Search Results - (( using deep optimization algorithm ) OR ( simulation optimization based algorithm ))

Investigating the Performance of Deep Reinforcement Learning-Based MPPT Algorithm under Partial Shading Condition by Yew W.H., Fat Chau C., Mahmood Zuhdi A.W., Syakirah Wan Abdullah W., Yew W.K., Amin N.

“…These DRL-based algorithms optimize the local and global maximum power point (MPP) using deep Q-learning and deep deterministic policy gradient (DDPG). …”

GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE by MOHAMMED SHARIFF, NUR ATIQAH

“…This paper will report on an initial study of the usage of Genetic Algorithm (GA) merged with Deep Neural Network based surrogate model to optimize simulation for electromagnetic structure. …”
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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

“…According to the simulation results, the proposed EMA-DL algorithm was found to outperform all the other compared algorithms based on the evaluated metrics. …”
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Development of deep reinforcement learning based resource allocation techniques in cloud radio access network by Amjad, Iqbal

“…The first proposed algorithm aims to optimize the EE by controlling the on/off status of RRH via a deep Q network (DQN) and subsequently solving a power optimization problem. …”
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Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources by Kumar, A., Ridha, S., Narahari, M., Ilyas, S.U.

“…In this research, a novel algorithm (Herschel Bulkley Network) is introduced to simulate the non-Newtonian fluid flow in a pipe using data redundant deep neural network (DNN) for fully developed, laminar, and incompressible flow conditions. …”
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A Hybrid Method Based on Cuckoo Search Algorithm for Global Optimization Problems by Shehab, Mohammad, Khader, Ahamad Tajudin, Laouchedi, Makhlouf

“…However, it undergoes the premature convergence problem for high dimensional problems because the algorithm converges rapidly. Therefore, we proposed a robust approach to solve this issue by hybridizing optimization algorithm, which is a combination of Cuckoo search algorithm and Hill climbing called CSAHC discovers many local optimum traps by using local and global searches, although the local search method is trapped at the local minimum point. …”
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Maximizing DRL-based energy efficiency in IRS-NOMA using a DDPG algorithm for the next generation of wireless communications by Audah, Kamil, Noordin, Nor K., Hussein, Wala'a, Rasid, Mod Fadlee B. A., Sali, Aduwati, Flah, Aymen

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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

“…Then, this study aims to optimize the hyperparameters of the developed DNN model using the Arithmetic Optimization Algorithm (AOA) and, lastly, to evaluate the performance of the newly proposed deep learning model with Simulated Kalman Filter (SKF) algorithm in solving image encryption application. …”
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Harris Hawk Optimization-Based Deep Neural Networks Architecture for Optimal Bidding in the Electricity Market by Kavita Jain, Muhammed Basheer Jasser, Muzaffar Hamzah, Akash Saxena, Ali Wagdy Mohamed

“…In this research, we provide HHO-NN (Harris Hawk Optimization-Neural network), a novel algorithm based on Harris Hawk Optimization (HHO) that is capable of fast convergence when compared to previous evolutionary algorithms for automatically searching for meaningful multilayered perceptron neural networks (MPNNs) topologies for optimal bidding. …”
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Optimisation of fed-batch fermentation process using deep reinforcement learning by Chai, Wan Ying

“…This research aimed to determine the optimal feeding strategy for fed-batch baker’s yeast fermentation process using the deep reinforcement learning algorithm in maximising the final production of yeast, while minimising the undesired ethanol formation. …”
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A Novel Hybrid Deep Learning Model Based on Simulated Annealing and Cuckoo Search Algorithms for Automatic Radiomics-Based COVID-19 Diagnosis by Saleh, Basma Jumaa, Omar, Zaid, As’ari, Muhammad Amir, Bhateja, Vikrant, Izhar, Lila Iznita

“…To further enhance COVID-19 lesion estimation, novel optimization strategies, including a hybrid simulated annealing-cuckoo search (SA-CS) algorithm, are introduced alongside the original SA method. …”
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Performance Comparison of Nature-inspired Optimization Algorithms Applied to MVDR Technique for Canceling Multiple Access Interference Signals by Suhail Najm, Shahab, Ayib Rosdi, Zainun, Nurul Hazlina, Noordin, Izzeldin, I. Mohd

“…Heuristic optimization algorithms are broadly used to solve many engineering problems. …”
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Deep reinforcement learning online offloading for SWIPT multiple access edge computing network by Teckchai, Tiong, Ismail Saad, Tze, Kenneth Kin Teo, Herwansyah Lago

“…This method no longer needs to solve combinatorial problems. Simulation results are presented to demonstrate that the proposed algorithm is able to approach near-optimal performance and superior in decreasing tasks computation time compared with existing optimization methods, enabling real time optimal resource allocation and offloading achievable in a fast-fading wireless environment.…”
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Unsupervised Deep Learning Algorithm to Solve Sub-Surface Dynamics for Petroleum Engineering Applications by Kumar, A., Ridha, S., Ilyas, S.U.

“…In the present study, the solution for the initial condition and boundary value problems in ordinary and partial differential equation by deep learning method have been analyzed. The propsed algorithm could be valuable aid for analyzing the fluid flow and reservoir simulation in an effective manner. …”
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Deep Reinforcement Learning For Control by Bakar, Nurul Asyikin Abu

“…The complete project is carried out in the CARLA simulator to determine how to operate in discrete action space using Deep Reinforcement Learning (DRL) algorithms. …”
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Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks by Ukasyah bin Mahamod

Maximizing deep learning-based energy efficiency in 5G downlink MIMO-NOMA systems by using MLP-CNN. by Audah, Kamil, Hussein, Walaa, Noordin, Nor Kamariah, Sali, Aduwati, A.Rasid, Mohd Fadlee

“…Simulation results demonstrate that the proposed framework improves power allocation, overall data rates, and Energy efficiency by around 15% compared to traditional deep neural network (DNN) algorithms, methods and strategies.…”
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Deep reinforcement learning based resource allocation strategy in cloud-edge computing system by Ahmed Adhoni, Zameer, Habelalmateen, Mohammed, DR Janardhana, DR Janardhana, Abdul Sattar, Khalid Nazim, Audah, Lukman

“…This research focuses on the utility of Multiagent Learning framework with Deep Reinforcement Learning (MAL-DRL) which is used for solution deployment concerning resource allocation in such systems, such that the end user enjoys optimization while operators optimize resource utilization. …”
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Deep reinforcement learning based resource allocation strategy in cloud-edge computing system by Ahmed Adhoni, Zameer, Habelalmateen, Mohammed I, D R, Janardhana, Abdul Sattar, Khalid Nazim, Audah, Lukman

“…This research focuses on the utility of Multiagent Learning framework with Deep Reinforcement Learning (MAL-DRL) which is used for solution deployment concerning resource allocation in such systems, such that the end user enjoys optimization while operators optimize resource utilization. …”
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Enhancing land cover classification in remote sensing imagery using an optimal deep learning model by Motwake, Abdelwahed, Hassan Abdalla Hashim, Aisha, Obayya, Marwa, Eltahir, Majdy M.

“…The current study presents an Improved Sand Cat Swarm Optimization with Deep Learning-based Land Cover Classification (ISCSODL-LCC) approach on the RSIs. …”
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