Search Results - (( simulation optimization method algorithm ) OR ( time reduction method algorithm ))*
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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Simulated Kalman Filter algorithms for solving optimization problems
Published 2019“…In this research, two novel estimation-based metaheuristic optimization algorithms, named as Simulated Kalman Filter (SKF), and single-solution Simulated Kalman Filter (ssSKF) algorithms are introduced for global optimization problems. …”
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Optimising cloud computing performance with an enhanced dynamic load balancing algorithm for superior task allocation
Published 2024“…Evaluations using CloudSim simulations demonstrate that the EDLB algorithm achieves substantial average improvements over benchmark algorithm and the-state-of-the-art algorithm, including a 59.46% reduction in total makespan, a 12.70% reduction in average makespan, a 22.46% reduction in execution time, and a 3.10% increase in resource utilisation. …”
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Mobility management schemes based on multiple criteria for optimization of seamless handover in long term evolution networks
Published 2014“…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. …”
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Omega grey wolf optimizer (ωGWO) for optimization of overcurrent relays coordination with distributed generation
Published 2019“…This thesis apply a meta-heuristic algorithm called Grey Wolf Optimizer (GWO) to minimize the overcurrent relays operating time while fulfilling the inequality constraints. …”
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Minimization of tool path length of drilling process using particle swarm optimization (PSO)
Published 2020“…In various publications and articles, scientists and researchers adapted several methods of artificial intelligence (AI) or hybrid optimization method for tool path artificial immune system (AIS), genetic algorithms (GA), Artificial Neural networks (ANN) Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) (Narooei and Ramli, 2014). …”
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Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman
Published 2025“…Compared to manual scheduling methods, the automated system reduced scheduling time and errors while achieving better space management efficiency. …”
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Metaheuristic algorithms for solving lot-sizing and scheduling problems in single and multi-plant environments / Maryam Mohammadi
Published 2015“…Metaheuristic approaches namely genetic algorithm, particle swarm optimization, artificial bee colony, simulated annealing, and imperialist competitive algorithm are adopted for the optimization procedures. …”
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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“…The Particle Swarm Optimization (PSO) search method is adopted to search the optimized scaling factor to obtain optimal error rate performance. …”
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Dual search maximum power point algorithm based on mathematical analysis under partially-shaded conditions
Published 2016“…In evaluation part, the DSMPP algorithm has been compared with two other methods.According to both simulation and experimental results, by implementing the DSMPP technique, the GMPP can be obtained faster. …”
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ETOU electricity tariff for manufacturing load shifting strategy using ACO algorithm
Published 2019“…Superior bio-inspired algorithm, Ant Colony Optimization (ACO) had been implemented to optimize the upright load profile of load shifting strategy in the Malaysia Enhance Time of Use (ETOU) tariff condition. …”
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Optimal Planning Of Distributed Generation Considering Time Varying Load
Published 2022“…The purpose of this thesis is to present an effective combination method based on Backward Forward Sweep Power Flow (BFSPF) and Mix Integer Optimization by Genetic Algorithm (MIOGA) to solve the network problem in the presence of DG with the purpose of developing power flow for radial distribution system by considering time-varying load, optimize the sitting and sizing of the DG in the radial distribution network for the loss minimization and voltage improvement and make comparison radial distribution network with single and multi-DG by considering time varying load condition. …”
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Undergraduates Project Papers -
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A new machine learning-based hybrid intrusion detection system and intelligent routing algorithm for MPLS network
Published 2023“…The dataset development for both algorithms is carried out via simulations in Graphical Network Simulator 3 (GNS3). …”
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Tool path generation of contour parallel based on ant colony optimisation
Published 2016“…Finally the simulation results show the reduction of 5% machining time compared to previous method.…”
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Improved genetic algorithm for direct current motor high speed controller implemented on field programmable gate array
Published 2019“…There are many researches have been done to optimize PI controller based evolutionary algorithm, such as Genetic Algorithm (GA). …”
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Multi-objective optimization for integrated task scheduling and load balancing in fog computing for IoT applications
Published 2025“…By using a task scheduling and offloading method, the FOCS algorithm arranges data according to size and sends it to the appropriate fog nodes. …”
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Energy-efficient data transmission with clustering and compressive sensing in wireless sensor networks / Mukil Alagirisamy
Published 2020“…The simulation results of clustering algorithms, INACS and Perceptron framework are validated using various data analysis methods. …”
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A basic study on hybrid systems for small race car to improve dynamic performance using lap time simulation
“…Therefore, the dynamic performance of the hybrid system was investigated by calculating the lap times on a given circuit using a quasi-steady-state method with low computational load and high prediction accuracy. …”
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