finally, proposal of some new job scheduling algorithms of optimal performance considering parameters like waiting time, turnaround time, response time, bounded slowdown, completion time and stretch time. …”
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Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…Due to the effective attraction-repulsion mechanism of electromagnetic-like (EM) algorithm and reliable exploration and exploitation phases of differential evolution (DE), these two methods were used to determine parameters of the single diode PV model and finding optimal sizing of the SAPV system. …”
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Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…Then, the rise time, settling time, and percentage of overshoot of the one best trial out of the 30 trials were observed for each method. …”
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Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems
Published 2019“…Then, the rise time, settling time, and percentage of overshoot of the one best trial out of the 30 trials were observed for each method. …”
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10
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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Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics
Published 2018“…Chemical Vapor Deposition (CVD) is the most efficient method for CNTs production.However,using CVD method encounters crucial issues such as customization,time and cost.Therefore,Response Surface Methodology (RSM) is proposed for modeling and the ABC-βHC is proposed for optimization purpose to address such issues.The selected CNTs characteristics are CNTs yield and quality represented by the ratio of the relative intensity of the D and G-bands (ID/IG).Six case studies are generated from collected dataset including four cases of CNTs yield and one case of ID/IG as single objective optimization problems,while the sixth case represents multi-objective problem.The input parameters of each case are a subset from the set of input parameters including reaction temperature,duration,carbon dioxide flow rate,methane partial pressure,catalyst loading,polymer weight and catalyst weight.The models for the first three case studies were mentioned in the original work.RSM is proposed to develop polynomial models for the output responses in the other three cases and to identi significant process parameters and interactions that could affect the CNTs output responses.The developed models are validated using t-test,correlation and pattern matching.The predictive results have a good agreement with the actual experimental data.The models are used as objective functions in optimization techniques.For multi-objective optimization,this study proposes Desirability Function Approach (DFA) to be integrated with other proposed algorithms to form hybrid techniques namely RSM-DFA,ABC-DFA and ABC-βHC-DFA.The proposed algorithms and other selected well-known algorithms are evaluated and compared on their CNTs yield and quality.The optimization results reveal that ABC-βHC and ABC-βHC-DFA obtained significant results in terms of success rate,required time,iterations,and function evaluations number compared to other well-known algorithms.Significantly,the optimization results from this study are better than the results from the original work of the collected dataset.…”
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…This research utilizes a genetic algorithm (GA) to optimize the multi-layer FFNN performance and structure in modelling three datasets: network traffic, rainfall, and tourist. …”
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Optimization of multipurpose reservoir operation using evolutionary algorithms / Mohammed Heydari
Published 2017“…One of the main problems of this method is premature convergence and to improve this problem, the compound of the particle swarm algorithm and genetic algorithm were evaluated. …”
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Modelling and Optimization of Asymmetric Vehicle Routing Problem Using Particle Swarm Optimization Algorithm
Published 2021“…This paper used a well-known metaheuristic optimization method, Particle Swarm Optimization (PSO) for solving AVRP models. …”
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Low Complexity Error Correction in Low Density Parity Check (LDPC) Code Decoder and Encoder for Decode and Forward Cooperative Wireless Communication
Published 2021“…In this model, eight different LU and QR encoding method variants are evaluated in terms of execution time, the number of nonzero, and the pattern of nonzero. …”
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Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real Coded Genetic Algorithm (RCGA)
Published 2024“…Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. …”
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An integrated reservoir modelling and evolutionary algorithm for optimizing field development in a mature fractured reservoir
Published 2016“…The second method is automatic optimization using Genetic Algorithm. …”
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Hybrid moth flame optimization mppt algorithm for accurate real-time tracking under partially shaded photovoltaic system
Published 2021“…All four MPPT methods were simulated and real-time evaluated under 10 constant irradiance test cases, 30 dynamic irradiance test cases and 100 partial shaded irradiance test cases. …”
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Multilevel optimization for dense motion estimation
Published 2011“…We evaluated the performance of different optimization techniques developed in the context of optical flow computation with different variational models.In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we developed the use of efficient multilevel schemes for computing the optical flow.More precisely, we evaluated the performance of a standard unidirectional multilevel algorithm - called multiresolution optimization (MR/Opt), to a bidrectional multilevel algorithm - called full multigrid optimization (FMG/Opt).The FMG/Opt algorithm treats the coarse grid correction as an optimization search direction and eventually scales it using a line search. …”
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