Search Results - (( problem implementation search algorithm ) OR ( parameter optimization method algorithm ))
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Enhanced gravitational search algorithm for nano-process parameter optimization problem / Norlina Mohd Sabri
Published 2020“…Based on the capabilities of the metaheuristic algorithms, this research is proposing the enhanced Gravitational Search Algorithm (eGSA) to solve the nano-process parameter optimization problem. …”
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Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
Published 2023“…—Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. …”
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Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
Published 2023“…Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. …”
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Identification of continuous-time model of hammerstein system using modified multi-verse optimizer
Published 2021“…his thesis implements a novel nature-inspired metaheuristic optimization algorithm, namely the modified Multi-Verse Optimizer (mMVO) algorithm, to identify the continuous-time model of Hammerstein system. …”
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A modified crow search algorithm with niching technique for numerical optimization
Published 2019“…Despite its easy implementation, crow search algorithm has weakness to find global optima and suffers from slow convergence rate in multi-modal optimization problems. …”
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A modified crow search algorithm with niching technique for numerical optimization
Published 2019“…Despite its easy implementation, crow search algorithm has weakness to find global optima and suffers from slow convergence rate in multi-modal optimization problems. …”
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Data fitting by G1 rational cubic Be´zier curves using harmony search
Published 2015“…HS is a derivative-free real parameter optimization algorithm, and draws an inspiration from the musical improvisation process of searching for a perfect state of harmony. …”
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Machining optimization using Firefly Algorithm / Farhan Md Jasni
Published 2020“…Then, the result will be compared with Tabu Search (TS) Algorithm, Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Continuous Ant Colony Algorithm (CACO). …”
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Nomadic people optimizer (NPO) for large-scale optimization problems
Published 2019“…Researchers have in the past few decades resorted to several methods that are inspired from complex optimization problems. …”
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Application of a primal-dual interior point algorithm using exact second order information with a novel non-monotone line search method to generally constrained minimax optimizatio...
Published 2008“…This work presents the application of a primal-dual interior point method to minimax optimisation problems. The algorithm differs significantly from previous approaches as it involves a novel non-monotone line search procedure, which is based on the use of standard penalty methods as the merit function used for line search. …”
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Data Fitting by G1 Rational Cubic Bézier Curves Using Harmony Search
Published 2015“…HS is a derivative-free real parameter optimization algorithm, and draws an inspiration from the musical improvisation process of searching for a perfect state of harmony. …”
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A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition
Published 2024“…Finally, a modified local search method using Perturb and Observe with adaptive step size method (P&O-ASM) is proposed to refine the near-optimal duty cycle and track GMPP with negligible oscillations. …”
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Sequential and parallel multiple tabu search algorithm for multiobjective urban transit scheduling problems
Published 2018“…This thesis studies multiobjective UTSP consisting of frequency setting, timetabling, simultaneous bus and driver scheduling by applying Multiple Tabu Search (MTS) algorithm. Metaheuristic methods have been widely applied to solve UTSP which is a NP-hard problem. …”
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Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system
Published 2023“…This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). …”
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PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM
Published 2011“…This research focuses on the use of binaryencoded genetic algorithm (GA) to implement efficient search strategies for the optimal architecture and training parameters of a multilayer feed-forward ANN. …”
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Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system
Published 2023“…This study proposes an automated-tuned hyper�parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). …”
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Estimation of photovoltaic models using an enhanced Henry gas solubility optimization algorithm with first-order adaptive damping Berndt-Hall-Hall-Hausman method
Published 2024“…The existing literature extensively explores the utilization of powerful Metaheuristic Algorithms (MAs) to address the complex constrained optimization problem in PV systems and achieve optimal solutions. …”
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Artificial intelligence technique in solving nano-process parameter optimization problem / Norlina Mohd Sabri...[et al.]
Published 2017“…The techniques are Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). …”
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Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh
Published 2015“…Secondly, a new DE algorithm (ARDE-SPX) is introduced that automatically adapts a repository of DE strategies and parameters control schemes to avoid the problems of stagnation and make DE respond to a wide range of function characteristics at different stages of evolutionary search. …”
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