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Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation
Published 2022“…The HFPSO technique hybridizes the Firefly Optimization (FFO) algorithm and the Particle Swarm Optimization (PSO) method to improve the exploitation and exploration strategies and enhance the convergence rate. …”
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Optimization of mycelium growth using genetic algorithm for multi-objective functions
Published 2019“…Based on that result, it is concluded that multi-objective optimization problem can be solve using the applied method.…”
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Undergraduates Project Papers -
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Machining optimization using Firefly Algorithm / Farhan Md Jasni
Published 2020“…For this project, we will verify the Firefly Algorithm (FA) into finding total profit rate by optimizing the machining parameter of milling operation and compare the effectiveness of FA with other non¬conventional method. …”
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Development and applications of metaheuristic algorithms in engineering design and structural optimization / Ali Sadollah
Published 2013“…In addition, two novel optimization methods are developed and presented which are named the mine blast algorithm (MBA) and the water cycle algorithm (WCA). …”
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GENETIC ALGORITHM OPTIMIZED PACKET FILTERING
Published 2013“…n our research, we present a method to optimize packet filtering by genetic algorithm. …”
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Optimization of cnc turning parameters for minimizing temperature rise in aluminum using a genetic algorithm
Published 2024“…The genetic algorithm is used in this optimization because it is capable of searching for global optimal solutions since the configuration of the method can be very flexible, allowing it to be used for a variety of problems. …”
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Comparative analysis of line search methods in the Steepest Descent algorithm for unconstrained optimization problems / Ahmad Zikri Shukeri, Puteri Qurratu Ain Megat Sulzamzamendi...
Published 2024“…This study focuses on "Comparative Analysis of Line Search Methods in SD Algorithm for Unconstrained Optimization Problems". …”
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Optimization of milling parameters using ant colony optimization
Published 2008“…The simulation based on ACO algorithm are successful develop and the optimization of parameters values is to maximize the production rate is obtain from the simulation.…”
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Undergraduates Project Papers -
9
Optimization of turning parameters using genetic algorithm method
Published 2008“…This method was demonstrated for the optimization of machining parameters for turning operation using conventional lathe machines. …”
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Wavelet neural networks based solutions for elliptic partial differential equations with improved butterfly optimization algorithm training
Published 2020“…To evaluate the performance of the proposed IBOA training method, the obtained results are compared to the results of the momentum backpropagation (MBP), the particle swarm optimization (PSO) and the standard butterfly optimization algorithm (BOA) training methods. …”
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Optimising neural network training efficiency through spectral parameter-based multiple adaptive learning rates
Published 2024“…The process of training neural networks heavily involves solving optimization problems. Most optimization algorithms use a !…”
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Optimization of turning parameters using ant colony optimization
Published 2008“…Method used for this project is Ant Colony Optimization. …”
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Optimal power flow using hybrid firefly and particle swarm optimization algorithm
Published 2020“…The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. …”
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A Novel Polytope Algorithm based on Nelder-mead method for localization in wireless sensor network
Published 2024“…Methods: It is suggested that the objective function that will be optimized using NMM is the mean squared error of the range of all neighboring anchor nodes installed in the studied WSNs. …”
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Optimal power flow solutions for power system operations using moth-flame optimization algorithm
Published 2021“…The comparison proves that MFO offers a better result compared to the other selected methods. In IEEE 30-bus test system, MFO outperform the other optimization methods with 967.589961$/h compared to 971.411400 $/h, 983.738069$/h, 975.346233$/h, 985.198050$/h, 1035.537820$/h for Improved Grey Wolf Optimizer (IGWO), Grey Wolf Optimizer (GWO), Ant Loin Optimizer (ALO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA) respectively. …”
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16
Artificial Immune System Based Remainder Method for Multimodal Mathematical Function Optimization
Published 2011“…Artificial immune system (AIS) is one of the nature-inspired algorithm for solving optimization problems. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability compare to other meta-heuristic methods. …”
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Improvement and application of particle swarm optimization algorithm
Published 2025“…Finally, real-world tests are performed to verify the proposed Algorithm's efficacy compared to existing optimization methods. …”
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Hybrid Artificial Bees Colony Algorithms For Optimizing Carbon Nanotubes Characteristics
Published 2018“…Optimization is a crucial process to select the best parameters in single and multi-objective problems for manufacturing process.However,it is difficult to find an optimization algorithm that obtain the global optimum for every optimization problem.Artificial Bees Colony (ABC) is a well-known swarm intelligence algorithm in solving optimization problems.It has noticeably shown better performance compared to the state-of-art algorithms.This study proposes a novel hybrid ABC algorithm with β-Hill Climbing (βHC) technique (ABC-βHC) in order to enhance the exploitation and exploration process of the ABC in optimizing carbon nanotubes (CNTs) characteristics.CNTs are widely used in electronic and mechanical products due to its fascinating material with extraordinary mechanical,thermal,physical and electrical properties. …”
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Enhancing three variants of harmony search algorithm for continuous optimization problems
Published 2021“…Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. …”
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