Search Results - (( simulation optimization testing algorithm ) OR ( parameter optimization _ algorithm ))

On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing by Kamal Z., Zamli, Alsariera, Yazan A., Nasser, Abdullah B., Alsewari, Abdulrahman A.

“…In doing so, this paper reviews two existing parameter free optimization algorithms involving Teaching Learning Based Optimization (TLBO) and Fruitfly Optimization Algorithm (FOA) in an effort to promote their adoption for CIT.…”
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Finite impulse response optimizers for solving optimization problems by Tasiransurini, Ab Rahman

“…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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A multiobjective simulated Kalman filter optimization algorithm by A. Azwan, A. Razak, Mohd Falfazli, Mat Jusof, Ahmad Nor Kasruddin, Nasir, Mohd Ashraf, Ahmad

“…This paper presents a new multiobjective type optimization algorithm known as a Multiobjective Optimization Simulated Kalman Filter (MOSKF). …”
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Finite impulse response optimizers for solving optimization problems by Ab Rahman, Tasiransurini

“…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

“…For its fast convergence and for its efficient search procedure, the self-adaptation is proposed in the parameters of the proposed hybrid algorithm. The effectiveness of this algorithm is verified by applying it on the unconstrained and constrained test functions through a simulation study. …”
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Optimization-based simulation algorithm for predictive-reactive job-shop scheduling of reconfigurable manufacturing systems by Tan, Joe Yee

“…In this case, the effectiveness and reliability of RMS is increase by combining the simulation with the optimization algorithm.…”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

“…These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction by Adnan R.M., Kisi O., Mostafa R.R., Ahmed A.N., El-Shafie A.

Characterization of PV panel and global optimization of its model parameters using genetic algorithm by Ismail M.S., Moghavvemi M., Mahlia T.M.I.

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Multi-Agent cubature Kalman optimizer: A novel metaheuristic algorithm for solving numerical optimization problems by Zulkifli, Musa, Zuwairie, Ibrahim, Mohd Ibrahim, Shapiai

“…Simulation results demonstrate that MACKO is superior, outperforming the benchmark algorithms, as indicated by Friedman's test with a 5 % significance level.…”
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Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Nor Farizan, Zakaria, Mohd Mawardi, Saari

“…This paper presents the application of a recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) for optimizing the Deep Learning (DL) parameters to estimate the state of charge (SOC) of a battery for an electric vehicle in the real environment. …”
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Optimal design of power system stabilizer for multimachine power system using farmland fertility algorithm by Sabo, Aliyu, Abdul Wahab, Noor Izzri, Othman, Mohammad Lutfi, Mohd Jaffar, Mai Zurwatul Ahlam, Beiranvand, Hamzeh

“…Moreover, at the end of the analysis, the FFA based PSSs design was found to converge faster with low computational cost and produces enhanced optimal PSSs parameters as compared to the other existing algorithms. …”
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Decentralized Intelligent PID based controller tuned by Evolutionary Algorithm for Double Link Flexible Robotic Manipulator with Experimental Validation by Annisa, Jamali, Mat Darus, I.Z., M. H., Hassan, Tokhi, M.O.

“…The controllers are incorporated with optimization algorithm that is PSO to find out the parameters of the PID controllers. …”
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Load dispatch optimization of open cycle industrial gas turbine plant incorporating operational, maintenance and environmental parameters by Fong, Yeow Huang

“…Four simulation tests were conducted to study and test the develop optimization software. …”
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BBO algorithm-based tuning of PID controller for speed control of synchronous machine by Kasilingam G., Pasupuleti J.

Fuzzy adaptive emperor penguin optimizer for global optimization problems by Md Abdul, Kader

“…The Emperor Penguin Optimizer (EPO) is a recently developed population-based metaheuristic algorithm that simulates the huddling behaviour of emperor penguins. …”
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Pendulum-like algorithm as a local search technique by Abed I.A., Koh S.P., Sahari K.S.M., Tiong S.K., Younis H.A.-K., Abed A.A.

“…Aluminum; Approximation algorithms; Local search (optimization); Optimization; Pendulums; Problem solving; attraction; Global solutions; Local search; Local search techniques; Optimization problems; repulsion; Repulsion mechanisms; simple harmonic; Parameter estimation…”

Evaluation of robot path planning algorithms in global static environments: genetic algorithm vs ant colony optimization algorithm / Nohaidda Sariff and Norlida Buniyamin by Sariff, Nohaidda, Buniyamin, Norlida

“…The effectiveness and efficiency of both algorithms were tested using a simulation approach. …”
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An Arithmetic-Trigonometric Optimization Algorithm with Application for Control of Real-Time Pressure Process Plant by Devan, P.A.M., Hussin, F.A., Ibrahim, R.B., Bingi, K., Nagarajapandian, M., Assaad, M.

“…The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase the effectiveness of ATOA. …”
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Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA by Zahari, Taha, Farzad, Tahriri, Siti Zawiah, Md Dawal

“…The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model.…”
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