Search Results - (( parameter optimization method algorithm ) OR ( parameter evaluation between algorithm ))

Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems by Nor Sakinah, Abdul Shukor

“…Notably, the SPSA and GSPSA based methods only produced the optimal design parameter at the final iteration while it may keep a better design parameter during the tuning process if it has a memory feature. …”
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Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems by Nor Sakinah, Abdul Shukor

“…Notably, the SPSA and GSPSA based methods only produced the optimal design parameter at the final iteration while it may keep a better design parameter during the tuning process if it has a memory feature. …”
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Modeling and Prediction of the mechanical properties of feedstock by cooling-slope casting process using MOJaya algorithm by Mohd Said, Rahaini, Mohamad Kamal, Mohamad Ridzuan, Hussin, Nor Hafizah, Mohamad Suhaili, Sinarwati, Hairol Anuar, Siti Haryanti, Samsudin, Adam, Mohd Yussoff, Nurul Hajar, Winanto, Eko Arip

“…Hence, computational methods namely the MOJaya algorithm are utilized to model and optimize the parameters of CS to address the CS problem and forecast the performance of the feedstock. …”
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Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems by Yasear, Shaymah Akram

“…The algorithm includes (i) a population update strategy which improves the movement of hawks in the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. …”
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Modeling and Prediction of The Mechanical Properties of Feedstock by Cooling-Slope Casting Process using MOJaya Algorithm by Rahaini, Mohd Said, Mohamad Ridzuan, Mohamad Kamal, Nor Hafizah, Hussin, Sinarwati, Mohamad Suhaili, Siti Haryanti, Hairol Anuar, Adam, Samsuddin, Nurul Hajar, Mohd Yussoff, Eko Arip, Winanto

“…Hence, computational methods namely the MOJaya algorithm are utilized to model and optimize the parameters of CS to address the CS problem and forecast the performance of the feedstock. …”
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Optimal power flow using a hybridization algorithm of arithmetic optimization and aquila optimizer by Ahmadipour, Masoud, Murtadha Othman, Muhammad, Bo, Rui, Sadegh Javadi, Mohammad, Mohammed Ridha, Hussein, Alrifaey, Moath

“…In this paper, a hybridization method based on Arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. …”
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Parameter estimation of essential amino acids in Arabidopsis thaliana using hybrid of bees algorithm and harmony search by Aw, Mei Yee, Mohd Saberi, Mohamad, Chong, Chuii Khim, Safaai, Deris, Muhammad Akmal, Remli, Mohd Arfian, Ismail, Corchado, Juan Manuel, Omatu, Sigeru

“…Global optimization methods can be applied by minimizing the distance between experimental data and predicted models. …”
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Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane by Julakha, Jahan Jui, Mohd Ashraf, Ahmad, Muhammad Ikram, Mohd Rashid

“…The efficiency of the proposed HMVOSCA algorithm is evaluated using the convergence curve, parameter estimation error, bode plot, function plot, and Wilcoxon's test method. …”
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VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern by Angeline Teoh, Szu Fern

“…These two models are based on topological placement method. DM is optimized using genetic algorithm (GA). …”
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Nomadic people optimizer (NPO) for large-scale optimization problems by Mohamd Salih, Sinan Qahtan

“…Researchers have in the past few decades resorted to several methods that are inspired from complex optimization problems. …”
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Data-driven PID controller of wind turbine systems using safe experimentation dynamics algorithm by Islam, Muhammad Shafiqul, Mohd Ashraf, Ahmad, Mohd Riduwan, Ghazali, Mohd Helmi, Suid, Mohd Zaidi, Mohd Tumari

“…These results underscore the efficacy of the SEDA method in providing optimal PID control parameters while reducing computational burdens by 52% compared to other multi-agent optimization-based methods.…”
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Delay and energy-aware routing for efficient data collection in wireless sensor networks / Ihsan Ali by Ihsan , Ali

“…Both Bat Algorithm parameters and AFW parameters are adaptively tuned to balance exploration and exploitation throughout the optimization process. …”
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A new hybrid deep neural networks (DNN) algorithm for Lorenz chaotic system parameter estimation in image encryption by Nurnajmin Qasrina Ann, Ayop Azmi

“…In addition, the hyperparameter tuning problem is considered in this research to improve the developed hybrid model by using the AOA algorithm. Lastly, a new hybrid technique suggests tackling the current image encryption application problem by using the estimated parameters of chaotic systems with an optimization algorithm, the SKF algorithm. …”
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Identification of the thermoelectric cooler using hybrid multi-verse optimizer and sine cosine algorithm based continuous-time Hammerstein model by Ahmad, Mohd Ashraf

“…The efficiency of the proposed method is evaluated based on the convergence curve, parameter estimation error, bode plot, function plot, and Wilcoxon’s rank test. …”
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Multi-objective pareto ant colony system based algorithm for generator maintenance scheduling by Shimailawi, Shatha Abdulhadi Muthana

“…The proposed models and algorithm can be used to solve the multi-objective GMS problem while the new parameters’ values can be used to obtain optimal or near optimal maintenance scheduling of generators. …”
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Software defect prediction framework based on hybrid metaheuristic optimization methods by Wahono, Romi Satria

“…There are two main problems that affect classification performance in software defect prediction: noisy attributes and imbalanced class distribution of datasets, and difficulty of selecting optimal parameters of the classifiers. In this study, a software defect prediction framework that combines metaheuristic optimization methods for feature selection and parameter optimization, with meta learning methods for solving imbalanced class problem on datasets, which aims to improve the accuracy of classification models has been proposed. …”
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Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection by Norfadzlan, Yusup, Izzatul Nabila, Sarbini, Dayang Nurfatimah, Awang Iskandar, Azlan, Mohd Zain, Didik Dwi, Prasetya

“…The experiment results show how these algorithms could be used to improve methods for recognizing human activities using wearables technology, such as feature selection, parameter adjustment, and model optimization.…”
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Fuzzy clustering method and evaluation based on multi criteria decision making technique by Sameer, Fadhaa Othman

“…This proposed algorithm is developed based on heuristic method named modified binary particle swarm optimization (MBPSO) with kernel fuzzy clustering method as a fitness function. …”
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Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin by Mohd Yassin, Ahmad Ihsan

“…A MySQL database was created to analyze the optimization results and speed up computations of the optimization algorithm. …”
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Hybrid optimization approach to estimate random demand by Wahab, Musa, Ku-Mahamud, Ku Ruhana, Yasin, Azman

“…The main objective of this study is to develop a demand forecasting model that should reflect the characteristics of random demand patterns.To accomplish this goal, a hybrid algorithm combining a genetic algorithm and a local search algorithm method was developed to overcome premature convergence in local optima problems.The performance of the hybrid algorithm was compared with a single algorithm model in estimating parameter values that minimize objective function which was used to measure the goodness-of-fit between the observed data and simulated results.However, two problems had to be overcome in the forecasting random demand model. …”
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