Search Results - (( evolution optimization system algorithm ) OR ( data simulation optimization algorithm ))

Optimization of handover algorithms in 3GPP long term evolution system by Lin, Cheng-Chung, Sandrasegaran, Kumbesan, Mohd. Ramli, Huda Adibah, Basukala, Riyaj, Patachaianand, Rachod, Chen, Lu, Afrin, Toyoba Sohana

“…Simulation results show that this optimization outperforms non-optimized algorithms by minimizing the average number of handovers per UE per second while maximizing average system throughput.…”
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A novel hybrid metaheuristic algorithm for short term load forecasting by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Yuhanis, Yusof, Syafiq Fauzi, Kamarulzaman

“…Later, the efficiency of GWO-LSSVM is compared against three comparable hybrid algorithms namely LSSVM optimized by Artificial Bee Colony (ABC), Differential Evolution (DE) and Firefly Algorithms (FA). …”
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Hybrid Metaheuristic Algorithm for Short Term Load Forecasting by Zuriani, Mustaffa, M. H., Sulaiman, Yuhanis, Yusof, Syafiq Fauzi, Kamarulzaman

“…Later, the efficiency of GWO-LSSVM is compared against three comparable hybrid algorithms namely LSSVM optimized by Artificial Bee Colony (ABC), Differential Evolution (DE) and Firefly Algorithms (FA). …”
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Efficient radio resource management algorithms for downlink long term evolution networks by Mamman, Maharazu

“…Optimal priority algorithm uses minimum data rate to guarantees resource allocation to users but increases the average delay and deteriorate the network performance. …”
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Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

“…EMA belongs to the evolutionary computation group of nature-inspired metaheuristic algorithms and offers a promising solution. A comparative analysis is conducted with other well-known algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Biogeography-Based Optimization (BBO), Teaching-Learning Based Optimization (TLBO), and Beluga Whale Optimization (BWO). …”
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Estimation of small-scale kinetic parameters of escherichia coli (E. coli) model by enhanced segment particle swarm optimization algorithm ese-pso by Mohammed Adam Kunna, Azrag, Jasni Mohamad, Zain, Tuty Asmawaty, Abdul Kadir, Marina, Yusoff, Jaber, Aqeel Sakhy, Abdlrhman, Hybat Salih Mohamed, Ahmed, Yasmeen Hafiz Zaki, Husain, Mohamed Saad Bala

“…The numerical findings indicate a good agreement between the observed and predicted data. In this regard, the result of the ESe-PSO algorithm achieved superior accuracy compared with the Segment Particle Swarm Optimization (Se-PSO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE) algorithms. …”
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Quality of service and scheduling performance optimization in LTE networks / Abubakar Auwal Idris by Abubakar Auwal , Idris

“…This research proposed an optimal downlink scheduling algorithm called Video Real-Time Scheduling Algorithm (VRTSA) to support real-time traffic of video in LTE network. …”
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HARQ aware scheduling algorithm for the downlink LTE system by Mohd. Ramli, Huda Adibah, Sandrasegaran, Kumbesan, Basukala, Riyaj, Afrin, Toyoba Sohana

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Mobility management schemes based on multiple criteria for optimization of seamless handover in long term evolution networks by Hussein, Yassein Soubhi

“…The results demonstrated that our proposed method results in significant reductions of HOF, HOPP and packet loss ratio (PLR) compared to the conventional HHO and enhanced weighted performance HO parameter optimization (EWPHPO) algorithm. The results also show that the reduction of HOF through FuzAMP over conventional HHO algorithm is approximately 60%, 65%, and 66% at 3, 30, and 120 km/h, respectively, and over EWPHPO algorithm is approximately 30%, 46%, and 50% at 3, 30, and 120 km/h, respectively. …”
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Mobility management for seamless handover in carrier aggregation heterogeneous networks deployment scenario of long term evolution-advanced by Ahmed-Abdulazeez, Mariam Ovayioza

“…But issues related to non-optimal algorithm for selecting the appropriate Handover Control Parameters (HCPs) needs further attention. …”
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Extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm by Muhsen D.H., Ghazali A.B., Khatib T., Abed I.A.

“…Algorithms; Evolutionary algorithms; Mean square error; Optimization; Parameter estimation; Photovoltaic cells; Coefficient of determination; DEAM; Five parameters; Hybrid differential evolution; Improved differential evolutions; IV characteristics; Photovoltaic modules; Root mean square errors; Iterative methods; algorithm; artificial intelligence; electromagnetic method; error analysis; experimental study; photovoltaic system…”

Differential Evolution Based Special Protection and Control Scheme for Contingency Monitoring of Transmission Line Overloading by Othman, Mohammad Lutfi, Hadi, Mahmood Khalid, Abdul Wahab, Noor Izzri

“…In this paper, a Special Protection and Control Scheme (SPCS) based on Differential Evolution (DE) algorithm for optimal generation rescheduling to mitigate line overloads in contingency conditions has been presented. …”
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Urgency-aware scheduling algorithm for downlink cognitive long term evolution-advanced by Mohd Ramli, Huda Adibah, Mohd. Isa, Farah Nadia, Asnawi, Ani Liza, Jusoh, Ahmad Zamani, Azman, Amelia Wong

“…Simulation results demonstrate that the proposed algorithm can significantly optimize the number of cognitive LTE-Advanced users with satisfactory RT QoS whilst having acceptable QoS for the NRT packets…”
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Parameter extraction of solar photovoltaic modules using penalty-based differential evolution by Ishaque, K., Salam, Z., Mekhilef, Saad, Shamsudin, A.

“…The analyses carried out using synthetic current-voltage (I-V) data set showed that the proposed P-DE outperforms other Evolutionary Algorithm methods, namely the simulated annealing (SA), genetic algorithm (GA), and particle swarm optimization (PSO). …”
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Privacy optimization and intrusion detection in modbus/tcp network-based scada in water distribution systems by Franco, Daniel Jose Da Graca Peceguina

“…Such approach is highly dependable on the clustering algorithm parameterization, and is not capable to deal with the normal system’s specification changes. …”
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Task scheduling in cloud computing using hybrid genetic algorithm and bald eagle search (GA-BES) by Kamal Khairi Supaprhman

“…Hence, Bald Eagle Search (BES) can increase efficiency and performance because it provides an efficient scheduling mechanism. The natural evolution optimization algorithm which is genetic algorithm can be improve by combining the nature meta-heuristic algorithms which is bald eagle search to improve the makespan of genetic algorithm using cloudsim that need to be implement on the eclipse platform. …”
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Optimal solution to the fractional knapsack problem for LTE overload-state scheduling by Ferdosian, Nasim, Othman, Mohamed, Kweh, Yeah Lun, Mohd Ali, Borhanuddin

“…System performance has been evaluated in terms of loss, delay and throughput in comparison with reference algorithms.…”
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Reliability assessment of power system generation adequacy with wind power using population-based intelligent search methods by Kadhem, Athraa Ali

“…This study sought to examine the performance of three newly proposed techniques, for reliability assessment of the power systems, namely Disparity Evolution Genetic Algorithm (DEGA), Binary Particle Swarm Optimisation (BPSO), and Differential Evolution Optimization Algorithm (DEOA). …”
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Solving an application of university course timetabling problem by using genetic algorithm by Norhana, Shaibatul Khadri

“…Among the approaches, genetic algorithm (GA), constructed based on Darwin's theory of evolution, becomes the renowned approach to solve various types of timetabling problems. …”
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Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation by Ahmadipour, Masoud

“…Furthermore, to evaluate the efficiency of the proposed modified differential evolution for the training of ridgelet probabilistic neural network, four statistical search techniques, namely, particle swarm optimization, genetic algorithm, simulated angling, and classical differential evolution are used and their results are compared. …”
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