Search Results - (( using solution learning algorithm ) OR ( simulation optimization based algorithm ))

Opposition-based learning simulated kalman filter for Numerical optimization problems by Mohd Falfazli, Mat Jusof

“…Simulated Kalman Filter (SKF) optimization algorithm is a population-based optimizer operated mainly based on Kalman filtering. …”
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A harmony search-based learning algorithm for epileptic seizure prediction by Kee, Huong Lai, Zainuddin, Zarita, Ong, Pauline

“…The proposed harmony search-based learning algorithm is used in the task of epileptic seizure prediction. …”
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A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution by Warid, Warid, Hizam, Hashim, Mariun, Norman, Abdul Wahab, Noor Izzri

“…An intelligence strategy called quasi-oppositional based learning is incorporated into the proposed algorithm to enhance its convergence property, exploration capability, and solution optimality. …”
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Enhancing simulated kalman filter algorithm using current optimum opposition-based learning by Kamil Zakwan, Mohd Azmi, Zuwairie, Ibrahim, Pebrianti, Dwi, Mohd Falfazli, Mat Jusof, Nor Hidayati, Abdul Aziz, Nor Azlina, Ab. Aziz

“…Simulated Kalman filter (SKF) is a new population-based optimization algorithm inspired by estimation capability of Kalman filter. …”
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On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing by Kamal Z., Zamli, Alsariera, Yazan A., Nasser, Abdullah B., Alsewari, Abdulrahman A.

“…Although useful, strategies based on the aforementioned optimization algorithms are not without limitation. …”
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Opposition- based simulated kalman filters and their application in system identification by Kamil Zakwan, Mohd Azmi

“…Among the various kinds of optimization algorithms, Simulated Kalman Filter (SKF) is a new population-based optimization algorithm inspired by the estimation capability of Kalman Filter. …”
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Antibody Remainder Method Based Artificial Immune System for Mathematical Function Optimization by Yap, David F. W., Koh, S. P., Tiong, S. K.

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Improved cuckoo search based neural network learning algorithms for data classification by Abdullah, Abdullah

“…On the other hand, LM algorithms which are derivative based algorithms still face a risk of getting stuck in local minima. …”
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BAT-BP: A new BAT based back-propagation algorithm for efficient data classification by Mohd. Nawi, Nazri, M. Z., Rehman, Hafifi, Nurfarian, Khan, Abdullah, Siming, Insaf Ali

“…The performance of the proposed Bat-BP algorithm is then compared with Artificial Bee Colony using BPNN (ABC-BP), Artificial Bee Colony using Levenberg-Marquardt (ABC-LM) and BPNN algorithm. …”
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Optimal placement and sizing of FACTS devices for optimal power flow using metaheuristic optimizers by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

“…OPF is one of the well-known problems in power system operations and with the inclusion of the FACTS devices allocation problems into OPF will make the solution more complex. Thus, seven metaheuristic algorithms: Barnacles Mating Optimizer (BMO), Marine Predators Algorithm (MPA), Moth–Flame Optimization (MFO), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching–Learning-Based Optimization (TLBO) and Heap-Based Optimizer (HBO) are used to solve two objective functions: power loss and cost minimizations. …”
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Optimizing dam and reservoir operations using machine learning by Mohammad Abdullah Adib Almubaidin

Unsupervised Deep Learning Algorithm to Solve Sub-Surface Dynamics for Petroleum Engineering Applications by Kumar, A., Ridha, S., Ilyas, S.U.

“…The solution provided by deep learning for a differential equation is in a closed analytical form which is differentiable and could be used in any subsequent computation. …”
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Hybrid Henry Gas-Harris Hawks comprehensive-opposition algorithm for task scheduling in cloud computing by Omran Alkaam, Nora, Md Sultan, Abu Bakar, Hussin, Masnida, Yatim Sharif, Khaironi

“…This method is based on two elements: comprehensive opposition-based learning (COBL) and Harris Hawks Optimization (HHO). …”
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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.

“…The proposed DNN algorithm is structured to incorporate initial/boundary conditions in cylindrical coordinates and approximate the solution without the aid of any simulated or training data. …”
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Simulated Kalman Filter with modified measurement, substitution mutation and hamming distance calculation for solving traveling salesman problem by Suhazri Amrin, Rahmad, Zuwairie, Ibrahim, Zulkifli, Md. Yusof

“…As a multi-agent algorithm, every agent in the population acts as a Kalman filter by using a standard Kalman filter framework, which includes a simulated measurement process and a best-so-far solution as a reference. …”
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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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WCBP: A new water cycle based back propagation algorithm for data classification by Mohd. Nawi, Nazri, Khan, Abdullah, Firdaus, Naim, M. Z., Rehman, Siming, Insaf Ali

“…Water Cycle algorithm is a modern nature inspired meta-heuristic algorithm to provide derivative-free solution to optimize complex problems. …”
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A fuzzy adaptive teaching learning-based optimization strategy for generating mixed strength t-way test suites by Din, Fakhrud

“…Owing to its proven performance in many other optimization problems, the adoption of the parameter-free Teaching Learning-based Optimization (TLBO) algorithm as a new t-way strategy is deemed useful. …”
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Distributed learning based energy-efficient operations in small cell networks by Mughees, Amna

“…Both user association and power allocation are solved in their respective action spaces. Simulation results demonstrate improved performance in power consumption, load, sum rate, utility, learning rate, convergence, and energy efficiency for small base stations (SBSs) and user equipment (UEs) compared to four benchmarked algorithms, including WMMSE, game theory, Q-learning, and DRL. …”
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A new machine learning-based hybrid intrusion detection system and intelligent routing algorithm for MPLS network by Mohammad Azmi Ridwan, Dr.

“…Machine Learning (ML) is seen as a promising application that offers autonomous learning and provides optimized solutions to complex problems. …”